ADDIS ABABA UNIVERSITY

SCHOOL OF GRADUATE STUDIES

ASSESSING & QUANTIFING THE LEVEL OF TRAFFIC

CONGESTION AT MAJOR INTERSECTIONS IN ADDIS

ABABA
(A CASE FOR EAST-WEST CORRIDOR)

A Thesis submitted to

The Department of Civil Engineering

In partial fulfillment

Of the requirements for degree of

Masters of Science in Civil Engineering (Road & Transport Engineering)

By

Wondwossen Taddesse

Advisor

Girma Gebresenbet (Prof.)

September, 2011
 Assessing & Quantifying the Level of Traffic Congestion at Major Intersections in Addis Ababa

ADDIS ABABA UIVERSITY

SCHOOL OF GRADUATE STUDIES

MSc Thesis on

ASSESSIG & QUATIFIG THE LEVEL OF TRAFFIC

COGESTIO AT MAJOR ITERSECTIOS I ADDIS ABABA
(A CASE FOR EAST-WEST CORRIDOR)

By
Wondwossen Taddesse Gedamu

Addis Ababa Institute of Technology

Department of Civil Engineering

Approved by board of examiners:

Girma Gebresenbet (Prof) ………………………. ……………………

Advisor Signature Date

Hailu Shume ………………………. ……………………

External Examiner Signature Date

Bikila Tekilu (Dr.) ………………………. ……………………

Internal Examiner Signature Date

Fekadu Melese ………………………. ……………………

Chairman Signature Date

By Wondwossen Taddesse 1
 Assessing & Quantifying the Level of Traffic Congestion at Major Intersections in Addis Ababa

DECLARATION

I certify that this research work titled “Assessing and Quantifying the Level of Traffic Congestion at
major Intersection in Addis Ababa (a case for East- West Corridor)” is my own work. The work has
not been presented elsewhere for assessment and award of any degree or diploma. Where
material has been used from other sources it has been properly acknowledged/ referred.

Wondwossen Taddesse .. ..

Name Signature Date

By Wondwossen Taddesse 2
 Assessing & Quantifying the Level of Traffic Congestion at Major Intersections in Addis Ababa

ACKNOWLEDGEMENT

I would like to express my sincere gratitude for my advisor, Professor Girma Gebresenbet for
his kind encouragement, follow up, patience and excellent guidance.

I would like to thank my colleagues Zegey Kebede who gave me a valuable data and
documents, Chombe, Hiywot and all GMX1 groups who encouraged me to be engaged and
work on this research by letting me off duty.

Finally, I would like to thank my son Natan & my wife Rahel Alemu without their support, love
and patience this wouldn’t have come true.

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 Assessing & Quantifying the Level of Traffic Congestion at Major Intersections in Addis Ababa

Table of Contents
DECLARATION ............................................................................................................... 2
ACKNOWLEDGEMENT .................................................................................................. 3
LIST OF TABLES ............................................................................................................ 7
LIST OF FIGURES .......................................................................................................... 8
LIST OF ACRONYMS ................................................................................................... 10
ABSTRACT ................................................................................................................... 11
1. INTRODUCTION .................................................................................................... 12
1.1. Background of study .................................................................................................. 12
1.2. Problem statement ..................................................................................................... 13
1.3. Literature review ........................................................................................................ 14
1.3.1. Defining traffic congestion .............................................................................................. 15
1.3.2. Causes of traffic congestion ........................................................................................... 16
1.3.3. Quantification of congestion ........................................................................................... 17
1.3.4. Components of congestion ............................................................................................ 18
1.3.5. Congestion indicators...................................................................................................... 19
1.3.5.1. Level of service (LOS) as congestion indicator................................................... 19
1.3.6. Performance measures using travel time .................................................................... 21
1.3.7. Traffic congestion and accident ..................................................................................... 22
1.3.8. Cost of traffic congestion ................................................................................................ 22
2. OBJECTIVE OF STUDY ......................................................................................... 24
2.1 Research goal................................................................................................................. 24
2.2 Research specific objectives ........................................................................................... 24
2.3 Scope and limitation........................................................................................................ 25
3. METHODOLOGY .................................................................................................... 26
3.1. Research approach .................................................................................................... 26
3.2. Data collection techniques and equipments ............................................................... 28
3.2.1. Video with manual transcription .................................................................................... 28
3.2.2. Manual traffic volume and vehicle occupancy count .................................................. 29
3.2.3. Video capturing equipments and setup ........................................................................ 29
3.3. Description of study area............................................................................................ 30
3.3.1.1. Study corridor: East –West corridor ...................................................................... 31

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 Assessing & Quantifying the Level of Traffic Congestion at Major Intersections in Addis Ababa

3.3.1.2. Study junctions and road sections /midblock/ ..................................................... 32

3.3.1.3. Population dynamics ............................................................................................... 33
3.3.1.4. Economic activity ..................................................................................................... 34
3.3.1.5. Traffic and transport operations in Addis Ababa................................................. 35
3.4. Data collection ........................................................................................................... 37
3.4.1. Travel time data ............................................................................................................... 37
3.5.1.1 Sampling .......................................................................................................................... 38
3.5.1.2 Travel time data .............................................................................................................. 38
3.5.1.3 Data reduction and quality control ............................................................................... 40
3.5.2 Traffic volume and vehicle occupancy data....................................................................... 40
3.5.2.1 Traffic volume data......................................................................................................... 40
3.5.2.2 Vehicle occupancy data ................................................................................................ 41
3.5.3 Questionnaires response...................................................................................................... 42
3.5.4 Traffic accident data .............................................................................................................. 42
4. RESULT .................................................................................................................. 44
4.1 Traffic flow pattern and vehicle composition analysis at mid-block .................................. 44
4.1.1 Directional traffic volume ...................................................................................................... 44
4.1.1.1 Torhailoch –Lideta midblock directional traffic volume ............................................. 45
4.1.1.2 Lideta to Mexico midblock directional traffic flow ...................................................... 46
4.1.1.3 Mexico – Legehar midblock direction traffic volume ................................................. 47
4.1.1.4 Wuhalimat – Haihulet midblock directional traffic flow.............................................. 48
4.1.2 Total traffic volume ................................................................................................................ 48
4.2 Intersections level of service (LOS) analysis ................................................................... 49
4.3 Congestion analysis ........................................................................................................ 51
4.3.1 Travel time .............................................................................................................................. 52
4.3.2 Average speed and travel rate ............................................................................................ 53
4.3.3 Delay rate, delay ratio and delay per traveler ................................................................... 54
4.3.4 Total segment delay (vehicle-min and person-min) ......................................................... 56
4.4 Questioners respondents profile................................................................................. 61
4.5 Traffic congestion and traffic accident trend analyses ..................................................... 62
4.5.1 Traffic accident trend in Addis Ababa ................................................................................. 62
4.5.2 Traffic volume vs. traffic accident ........................................................................................ 63
4.5.3Travel time vs. traffic accident .............................................................................................. 63

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 Assessing & Quantifying the Level of Traffic Congestion at Major Intersections in Addis Ababa

4.5.4 Traffic accident correlation with traffic volume and travel time ....................................... 65
4.5.5 Accident spots and congestion spots ................................................................................. 66
5. DISCUSSION.......................................................................................................... 67
5.1 Traffic volume/flow trend at mid block and congestion .................................................... 67
5.2 Travel rate and travel delays ........................................................................................... 68
5.3 Traffic congestion effect on Accident .............................................................................. 68
6. CONCLUSION ........................................................................................................ 70
REFERENCES .............................................................................................................. 71
APPENDIXES................................................................................................................ 74
APPENDIX A: Travel Time, Traffic Volume and Vehicle Occupancy data ............................. 75
APPENDIX B: Level of Service analysis output using aaSIDRA software ............................. 81
APPENDIX C: Congestion analysis sheet ............................................................................. 94
APPENDIX D: Traffic accident data .................................................................................... 105

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 Assessing & Quantifying the Level of Traffic Congestion at Major Intersections in Addis Ababa

LIST OF TABLES
Table 1: Major causes of traffic congestion in Lagos Metropolitan ............................................ 17
Table 2: Typical Highway Level of Service (LOS) rating (Source: HCM 2000) ......................... 20
Table 3: Typical Intersection Level of Service (LOS) rating (source: HCM 2000) ...................... 20
Table 4: Summary of Congestion measures (Source: (Tim Lomax, Shawn Turner, and Gordon
Shunk, 1997) ............................................................................................................................ 21
Table 5: Video capturing schedule & locations ......................................................................... 30
Table 6: Major Intersections along East-West corridor ............................................................. 32
Table 7: Study Location and type of Analysis ........................................................................... 33
Table 8: Vehicle ownership per capita for some countries in the world ..................................... 35
Table 9: Travel demand for year 2004 and for projected year (2020) (Source: Urban Transport
Studies 2005) ........................................................................................................................... 36
Table 10: Summary of Trends in Addis Ababa ......................................................................... 37
Table 11Travel Time Data collection locations & segment length ............................................. 39
Table 12: Passenger Car Equivalent factors (source: HCM 2000) ............................................ 41
Table 13: Directional Hourly traffic volume for Mid-Blocks ........................................................ 44
Table 14: Input geometric and traffic demand data. .................................................................. 50
Table 15: summary of output for level of service analysis for intersections ............................... 51
Table 16: Buffer Index & Travel Time Index.............................................................................. 58
Table 17: Fuel & vehicle idle cost ............................................................................................. 60
Table 18: Questioner respondents’ profile ................................................................................ 61
Table 19: Travel Rate (min/km) and Traffic Accident Data........................................................ 64
Table 20: AVERAGE TRAVEL TIME AT CONGESTED SEGEMENTS (in sec) ....................... 76
Table 21: DIRECTIONAL TRAFFIC VOLUME AT MIDBLOCKS (in PCU) ................................ 77

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 Assessing & Quantifying the Level of Traffic Congestion at Major Intersections in Addis Ababa

LIST OF FIGURES
Figure 1: Conceptual frame work of Congestion Cause & Impact ............................................. 16
Figure 2: Components of Congestion (adapted from Jenks et.al 2008) .................................... 19
Figure 3: Framework for research approach ............................................................................. 27
Figure 4: Typical Arrangement of Video camera during recording @ Haile G/Silase building ... 29
Figure 5: Locations for video capturing ..................................................................................... 30
Figure 6: Study Area Location (Source: Google earth, Airodata International Survey et.al 2010;
Picture take on 23/08/11) ......................................................................................................... 31
Figure 7: East-West Corridor of Addis Ababa City (source: urban transport study for Addis
Ababa city final report, 2006) .................................................................................................... 32
Figure 8: Population of Addis Ababa in millions (Source: FDRE Census result 2007and Urban
Transport Studies 2005) ........................................................................................................... 34
Figure 9: Real GDP Growth of Ethiopia (source: Global Finance Magazine web site, accessed
on 28/8/2012) ........................................................................................................................... 34
Figure 10: Traffic flow in Passenger car unit – (source: Urban Transport study 2005) .............. 36
Figure 11: Mexico – Roundabout.............................................................................................. 39
Figure 12: Urael Intersection ................................................................................................... 39
Figure 13: Legehar Intersection ................................................................................................ 39
Figure 14: a screen copy of portion of raw traffic volume data .................................................. 40
Figure 15: screen copy of raw Vehicle occupancy data ............................................................ 42
Figure 16: Traffic Volume for Torhailoch-Lideta Mid-Block ....................................................... 45
Figure 17: Traffic Volume by vehicle type ................................................................................. 46
Figure 18: Traffic Volume for Lideta Mexico Mid-Block ............................................................. 46
Figure 19: Traffic Volume by Vehicle Type ............................................................................... 47
Figure 20: Traffic Volume for Mexico-Legehar Mid-Block ......................................................... 47
Figure 21: Traffic Volume for Wuhalimat-Haihulet .................................................................... 48
Figure 22: Total directional Vehicle volume for the day light 12-hour count .............................. 49
Figure 23: Total both direction traffic volume (veh) of mid-blocks ............................................. 49
Figure 24: Average Travel Time at Lideta to Mexico entry leg of Mexico Roundabout (350m
length) ...................................................................................................................................... 52
Figure 25: Average Travel Time (Sec) for Legs at Legehar Intersection (100 m length) ........... 52
Figure 26: Average Travel time (sec) for Atlas-hotel, Wuhalimat and Kasanchis legs at Urael
Intersection (250, 150 and 60m length respectively)................................................................. 53
Figure 27: Average Travel Speed (Km/Hr)................................................................................ 53
Figure 28: Average Travel Rate (Min/Km) ................................................................................ 54
Figure 29: Delay Rate for all intersection (min/Km)................................................................... 54
Figure 30: Delay Ratio for all intersection ................................................................................. 55
Figure 31: Delay Ratio for Legehar intersection ........................................................................ 55
Figure 32: Delay per Traveler (annual-hour) ............................................................................. 56
Figure 33: Total Segment Delay ............................................................................................... 56
Figure 34: Total Segment delay density (Veh-min)/meter ......................................................... 57
Figure 35: Total Segment delay (Person-Min) .......................................................................... 57
Figure 36: Total Segment Delay density (Person-Min)/meter) .................................................. 58
Figure 37: Buffer Index ............................................................................................................. 59

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 Assessing & Quantifying the Level of Traffic Congestion at Major Intersections in Addis Ababa

Figure 38: Planning Time Index ................................................................................................ 59

Figure 39: Yearly Fuel & Vehicle cost due to delay................................................................... 60
Figure 40: Cumulative Accident trend with the time of a day .................................................... 62
Figure 41: Traffic Accident by times for individual years ........................................................... 62
Figure 42: Average Accident & Traffic Volume Trend ............................................................... 63
Figure 43: Travel Rate Vs Traffic Accident trend in a day ......................................................... 64
Figure 44: Traffic volume/ flow Vs Traffic Accident ................................................................... 65
Figure 45: Travel Rate Vs Traffic Accident ............................................................................... 65
Figure 46: GIS plotting of traffic and conjunction spot............................................................... 66
Figure 47: The Bottleneck at Torhailoch roundabout ................................................................ 67
Figure 48: Haihulet Intersection Geometry using aaSIDRA ...................................................... 82
Figure 49: Total Directional Demand flow at Haihulet intersection ............................................ 82
Figure 50: Degree of Saturation at Haihulet Intersection .......................................................... 83
Figure 51: Leg & lane Level of Service of Haihulet Intersection ................................................ 83
Figure 52: Legehar intersection Geometry using aaSIDRA ...................................................... 86
Figure 53: Total Directional hourly Demand flow at Legehar intersection ................................. 86
Figure 54: Lane degree of saturation for Legehar intersection .................................................. 87
Figure 55: Leg and lane Level of Service for Legehar Intersection ........................................... 87
Figure 56: URAEL Intersection Geometry using aaSIDRA ....................................................... 90
Figure 57: Total hourly directional demand flow for URAEL...................................................... 90
Figure 58: Degree of saturation of URAEL intersection ............................................................ 91
Figure 59: Leg & lane Level of Service at URAEL intersection ................................................. 91

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 Assessing & Quantifying the Level of Traffic Congestion at Major Intersections in Addis Ababa

LIST OF ACRONYMS
aaSIDRA akcelik & associates traffic Signalized & un signalized Intersections Design and

Research Aid

CMS Congestion Management System

HCM 2000 Highway Capacity Manual 2000

LOS Level of Service

PCU Passenger Car Unit

RTA Ethiopian Road Transport Authority

TOC Traffic Operation Center

TTI Texas Transport Institute

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 Assessing & Quantifying the Level of Traffic Congestion at Major Intersections in Addis Ababa

ABSTRACT
Traffic Congestion is an ever growing chronic problem in the transportation system soon after
the invention and mass production of automobiles. All major cities both in developed and
developing countries are facing the problem due to increasing travel demand which follows
economic and population growth. Traffic congestion directly affects commuters with an
increased travel time, excessive delay in a queue, increased fuel cost, delay for important
appointment and job, loss in productive hours; and it indirectly affects the living standard and
the environment as well. Hence, traffic congestion cause upon road users and cities to incur a
significant amount of money for both economic and social costs. Quantifying the level of the
traffic congestion and understanding how much effect and cost are being incurred due to traffic
congestion; hence, will be important for making improvement decisions and evaluate
implemented mitigation measures.

Following the economic and population growth in Addis Ababa, traffic congestion problem has
emerged and the problem is even growing faster. In this study, the level of the traffic congestion
in Addis Ababa city was quantified using travel time approach. The city’s one of the most
congested East-West corridor was considered and travel time, traffic volume, and vehicle
occupancy data were collected at four midblock and four intersections. Accordingly, the travel
rate, the delay rate, total travel delay (Veh-Min and Per-min), buffer index and planning time
index were calculated. And also, the average hourly travel rate is correlated with the average
hourly traffic accident data and congestion spots and accident black spots were plotted on the
GIS map to see the relationship between the traffic accident and traffic congestion.

Accordingly, the result showed that on average about 18,000 Veh-min or 38 Veh-day and about
169,000 Per-min or 352-person-day are wasted at each major intersection entry and the city
incurs annually about 5-8 Million Birr per intersection only for vehicle and fuel cost. The result
also showed that the city’s traffic accident rate correlated with travel rate better than traffic
volume and the congestion spots identified from questionnaire data conside with the black spots
identified by the national road safety agency.

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 Assessing & Quantifying the Level of Traffic Congestion at Major Intersections in Addis Ababa

1. INTRODUCTION
1.1. Background of study
As history of many cities shows, socio-economic growth usually accompanied with an
increasing demand for mobility and transportation. For instance Eurostat (2002) showed that
passenger- Km travel demand increases as fast as the gross domestic product (GDP) for
European nations. In order to meet such travel demand, countries and cities obligated to spend
considerable portion of their GDP on transportation sector. According to the ECMP (2007)
European countries expend more than 7% of their GDP on transportation and out of which only
traffic congestion costs more than 1% of the GDP. In Ethiopia, different reports estimates the
transportation expenditure to be about 10% of the country’s GDP; however, the actual cost
incurred due to traffic congestion is not yet known.

Addis Ababa, which is the capital city of Ethiopia and the seat of many international
organizations with more than 100 embassies, has now become one of the fastest growing
relatively modern cities in the sub Saharan Africa. According to the 2007 census the population
of Addis Ababa was estimated to be 2.8 Million with an average growth rate of 2.1% (FDRE
Population Census Commision, 2008). Following the current economic development in the
country, Addis Ababa has become the economic hub of the nation due its geographical as well
as political significance. Accordingly, many financial and commercial institutions and about 85 %
of the manufacturing industries of the country are located inside and at the periphery of Addis
Ababa. Such rapid socio-economic development in the city creates a huge demand for
transportation and the passenger-Km travel is increasing. The Urban Transport study report of
Addis Ababa estimates that the travel demand of Addis Ababa will be doubled in 2020 and the
daily trip will become 7.7 Million trips per day from 3.6 Million in 2004 (CES in association with
SABA Engineering, 2005). Accordingly, evidences show that the associated transportation
problems in the city; namely, traffic congestion and traffic accident rate are becoming worse and
worse.

The problem of traffic congestion in Addis Ababa has emerged and intensified within a short
period of time despite efforts of the city administration in expanding the city’s road network.
Though, the vehicle ownership in Ethiopia is the lowest even compared with the sub Saharan
countries, it is assumed that about 80% of the vehicles in the country are found in Addis Ababa
and the vehicle number is growing at about 5% yearly (CES in association with SABA
Engineering, 2005). Being exacerbated by the above and many more road side factors, traffic
congestion and traffic accident are now becoming a chronic problem in the city’s transportation
system (Birhanu, 2000,).

Currently the Addis Ababa City Transport Authority has realized the problem of traffic
congestion and planned to launch an advanced traffic management system and is working on
the establishment of Traffic Operation Center (TOC). According to the unpublished draft project
profile prepared by the Ministry of Transport and Communication, the planned TOC will serve to
improve safety, improve mobility and relief congestion, and provide traveler information service
(Ministry of Transport and Communication, 2010).

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 Assessing & Quantifying the Level of Traffic Congestion at Major Intersections in Addis Ababa

However, despite the well known problem of traffic congestion and the city’s administration
effort to improve the problem, limited quantitative researches have been conducted on city’s
traffic congestion level. Therefore, proper quantification and measuring the extent or level of
congestion is an important step for understanding the performance of the existing road network
and for evaluation of proposed congestion mitigation measures. Hence, this thesis will focus on
this information gap and will asses and quantify the level of the traffic congestion on the
selected study corridor of Addis Ababa based on travel time delay approach; and it will assess
the effect of traffic congestion on the traffic accident situation of the city.

1.2. Problem statement

In Addis Ababa, despite the intensive road network expansion and the limited number of vehicle
ownership compared to the other sub Saharan countries, traffic congestion has now become the
major threat in the cities economic growth by restraining the commuters’ mobility especially at
peak hours. In addition to waiting time for the limited public transportation, both vehicle owners
and public transport users are forced to delay within the congested traffic lane. Hence, late
arrival to work places and appointments for social or business activities have become common.

Despite the problem being recognized by all road users and transport professionals, there is
only insignificant attempt for quantitative research done on the extent of the traffic congestion in
Addis Ababa. A single attempt was made by Haregewoin Y. (2010) to assess the amount of
travel time delay along Total-Ayer Tena road section. However, this study was based on a
limited travel time data and most of the analysis was based on a subjective questionnaires’
response than engineering parameters. Hence, questions on the major cause, the level and the
effect of the traffic congestion on the road user and on the economy are still not well
investigated and answered.

Therefore, quantitative researches based on the engineering parameters of traffic congestion

should be conducted to answer at least the following questions. These include:

1. What are the main causes and contributing factors for the traffic congestion in Addis
Ababa?

2. Which parts of the road network are more prone to traffic congestion and at which area
is the situation recurring?

3. What is the level of the traffic congestion quantitatively in terms of explanatory

parameters showing its intensity, extent, duration and reliability?

4. How is traffic congestion affecting the traffic accident in the city?

5. If a traffic management scheme is to be applied which sections of the road network or

intersection should be prioritized?

Accordingly, this thesis will try to answer some of the problems by assessing and quantifying the
traffic congestion along the highly congested East-West corridor of Addis Ababa.

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 Assessing & Quantifying the Level of Traffic Congestion at Major Intersections in Addis Ababa

1.3. Literature review

Many researchers and professionals in the field of transportation agree that road traffic
congestion is an ever growing problem and global phenomenon of major cities throughout the
world. Further to this Lomax (1997) showed that traffic congestion is expanding toward the
suburbs as commercial activities are being pulled out of the central business districts (Lomax,
Turner, and Shunk, 1997; Maitra, P.K.Sikdar, and S.L.Dhingra, 1999). In fact, it is almost certain
that traffic congestion will also get worse during at least the coming decades mainly due to the
increasing population number and the growing economy of nations. Traffic congestion is a
negative output of a transportation system which has many detrimental effects on the
performance of the road network, the traffic flow, the society, the national economy and the
environment. Maitra (1999) summarizes some of the negative effects of traffic congestion as;
considerable loss of travel time, higher fuel consumption, more vehicle emission and associated
environmental and health impact, increased accident risk, stress and frustration on commuters
and greater transportation cost.

Since it is a day to day occurrence to almost all road users, the concept of congestion as a
serious problem of traffic flow is well known to the public or road users. However, many
documents showed that there was no considerable effort to conceptually investigate congestion
before 1990’s (W.D.Cottrell, 2001; Lomax, Turner, and Shunk, 1997). According to Cottrell
(2001), the 1991 Intermodal Surface Transportation Efficiency Act and the subsequent
Transportation Equity Acts mark a significant start for researches and investigations on
congestion as part of Congestion Management System (CMS) in United States of America.
Since then different research efforts to develop methods and parameters for measuring traffic
congestion have been proposed by different researchers and manuals. One of such efforts was
the research project funded by the National Cooperative Highway Research Program (NCHRP)
titled “Quantifying Congestion”.

Further to the above; many more researches have been conducted by different researchers and
professionals to develop measuring parameters and models (Maitra, P.K.Sikdar, & S.L.Dhingra,
1999; Lomax, Turner, and Shunk, 1997; W.D.Cottrell, 2001). However, many scholars agree
that unlike the other traffic flow characteristics, still there is no consistent definition and a single
performance measure for traffic congestion (B.Medley and J.Demetsky, 2003). So far, different
congestion measures and models have been proposed and used to determine the extent,
severity and duration of congestion and also transport professional are still developing different
models for congestion prediction and simulation (Moran and Koutsopoulos, 2010).

Proper quantification and measuring the extent or level of congestion is an important step for
understanding the performance of the existing road network and for evaluation of proposed
congestion mitigation measures. NCHRP-398 states that congestion measures are needed to
analyzing and prioritizing system improvement options, to provide quantitative information for
policy makers and the public, to determine how much delay and queue size formed, which area
or region is more congested (Lomax, Turner, and Shunk, 1997).

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 Assessing & Quantifying the Level of Traffic Congestion at Major Intersections in Addis Ababa

1.3.1. Defining traffic congestion

As a general term, congestion is a phenomenon that occurs almost in all walks of life which
demand competition for certain service or supply. For instance, at banking desk, fuel stations,
theater gates, e.t.c. Similarly, the Hand Book of Transportation explain road traffic congestion
as a phenomenon resulted when vehicles compete or demand for the available road space and
the demand reaches or exceeds the capacity.

Many scholars agreed that, despite the fact that engineers and other transport professionals
had studied traffic congestion for long time, there is no still consensus even within academia on
the single and precise definition of traffic congestion (T.Thianniwet and S.Phosaard, 2009). This
is mainly due to the fact that traffic congestion is:

1. A physical phenomena relating to the manner how vehicles impede each other’s
progression as demand for limited road space approach to capacity (Cambridge
Systematics, 2005)

2. A relative phenomena to users expectation versus road system performance

(Lomax, Turner, & Shunk, 1997)

3. It can’t be fully described using one dimensional parameter (W.D.Cottrell, 2001)

Hence, there are many definitions given for traffic congestion based on different parameters. If
we summarize them they all lie in at least one of the following definition. These are:

• Traffic Congestion is travel time or delay in excess of that normally incurred under
light or free flow travel condition.

• Traffic Congestion is a situation where the traffic demand for the road space exceeds
the capacity.

• Traffic Congestion is an excess of vehicle on the portion of the road way at a

particular time resulting in slower speed from normal or free flow speed and mostly
characterized by stop or stop-go traffic.

As it can be seen from the above definitions and the diagram below, definitions of traffic
congestion generally fall in to two major categories. These are definitions which base on the
cause and which base on the impact of traffic congestion. However, in order to quantify or
measure traffic congestions definitions which are based on the impacts are more appropriate
due to the fact that the impact of traffic congestion can be felt by many road users and easy to
understand.

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 Assessing & Quantifying the Level of Traffic Congestion at Major Intersections in Addis Ababa

Cause Effect Impact

• Longer travel
• Demand
exceeds supply
TRAFFIC time
COGESTIO
• Bottleneck • Slower travel
speed
• incidents

Figure 1: Conceptual frame work of Congestion Cause & Impact

Lomax (1997) argued that traffic congestion is dependent on the perception of the road user’s
and gives two definitions for “Congestion” and “Unacceptable Congestion”. Accordingly;
“Congestion” was defined as a travel time or delay in excess of what normally incurred under
light or free flow travel condition and “unacceptable congestion” as travel time or delay in excess
of an agreed upon norm. However, the later definition involves a subjective aspect and difficult
to demark in between. Hence, many researches and reports use the first definition in quantifying
traffic congestion.

In traffic engineering, flow is an important parameter that shows the state of the traffic
movement. In terms of traffic flow, congestion is usually considered as the state where the
speed-flow graph is reverted or sloped positive. Hence, congestion can be defined as a state in
the traffic flow pattern which represents the condition at which demand exceeds capacity or the
speed is below acceptable value (Yu, Liu, Shi, and Song, 2010).

Depending on its occurrence congestion can be classified as recurring and non-recurring

congestion. Recurring congestion includes congestion due to bottlenecks, traffic signal, and
persistent higher demand etc and they are predictable. Whereas non-recurring congestion is
includes those congestion caused by mainly accidents and unprecedented events
(Skabardonis., P.Varaiya, and F.Petty, 2003).

1.3.2. Causes of traffic congestion

Different researches and reports identified many interrelated factors that cause traffic
congestion in developed and developing countries where the road network and road users
behavior are different (Cambridge Systematics, 2005; Aworemi et.al., 2009; Kwon, Mauch, &
Varaiya, 2006). For instance, Cambridge Systematics(2005) in its “Traffic Congestion and
reliablity” report showed the main causes of traffic congestion in United States of America and
the research by Aworemi, et.al;( 2009) identified the major traffic congestion causes in Lagos
Metropolitan. Accordingly, the results showed that in the United States of America the cause
and their percentage share are; bottleneck (40%), traffic incidents (25%), work zone (10%), bad
weather (15%), poor signal timing (5%) and special events contribute 5% of the traffic
congestion.

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 Assessing & Quantifying the Level of Traffic Congestion at Major Intersections in Addis Ababa

Adedimila (as quated by Aworemi, et.al: 2009) classifies the major causes of teaffic congestion
in lagos metropolitan in to five and the summary of his discussion is shown in the Table 1
below.

Table 1: Major causes of traffic congestion in Lagos Metropolitan

Item
Factors Causes described
No.

• Rising population number together with the rural-

urban migration

• Unplanned land use which result unidirectional traffic

1 Social & Economic factors
flow especially at pick hours

• Increased car ownership in line with the improved

living standard

• Smaller number of lane & Narrow road with

• Lack of side walk which result occupation of traffic

lanes by pedestrians
2 Road factors
• Distressed pavement which result in a reduced travel
speed

• Uncontrolled traffic Intersections

3 Vehicle factors • Size of vehicle

• Age of vehicles

4 Human factors • Perception of drivers

• Perception of pedestrians

5 Accident • The severity, number and location of accident

In his MSc thesis research Haregewoin (2010) identifies causes of traffic congestion in Addis
Ababa along Total-Ayer Tena road as; limited road capacity, road parking, un-integrated urban
planning, and lack of mass transit, accident, poor vehicle condition, and road side illegal trade.
Therefore, the common feature in the causes of traffic congestion in developing countries shows
that the root causes emanate from the lack of proper planning and improper use of limited road
network.

1.3.3. Quantification of congestion

According to Cottrell(2001) and other studies, during the early 1990’s the ever growing traffic
congestion became the concern of transport agencies of major metropolitans .Then different
legislations and acts were drawn in the United States of America which demands transport
agencies to establish Congestion Management Systems (CMSs). In response, some state

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agencies funded researches on measure, threshold, and method of assessing congestion.

Further studies and researches then conducted to develop parameters and indexes to quantify
traffic congestion. And also developing empirical models that help to predict recurrent and non-
recurrent traffic congestion become a concern (B.Medley and J.Demetsky, 2003;W.D.Cottrell,
2001; Skabardonis., P.Varaiya, and F.Petty, 2003; Moran C. A., 2008).

The survey made by Lomax (1997) in 1992 to investigate the existing practice of different
agencies for measuring traffic congestion showed that there were a range of empirical
measures being used by different agencies and out of these about 90% used the Level of
Service (LOS) as congestion measure as defined in Highway Capacity Manual (HCM).
However, the same research assesses the suggestion of agencies to on the most appropriate
measure for congestion. Accordingly, Travel delay and Travel time/ speed were frequently
proposed as the best congestion measure (Lomax, Turner, and Shunk, 1997).

The Highway Capacity Manual 2000 defines six states of traffic flow or operations with clear
boundaries of traffic flow parameters. The six states of traffic flow are named with the English
alphabet from A to F where A represents a free flow condition while F represents a blocked or a
stop and go traffic flow. However, the HCM do not specify a boundary as to which LOS is
considered as congested state. Hence, different agencies define their own boundary for
congestion and the survey result showed that LOS C, D, E and the worse were used by
agencies. Furthermore, as the HCM uses the volume to capacity ratio or saturation index (v/c)
as a base for LOS criteria, some agencies were using the v/c ration for measuring congestion
and the values ranging from 0.8-1.25 were used as a boundary for defining congested state
(Lomax, Turner, and Shunk, 1997;Schrank, Lomax, and Turner, 2010).

However, all the above congestion approaches both LOS and v/c ratio cannot be a
comprehensive measure for congestion due to the fact that congestion is a multidimensional
phenomenon. Meyer (1994) indicates that there is no consistent congestion measure used by
transport engineers and planners to monitor system congestion. Meyer also states “A good set
of congestion measures has the potential to improve not only the quality and consistency of
public transportation policy but also pubic understanding of the congestion phenomenon,
leading to political support for policy improvements and more rational behavior by individual
travelers”. Accordingly, most literatures agree that travel time approach for quantifying
congestion gives a better opportunity for public and policy makers to understand the level of
congestion.

1.3.4. Components of congestion

All researches done so far agreed that in order to fully express traffic congestion, it is necessary
to understand its four components or dimensions; namely, duration, extent, intensity and
reliability (Jenks et.al., 2008; W.D.Cottrell, 2001). Duration express the amount of time that the
congestion affects the transportation system or lasts with daily recurrences possible. Extent
concerns the number of persons or vehicles affected by travel delay. Intensity describes how
much the congestion is severe and affects the travel and Reliability/Variation describes the
changes in the above three other parameters and their predictability.

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According to Jenks et.al (2008), the four dimensions are actually are very important and can
help to define the magnitude of congestion. He explained the relationship of the four
components with a three dimensional box as shown in the Figure 2 below and the volume of the
box is related with the magnitude of congestion and the variation in the volume of the box with
time is an indication of reliability.
Duration

Extent

Intensity
Figure 2: Components of Congestion (adapted from Jenks et.al 2008)

1.3.5. Congestion indicators

As congestion is a relative measure unlike the other traffic flow parameters and it is defined on
the road user’s feedback on how the transports system is operation at a given period of time; it
is essential to define or have indicators of the presence of congestion in the system. According
to Cottrell (2001) many other researchers LOS is the best empirical indicator of congestion in
transport system. Moreover, according to Lomax (1997) the road user’s perception as a
measure for “acceptable” or “Unacceptable” congestion can be taken as an indicator or a
demarcation for classifying a road section or an intersection as Congested or not.

1.3.5.1. Level of service (LOS) as congestion indicator

The objective of High way Capacity Manual (HCM) is to provide a consistent system and
techniques for the evaluation of the quality of service on highways and street facilities. The HCM
does not set policies regarding a desirable or appropriate quality of service for various facilities,
systems, regions, or circumstances. Its objectives include providing a logical set of methods for
assessing transportation facilities, assuring that practitioners have access to the latest research
results, and presenting sample problems. HCM presents LOS as an easy-to-understand
methodology of analysis and performance measure for single homogenous road segments.
LOS is featured for describing conditions in road links and there is no direct methodology for
aggregation. LOS has been criticized by analysts and experts in the area, but it is still in use for
the easy-to-communicate properties.

As it is stated earlier, HCM doesn’t specify the boundary LOS for congestion state but clearly
states that the LOS F is defined as the worst state of flow and represents congested flow.
Though there are some reports using other level of service (D and E) as congested flow, LOS F
is generally accepted as a state of traffic flow and hence LOS is the most appropriate
congestion indicator. The LOS criteria on the HCM are given in the form of min speed, flow or

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density for road way sections and as a max delay in sec for signalized and un-signalized
intersection.

The LOS criteria of HCM are summarized in Tables 2 and 3 below

Table 2: Typical Highway Level of Service (LOS) rating (Source: HCM 2000)

LOS Description Speed Flow Density

(mile/hr) (Veh/hr/ln) (Veh/mile)
Traffic flows at or above posted speed limit.
A Motorists have complete mobility between Over 60 Under 700 Under 12
lanes.
Slightly congested, with some impingement of
B maneuverability. Two motorists might be forced 57-60 700-1100 12-20
to drive side by side, limiting lane changes
Ability to pass or change lanes is not assured.
Most experienced drivers are comfortable and
C posted speed maintained but roads are close 54-57 1100-1550 20-30
to capacity. This is the target LOS for most
urban highways
Speeds are somewhat reduced, motorists are
D hemmed in by other vehicles. Typical urban 46-54 1550-1850 30-42
peak-period highway conditions.
Flow becomes irregular, speed vary and rarely
E reach the posted limit. This is considered a 30-46 1850-2000 42-67
system failure.
Flow is forced; with frequent drops in speed to
F Under 30 Unstable 67- max
nearly zero mph. Travel time is unpredictable.

Table 3: Typical Intersection Level of Service (LOS) rating (source: HCM 2000)

Level of Service Delay at signalized intersection Delay at un signalized intersection

(LOS)

A ≤ 10 sec ≤ 10 sec

B 10-20 sec 10-15 sec

C 20-35 sec 15-25 sec

D 35-55 sec 25-35 sec

E 55-80 sec 35-50 sec

F ≥80 sec ≥50 sec

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1.3.6. Performance measures using travel time

Each of the dimensions of traffic congestion stated before can be measured with different
operational characteristics (speed, delay, travel time, density e.t.c) or volume characteristics
(operating traffic volume, volume to capacity ratio, traffic volume per lane, e.t.c). Many
literatures including the NCHRP report 398 “Quantifying Congestion” provide different measures
for congestions based on travel time approach. Most of the measures explain only one or two of
the dimension of congestion and hence it is necessary to use more than one congestion
measure to explain the level of congestion at a road section. Accordingly, there are quite a
number of congestion measures suggested in different literatures for each congestion
dimension. However, the following congestion measures are taken & summarized mainly from
NCHRP 398: Quantifying Congestion by Lomax (1997) and NCHRP 618 by Jenks et. al (2008) .

Further to the following listed congestion measures in Table 4, new parameters in the form of
indexes have been emerging (Anjaneyulu and B.N.Nagaraj, 2009;Maitra, P.K.Sikdar, and
S.L.Dhingra, 1999). These indexes give a better understanding of the severity of the congestion
in terms of its spread over time and space. Some of the indices indicated in many literatures
include; Severity Index, K-factor, Lane –mile duration Index, Road way congestion index,
freeway congestion index, travel time index, buffer time index.

Table 4: Summary of Congestion measures (Source: (Tim Lomax, Shawn Turner, and Gordon Shunk,
1997)

Travel
Rate

Delay
Rate

Delay
Ratio

Delay
Per
Traveler

Travel
Time

Travel
Time
Index

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Buffer
Index

Planning
Time
Index

Total
Delay

1.3.7. Traffic congestion and accident

There are only limited researches available on the relationship between traffic accident and
congestion as it relates to the performance of the transportation system .However, Cambridge
Systematic, Inc (2008) report and a study by the Victoria Transport Policy Institute showed
some evidences that traffic congestion is related with traffic accident.

The evidence is mixed on the degree to which congestion reduces the number of traffic accident
on a congested road segment. In some cases, traffic accident shows a reduction in less
congested road section. The study concludes that shifting vehicle travel from congested to less
congested condition tends to reduce traffic accident but increases the accident severity. Other
researches for instance HRD (2008) agreed that traffic congestion causes traffic accident and
hence the cost of congestion should include the cost of accident risks.

The traffic accident rate in Ethiopia is reported to be one of the highest accident rates in the
world. Though, the vehicle ownership in the country is the lowest among the sub-Saharan
countries, the traffic accident is found to be the highest. According to the Ethiopian Road
Transport Authority statistics about 1,800 people died, 7000 people injured and over 400 Million
Birr was lost only in the year 2003 (RTA web site acceced on 1/9/2003). Birhanu (2000) in his
PhD dissertation disclosed that out of the total traffic accident in Ethiopia, 21 % of the fatalities,
42% of injury accidents and 65% of the total accidents occurred in Addis Ababa. Moreover, he
related the traffic volume as a parameter in the traffic accident model and concluded that as the
travel volume increase the headway between vehicles decrease and minor nose-tail collision
rate increases. Even though there are many research have been conducted on the traffic
accident & safety issues in Ethiopia, there was no any research so far studied on the
relationship between the traffic accident and the traffic congestion in the context of Ethiopia or
Addis Ababa.

1.3.8. Cost of traffic congestion

Many transport engineers and economists have been interested in costing traffic congestion for
long period and different studies have been done to estimate the cost of traffic congestion. As

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all planning and congestion mitigation measures decisions require a quantified cost benefit
analysis, costing traffic congestion is a critical task in traffic congestion management process.
Traffic congestion costs nations for their transportation activities, negatively impact their national
economy, impair the quality of life by costing traveler’s time and money, degrading the
environment and causing accident (HDR, 2008). According to HDR (2008) report the principal
economic and social costs of traffic congestion are:

o The costs of reduced economic output and accompanying job losses

o The costs of travel delay or lost time
o Vehicle operating costs (fuel, ideal time)
o Environmental costs and higher frequency of accident risks

Estimating the social and environmental cost is much difficult and different from area to area;
but, some literatures try to estimate person hourly cost as a function of considering all trips to
work place.

However, the Urban Mobility Report 2010 of TTI, determined the cost of congestion in United
States of America as a function of delay time and wasted fuel cost of 2009. Accordingly, the
result shows that: (Schrank, Lomax, and Turner, 2010)

o The congestion cost for extra time and fuel for 439 urban areas were 24 Billion,
58 Billion and 115 Billion for the years 1982, 2000 and 2009 respectively.
o 3.9 Billion gallon of fuels wasted
o 4.8 Billion person-hours of extra time wasted

Similarly, the congestion cost estimated for Toronto and for major Australia’s cities estimated to
be 3.3 Billion and 9.39 Billion per year respectively (HDR. 2008). The above results show how
the traffic congestion costs individual travelers and a nation in general. However, to the
knowledge of the researcher of this thesis there is no single attempt so far in Ethiopia to
evaluate the cost of traffic congestion in major cities and hence the problem of traffic congestion
is felt but it is unknown.

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2. OBJECTIVE OF STUDY
2.1 Research goal
As it is stated above, the growing problem of traffic congestion in Addis Ababa has been
perceived by all the public, the policy makers and the operators in the city. In addition, the
increasing population number, growing national economy and the accompanying travel demand
growth are expected to aggravate the traffic congestion and worsen the problem. However, the
Addis Ababa city Administration had been implementing an extensive road expansion projects
for the last two decades and currently has planned to implement an advanced traffic
management system in order to solve the growing chronic traffic congestion problem in the city.

Such huge investments and developments decisions; however, should be backed up with
focused researches and research results. Furthermore, even though the problem of the traffic
congestion was perceived by all the public and the academicians, the problem was not yet
quantified and known. For instance, the travel time delay a traveler will spend at peak period,
the total person-hours or vehicle hours delayed and wasted, the cost of fuel wasted due to
congestion and the total cost of the congestion the city is incurring e.t.c are not yet known.
Therefore, the objective of this study was to try to answer the basic research problems raised
and pave a way in quantifying the traffic congestion of Addis Ababa by taking a portion of the
city. In doing so the researcher believes that indicative results and an in sighting outputs would
be generated which will help decision makers to make an informed decision and initiate further
researches.

Furthermore, all previous researches on traffic accident in Addis Ababa is usually tied only with
road and vehicle or traffic factors. However, researches in the literature review showed the
effect of traffic congestion on accident. Hence, the researcher believed that the relationship of
traffic congestion and accident in Addis Ababa should be assessed.

2.2 Research specific objectives

The specific objectives were to:

identify the peak hours and peak periods of traffic flow within the time of the day

measure the level of service (LOS) of the intersections along the study corridor using
standard procedure

measure the performance of the intersections during the time period of a day using
travel time approach

determine the level of congestion intensity, extent and reliability based on the
parameters identified in literature

estimate the economic cost of congestion at intersection by considering the vehicle and
fuel cost only

compare and prioritize road sections and intersections based on their traffic congestion
level to identify where the traffic congestion is worse.

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asses the relationship of traffic congestion and traffic accident in Addis Ababa

2.3 Scope and limitation

As the topic of congestion assessment touches lots of areas and wide, it is necessary to define
the scope of the study so that the untreated topics could be left for other researchers.
Accordingly, the scope of this study was limited to the east –west corridor or major road of Addis
Ababa and other road sections and intersections were not included in this study. Furthermore,
the analysis was segment study rather than area wide or regional study. Hence, it focused
mainly on the road segments at the entry of selected intersections and the relative effect of
consecutive intersection was not discussed. Since, the main objective of the study is
assessment and quantifying the congestion level, the congestion management procedures and
measures were not discussed as it is a wide and need its own investigation.

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3. METHODOLOGY
The methodology employed for a research work was the critical aspect for ensuring the proper
result which aligns with the objective or the research question rose. Hence, this part of the
thesis discusses the methodology followed and the reason for the selection of the methods in
order to address the research problem stated earlier in chapter 1.2.

3.1. Research approach

The research approach in this thesis involves both quantitative and qualitative approaches.
Quantitative data and analysis were used to determine the level of service of intersections and
to measure the congestion levels quantitatively. Observation, direct field measurements and
secondary data were the main sources of quantitative data. Furthermore, qualitative data from
questionnaire were also used to determine whether the congestion in Addis Ababa is
considerable or not and to assess other related parameters.

Observations, collecting relevant data and subsequent analysis of the data help to generate
inductive conclusions on the level of congestion at the observed or considered Intersections and
road sections. Though it is impossible to assess the traffic congestion at all intersections and
road sections in the city, representative samples could be taken at different location of the city to
derive a generalized conclusion. However, in this research the intersections and road sections
considered were only at the East-West corridor of the city; which is connecting the highly
populated residential ends and passes through the central business district of the city.

In this thesis the methods followed were designed in such a way that the key questions of the
research be answered properly. As it shown in Figure: 3 below, in order to assess whether the
intersections or the road sections are congested or not; a key question “Does traffic congestion
exists at this location?” was raised and answered first using congestion indicator parameters.
The congestion indicator parameters used in this research were Level of Service (LOS) and
road users’ perception. The LOS criterion was according to HCM-2000 and determined using
the widely used aaSIDRA software and the road users’ perception was collected using
questionnaire.

For the road intersections and road sections identified as “Congested” further analysis for the
level of the congestion was done using travel time approach. In doing so, the performance
measure parameters were used to measure the intensity, extent and duration of the congestion.
As travel time approach is easy to understand and interpret by every people and it is easy to
convert to other index parameters, the performance measurement parameters used in this
research were based on travel time approach.

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Defining Study Corridor

Sampling
Intersections &
road section

IS THE TRAFFIC CONGESTION AT

Indicators THIS LOCATION IS
• LOS CONSIDERABLE?
• Commuters’
perception

YES NO

WHAT IS THE LEVEL OF THE

CONGESTION? Performance
Measurement
Parameters

Intensity Duration Extent Reliability

. Total Travel . Congested Buffer Index
delay Hours
Relationships of
Trend & Relationship assessment

Traffic
Congestion &
Traffic Accident
Comparison of Intersections & Road sections

Segment 1 Segment 2 Segment 3 Estimating

Performance Performance Performance future Trends of
measure value measure value measure value Traffic
(P1) (P1) (P1) Congestion

Costing Traffic Congestion

Figure 3: Framework for research approach

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3.2. Data collection techniques and equipments

Different types of data were collected for the purpose of this research mainly through primary
sources and some data were acquired through secondary sources. For the primary data
collection internationally reputable and recommended techniques of traffic data collection were
used. The primary traffic flow and travel time data collection technique used were

1. Video recording with manual transcription

2. Manual traffic volume count

In addition to the above traffic flow and travel time data collection techniques other field
measurements were done to gather data on the geometrical features of intersection for capacity
analysis. These include, number of lanes, lane width, configurations of lanes, grade, width of
median, movement policy e.tc. These measures were done for the intersections whose level of
service is going to be determined.

The other kind of primary data collection technique used was questionnaire. A structured
questioner was developed to gather additional information on the perception of road users’
about the Addis Ababa city traffic congestion. The questionnaire also helped to identify
congested road sections and intersections in the city and the possible causes of traffic
congestion. The questioners were distributed randomly for road users (taxi drivers, private car
owners, public transport users) mainly along the east-west corridor.

3.2.1. Video with manual transcription

Video recording and manual transcription or tracing were used to collect travel time data. This
method of travel data collection relies on video cameras to collect or capture the traffic flow in
the field and human personnel to transcribe or trace vehicles into a database at the office after
the actual time of data collection. According to travel time collection handbook; though it is
costly, Video capturing techniques is preferred over the manual collection (pen and paper
method) because:

• it provides a permanent, easily-review record and show the traffic conditions at any
time;
• it permits the reading of required parameters in a controlled environment in which
plate characters can be closely examined;
• it provides additional information about traffic flow characteristics such as traffic
volume and vehicle headway; and
• It can provide a time stamp for accurate determination of arrival times.
• have better accuracy than manual methods; and
• Able to capture a larger sample of the total number of vehicles.

Therefore, in order to exploit the above advantage and due to its convenience video cameras
with tripod were arranged at convenient height where maximum possible view could be
captured and visibility was maximized. The locations for video capturing were the roof & floors

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of high-rising buildings alongside the study sections. Figure 4 below shows the camera setup at
one of the locations and Figure 5 shows the four locations of video capturing.

3.2.2. Manual traffic volume and vehicle occupancy count

Manual traffic counts were conducted at different locations (Road mid blocks and Intersections)
to determine the directional traffic volume and flow at every 15 min. furthermore, vehicle
occupancy study were conducted using manual count method at different road mid-blocks and
intersections. However, these counts were not directly done by the researcher. The traffic
counts were done by the Addis Ababa City Transport Authority and the raw data was availed to
the researcher. The data was manipulated and transformed to the required size for the
analysis.

Therefore, from this data collection the following quantitative data were generated. These
include,

1. Directional Traffic Volume/flow per 15 min of interval for four Road mid-blocks and six
intersections.
2. Vehicle composition
3. Vehicle occupancy

In addition to the primary data acquired in the above methods, some secondary data; mainly on
Traffic accident, vehicle population, population and economic growth parameters were taken
from other literatures and reports. The sources of these secondary data are properly
acknowledged at their respective locations.

3.2.3. Video capturing equipments and setup

Figure 4: Typical Arrangement of Video camera during recording @ Haile G/Silase building

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Figure 5: Locations for video capturing

Table 5: Video capturing schedule & locations

Date Time Target Section Station for Vedio Camera

8:30 AM - At the 13th floor (about 40m
Thursday August Morning 12:00 AM height) of a new building in front
4/2011 Mexico - Lideta Mid
of “Buna na’ Shai Building”
Block
12:00 AM - (Location 1 at the above areal
Afternoon 6:30 PM picture)
Friday August 8:30 AM -
5/2011 Morning 12:00 AM Mexico - Lideta Mid
>>
12:00 AM - Block
Afternoon 6:30 PM
Monday August At the 6th floor of a new building
Morning
8/2011 Legehar - Mexico mid besides Anti-corruption building
12:00 AM - block (Location 2 at the above aerial
Afternoon 6:30 PM photo)
Tuesday August 9:30 AM -
Hayahulet- Urael Mid
9/2011 Morning 12:00 AM At the 7th floor of Haile G/Silase
block/Leg of Urael
12:00 AM - Building (Location 4 at the above
Junction
Afternoon 6:00 PM aerial photo)
Wednesday August 9:00 AM -
10/2011 Morning 12:00 AM Urael – Atlas Midblock At 6th floor of a new building
12:00 AM - @ Urael Junction (Location 3 at the above aerial
Afternoon 5:00 PM photo)

3.3. Description of study area

The study area selected for this research is Addis Ababa city which is the capital city of
Ethiopia. Addis Ababa is not only the capital city of Ethiopia but it is also the seat of African
Union head quarter and more than 100 embassies. Due to the fact that Addis Ababa is the
political and economic center of the nation, it is the highly populated town in the country.

Most of the economic and social developments in the country manifested at this capital city and
hence all the benefits and aftermath of such economic and population growth affect Addis
Ababa. One of the undesirable effects of such growth in the city is traffic congestion. In order to
study traffic congestion in Ethiopia, there is no a best place like Addis Ababa due to many
factors. Hence, this research focuses on the Addis Ababa city and this section of the research
describes briefly the study area and the selected corridors. It also discusses descriptive
parameters and trends which affect the traffic congestion.

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As shown in Figure 6 below Addis Ababa is one of the metropolitans in Africa which is found at
the horn of the continent with geographical coordinates 9o1’48’’ North and 38o44’24’’ East and
an average elevation of 2355 above sea level. The city has a total area of about 530.14 Km2
and a population of 2,738, 248 according to 2007 censes. The city is divided in to 10
administrative sub-cities and 99 kebeles.

3.3.1.1. Study corridor: East –West corridor

The final report of urban transport study for Addis Ababa city defines four major corridors in the
road net work of the city. These are; the East-west Axis or corridor, the North-South Axis or
corridor, the ring road and the CBD orbit. As summarized in the table below each corridor has
its own characteristics. However, only the east west corridor will be considered in this research.
This is mainly due to the availability of data and the cost of collecting more data in other corners
of the city.

Figure 6: Study Area Location (Source: Google earth, Airodata International Survey et.al 2010; Picture
take on 23/08/11)

The east –west corridor of Addis Ababa as shown in Figure 7 below is defined to start its
eastern end from the intersection with the ring road at Megenagna and its west end at the
Torhailoch intersection with the ring road. This corridor stretches for 9km and encompasses the
Haile G/silase street- Jemo Keniyata street- Ras Mekonen Street- Chad-streets. This corridor
passes through the city’s core area of “Meskel Square” and the Mexico area which is a tangent

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and center for movement toward the Central Body District (Mercato & Piaza). In addition,
different trip attraction spots and governmental and nongovernmental institutions are found
along this route. This East-West corridor also links the two highly populated residential areas at
the west (Ayer Tena) and at the east (Ayat & CMC). Due to the above facts this route is found to
be the highly trafficked and congested route during peak hour.

Figure 7: East-West Corridor of Addis Ababa City (source: urban transport study for Addis Ababa city final
report, 2006)

3.3.1.2. Study junctions and road sections /midblock/

The East-West corridor of Addis Ababa which is the study corridor for this research as stated
above contains more than 14 junctions and mid blocks. Some of the main junctions along this
corridor are listed in Table 6 below.

Table 6: Major Intersections along East-West corridor

No. Junction Name Type of Junction Remark

1 “Torhailoch” Junction 3-leged Roundabout

2 CoCa-Cola Junction T-Junction
3 Federal Court Junction Four Leg Junction
4 Mexico Junction 6-Leg Roundabout
5 Commerce Junction T-Junction
6 “Legehar” Junction Four-Leg Junction
7 Stadium Junction T-Junction
8 “Meskel” Square Junction A six leg weaving Junction
9 “Bambis” Junction 4-Leg Junction
10 “Urael” Junction 4-Leg Junction
11 “Wuhalimat” Junction T-Junction

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12 “Hai-Hulet” Junction 4-Leg Junction

13 “Lem-Hotel” Junction 4-Leg Junction
14 “Megenagna” Junction 4 –leg Roundabout

Accordingly, there are about more than 13 mid-blocks can be considered between the
intersections listed above. However, as traffic flow data which is collected from the Addis Ababa
City Transport Authority is only for some of the mid blocks and Junctions, the selection of study
Junctions and mid-block somehow guided to some extent by the availability of traffic flow data
and Vehicle occupancy data.

Hence, out of the above junctions and Mid-blocks within the study corridor, the following were
selected and appropriate data collected for travel time and delay using video camera. The
summary of study locations and the type of analysis done is shown in Table7 below.

Table 7: Study Location and type of Analysis

No. Type of Analysis Road section studied Remark

Lideta- Mexico Midblock

Direction Traffic Flow analysis Mexico-Legehar Mid Block Midblock or
1
&flow pattern study Torhailoch – Lideta Mid Block Road segments
Whalimat – Hai Hulet Mid Block
“Legehar “Junction
Level of Service determination “Urael” Junction
2 Junction
using aaSIDRA program “Hai-Hulet” Junction

Mexico –Lideta approach leg of Mexico

Roundabout
Mexico –Legehar approach leg of
Legehar Junction
Mexico –Legehar exit leg of Legehar
Travel Time & Congestion Junction
3 Junction Legs
Analysis Wuhalimat – Urael entry leg of Urael
Junction
Kasanchis – Urael entry leg of Urael
Junction
Atlas Hotel – Urael entry leg of Urael
Junction

3.3.1.3. Population dynamics

According to the 2007 and previous census report the population of Addis Ababa is increasing
at an alarming rate. The annual growth rate for 2007 was 2.1% and according to estimates the
population number will be about 5 Million by 2020. Migration from rural area contributes more
than half the population growth rate. For instance, in the 1994 censes, out of the 3.8% annual

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population growth about 1.98% of the population growth was due to migration from rural areas
(CES in association with SABA Engineering, 2005).

Figure 8 below shows the population growth trend and population numbers during the last three
national censes periods.

Figure 8: Population of Addis Ababa in millions (Source: FDRE Census result 2007and Urban Transport
Studies 2005)

3.3.1.4. Economic activity

According to the International Monetary Fund (IMF)-World Economic outlook report October
2010 data, the real GDP growth of the country is summarized in Figure 9 below. The data
shows that the country recorded a double digit economic growth after 2003 and keeps the pace
despite the current global economic recession.

As most of the economic activities in the country centers the capital city Addis Ababa, such
economic growth of the country obviously reflected in the cities economic activities. Therefore,
we can conclude that the economic activity in Addis Ababa is increasing with equal or higher
rate than the national economic growth rate.

Figure 9: Real GDP Growth of Ethiopia (source: Global Finance Magazine web site, accessed on
28/8/2012)

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3.3.1.5. Traffic and transport operations in Addis Ababa

Understanding the characteristics of the traffic and transportation system in the city helps to
correlate and interpret the basic parameters and congestion exacerbating factors. Hence,
essential data and resources regarding the vehicle ownership, the growth in vehicles number
and the trend in traffic demand for Addis Ababa city are discussed below.

3.3.1.6 Vehicle ownership and growth trend

The vehicle ownership per capita of Ethiopia is the lowest in the world, even below from the
sub-Saharan countries only better than Afghanistan & Malawi. A vehicle ownership per capital
data of about 145 countries for the year 2010 is given on Wikipedia and the data is summarized
below to help comparison of the Ethiopia’s vehicle ownership with other developed and sub-
Saharan countries.

Table 8: Vehicle ownership per capita for some countries in the world

Vehicle per Vehicle per Vehicle per

Country 1000 Country 1000 Country 1000
peoples peoples peoples

Puerto Rico 858 Libya 234 Cameron 8

USA 779 Algeria 154 Sudan 3
Italy 571 South Africa 123 Somalia 3
Germany 558 Egypt 30 Uganda 2
UK 458 Djibouti 28 Ethiopia 1
Afghanistan &
S. Korea 338 Senegal 18 Malawi <1

However, despite the fact that the per capital vehicle ownership in the country is so small, data
and reports from Transport Authority showed that there were 105,850, 132,938 and 143,366
registered vehicles in 1998, 2002 and 2005 respectively. Out of the total vehicles about 44% are
private vehicles and the average vehicle number growth rate is above 5% (RTA website
accessed 1/9/2003; CES in association with SABA Engineering, 2005).

One of the interesting information stated on Urban Transport study final report is that about 80%
of the total vehicles in the country are believed to be in Addis Ababa only. According to the
estimate on the above document the projected vehicle number will be 231,556. Which means
about 90,000 vehicles will join the road net work from 2005-2020. However, the absence of
adequate public transport and the practices of vehicle assembling activity in the country
escalate the vehicle ownership; hence’ the estimate could be undermined and the value could
reach to the said figure within few years only.

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3.3.1.6 Travel demand trend and forecast

According to the result of Urban Transport Study (2005), the 2004 average daily person-trip in
Addis Ababa was about 3.63 Million trips per day and out of which 60.5 % is walking and 31.5%
of the trip was public transport leaving the private vehicle trip only 8%. However, the projection
for year 2020 showed that the travel demand will increase by more than 100% and estimated to
be 7.7 Million trips per day.

Table 9: Travel demand for year 2004 and for projected year (2020) (Source: Urban Transport Studies
2005)

Share of Person Trip per day

Mode Base Year 2004 Horizon Year 2020
Trip (Million) % Trip (Million) %

Walk 2.03 60.5 3.5 45.45

Public Transport 1.06 31.5 3.5 45.45
Private Vehicles 0.27 8.0 0.7 9.10

Furthermore, as shown in the following Figure 10 the travel demand along the East-West
corridor is significantly high showing the fact that this corridor links the two east and west end
populated residential areas with the trip attracting institution along the corridor.

EAST _ WEST
CORRIDOR

Figure 10: Traffic flow in Passenger car unit – (source: Urban Transport study 2005)

In summary, facts and data showed that the transport demand in Addis Ababa is by far higher
than the supply and hence, the number of vehicle joining the cities road will increase with
considerable rate. Furthermore, some of the parameters discussed above which are related to

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the traffic congestion shows an increasing trend. The parameters which affect the traffic
congestion level discussed above are summarized in the following Table 10.

Table 10: Summary of Trends in Addis Ababa

Parameter Trend

Increase by average annual

Population rate of 2.1%
GDP Average yearly GDP of 8.5%

Vehicle Number Increase 5% yearly

Travel Demand Will increase by 106% in 2020

3.4. Data collection

To attain the objectives of this research, different types of quantitative and Qualitative data
namely; traffic flow or volume data, vehicle occupancy data, travel time data and travel accident
data and road users’ congestion perception data and causes of traffic congestion were required.
Despite the challenges, an attempt was made to collect the data using the techniques stated in
the methodology and described below at each section. As there is no a trend in the country for
a permanent data acquisition and computerized system in any of the field operating system,
acquiring data is highly challenging and costly. Hence, it was difficult to gather primary data at
all stations or congestion spots in the city. Rather possible representative road sections and
Intersections as shown in Table 11 were considered along the study corridor.

This section of the study discusses how data was sampled, collected and extracted from the
data source and also presents the gathered primary and secondary data by systematically
organizing and summarizing using standard formats.

3.4.1. Travel time data

Travel time data was the most important data for the congestion analysis. In order to collect the
travel time data at the selected locations, the procedures described on travel time data
collection handbook (1998) were followed. Accordingly, video with manual transcription was
taken and data collected using this technique. This method was chosen because;

• The video data provides a permanent, easy -review record of traffic condition
• Helps to capture as much as data required or helps to capture large sample size data
• Different types of data other than travel time can be extracted if required
• Provide a better accuracy than manual count
• Requires lesser number of peoples but many expensive equipments

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Before the actual video capturing started training was given for data collectors and owners of
the buildings where we plan to set up the video were requested for permission. Then a trial run
was made to identify best locations and possible problems to be happened and get ready for
that. A video camera and two Photo-video cameras with adjustable tripod, a laptop and hard
disk, power cables and hand books were prepared for the purpose.

Once all preparations are completed capturing video was started at the location and heights as
described in the Table 5. Accordingly, a full day traffic flow video data was acquired for further
manual transcription at office or on a computer.

3.5.1.1 Sampling

Once the video recorded data was acquired, extracting travel time taken by an individual vehicle
to travel a specified length of a road section was determined by tracing every individual vehicle.
Since many vehicles negotiate the entry point at a time, vehicles were selected randomly but
statistically significant sample size was determined for each 15-30 min of count. The sample
size was determined according to the procedure and equation on the handbook.

According to travel time data collection Handbook the sample size for manually transcript travel
time data is given by the equation;

Sample size for travel time study (n) =

Where: t= t-statistics from student t-distribution

for specified confidence interval
C.V = coefficient of variance
e = relative error

However, the handbook using the above statistical equation provides a sample sizes for
different traffic conditions and level of confidence. Accordingly, for congested traffic condition at
90% confidence interval and + 10% error, the minimum sample size was calculated to be 18 for
15-30 min count. Therefore, for 15 min interval about 10 -15 vehicles travel time were recorded
in the case of this research.

3.5.1.2 Travel time data

Table 11 below shows the locations where travel time data were collected with the duration of
data recording. Though the travel time data collection handbook recommends a segment length
of 400m, it was impossible to clearly see and trace vehicles above the segment length used in
the table. Hence, the travel time data were collected for the road segment length shown in the
table.

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Table 11Travel Time Data collection locations & segment length

N Segment Time of data

Location
o. length (m) collection
350
1 Mexico –Lideta approach leg of Mexico Roundabout 2:30 AM – 5:00 PM
100
2 Mexico –Legehar approach leg of Legehar Junction 2:15 PM – 6:00 PM

3 Mexico –Legehar exit leg of Legehar Junction 100

2:15 PM – 6:00 PM
150
4 Wuhalimat – Urael entry leg of Urael Junction 3:15 AM – 6:00 PM
60
5 Kasanchis – Urael entry leg of Urael Junction 3:30 AM – 5: 00 PM
6 Atlas Hotel – Urael entry leg of Urael Junction 250 3:30 AM – 5: 00 PM

Figure 11: Mexico – Roundabout

Figure 12: Urael Intersection

Figure 13: Legehar Intersection

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3.5.1.3 Data reduction and quality control

According to the sample size determined above, travel times were determined for each segment
within 15 min interval and recorded in an Excel database. However, the data were bulk and it is
necessary to reduce and produce manageable travel time data. The travel time data collection
handbook recommends the reduction in two ways:

1. Reduce the number of data records by eliminating invalid data; or

2. Producing a summary data and statistics at different aggregation levels

Accordingly, the data were averaged or mean value was take for the 15 min interval data and
central tendency statistical tests were done using the standard deviation and coefficient of
variance. In reducing the data outlier values were eliminated. The raw data for the travel time of
each section is summarized in tabular form and attached in the Appendix A.

3.5.2 Traffic volume and vehicle occupancy data

3.5.2.1 Traffic volume data

Traffic volume and vehicle occupancy data are very important to determine and understand the
flow pattern in the facility, to determine the peak flow rates and peak periods, to assess the
relationship between traffic volume and congestion. Furthermore, it is extremely required to
analyze the level of service of a facility and quantify the congestion intensity. Hence, acquiring a
traffic volume data at selected road sections and intersections in the study corridor were
mandatory and luckily enough, the raw data was available at the Addis Ababa City Transport
Authority which was collected for their own purpose.

The traffic volume count was made for 12 solid hours starting the morning 7:00 AM to the
evening 7:00 PM at 15 minutes interval. The vehicles were counted in category as “Passengers
car” and “Goods vehicles”. The Passengers cars category includes vehicle types namely; Cars
and Taxi, 4WD, Minibus Taxi, Mid-Bus and standard Bus, where as the Goods Vehicle category
includes vehicle types namely; Pickups, Light, Medium and Heavy commercial vehicles.

The following Figure 14 shows the raw data format of traffic count.

Figure 14: a screen copy of portion of raw traffic volume data

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The traffic count was directional and hence directional traffic flow characteristics can be easily
summarized and studied. As travel time data was averaged for all vehicles type and a single
travel time was considered in the 15 min time interval as discussed before, it is also necessary
that the vehicle volume count should be converted to passenger’s equivalent unit to conduct
congestion analysis. Therefore, following the Passenger Equivalent factors were used to
convert the traffic volume count in to PCU. The traffic volume in PCU is summarized and
presented at appendix A.

Table 12: Passenger Car Equivalent factors (source: HCM 2000)

Passenger Vehicles Goods Vehicles

Vehicle Mini Mini / Small Medium Heavy
Type Cars and Std.
4-WD Bus Midi MAV > 3
Taxi Bus Pickup LCV 2 / 3 – Axle
Taxi Bus Axle
PCU
1 1 1.5 1.5 3 1 1.5 3 3
factors

The directional traffic volume for each intersection is shown in the appendix as an input data for
aaSIDRA analysis.

3.5.2.2 Vehicle occupancy data

Vehicle occupancy; which is the number of peoples per vehicles, is an extremely important
parameter in traffic engineering and transportation planning. Usually it is used to convert person
trip to vehicle trip in the four step travel demand forecasting process and to determine parking
space requirement for public facility and spaces. However, its use is becoming increasingly
important in the congestion management process to compute person-delay; person-mile e.t.c.
Hence, vehicle occupancy is very important parameter for calculating congestion intensity
parameters.

The raw vehicle occupancy data for this study was obtained from Addis Ababa City Transport
Authority and it was processed to be used in the congestion analysis. The raw data gave the
occupancy for each vehicle type over the period of the study. However, as a single average
value is needed for the analysis, the weighted average vehicle occupancy is calculated as per
the following equation

Weighted Average Vehicle Occupancy:

AVOw =

Where: AVOw = Weighted Average Vehicle occupancy

Vi,t = Traffic volume of ith vehicle category at time interval t

VOi,t = the Vehicle occupancy of the ith Vehicle category at time interval t

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 Assessing & Quantifying the Level of Traffic Congestion at Major Intersections in Addis Ababa

The screen picture below shows the portion of the raw data used and the calculated average
vehicle occupancy for each segment. The full data is attached in the appendix A.

Figure 15: screen copy of raw Vehicle occupancy data

3.5.3 Questionnaires response

A structured questioner was prepared in order to gather additional information for the
congestion analysis. As congestion is a function of people’s perception toward their time and
their trip purpose, it was necessary to gather information and data on how the road users in
Addis Ababa perceive the current traffic congestion and know how much delay is acceptable for
them.

According to the definition by Lomax (1997) congestion is a travel delay in excess of the
acceptable travel time. Hence, according to this definition the road user’s element should be
included to define the demarcation between congested and uncongested. Hence, the structured
questioner was distributed randomly for road users (Taxi drivers, passengers, Traffic polices,
company owners, lecturers and other peoples) mainly in the east-west corridor. Furthermore,
respondents also requested to list at least 5 congestion spots they know and to prioritize the
possible congestion causes identified from literature and asked if there was other possible
congestion cause in the city.

The questioners were distributed through e-mail, through interview-questioner (the data
collector interview the respondent while filling the questioner) and distributing for respondent.
Accordingly, about 70 questioners were distributed and 43 were returned and analyzed. The
researcher believes that statistically significant samples should be considered to draw
conclusion out of analysis made on such questioner data. However, due to the fact that most of
the basic analyses in this research are based on the quantitative data described before and the
data on the questioner are a supplement for the result, the respondent size would be sufficient
for the purpose of this study.

3.5.4 Traffic accident data

Traffic accident rate in Ethiopia is one of the highest in the world. Girma (2000) showed that out
of the total accident in the country about 62% of the total accident occurs in the capital city
Addis Ababa. Data and documents show that the traffic accident in the city is alarmingly
increasing and different researches were made in this regarded. Most of these researches
showed the relationship between traffic flow and traffic accident. However, none of them identify
the relationship between traffic accident and traffic congestion. Hence, in this study, in order to

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see the relationship between traffic accident and traffic congestion, different accident data were
collected from secondary data. The most important data collected from secondary sources are:

1. 10 year Accident data by time of a day

2. Accident data by Type of Accident
3. Addis Ababa City Accident Black spot map which was prepared by National Road
Safety Coordination Office

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4. RESULT
The analysis was made on the gathered quantitative and qualitative data to look in to the trend
of the traffic flow with in the day and identify the peak period and peak hour volumes. The level
of service for the identified intersections was analyzed using a program aaSIDRA and the
intersections were checked if they fall as congested or not congested based on HCM 2000
criteria. Congestion analysis also made on the sections where the travel time data was
collected and the results interpreted and discussed. In the congestion analysis, parameters for
quantifying congestion were calculated based on travel time approach for each section. Finally,
the relationship between traffic accident with Traffic volume and travel rate was seen and a
regression equation was generated.

4.1 Traffic flow pattern and vehicle composition analysis at mid-block

4.1.1 Directional traffic volume

A directional traffic volume analysis was conducted on a traffic volume data which is counted at
15 min interval and for 12 solid hours of a day starting from the early morning to the late
afternoon. The traffic volume analysis is done for both direction and for four mid blocks along
the east –west corridor. The road sections or the mid blocks considered are

1. Torhailoch – Lideta Midblock

2. Lideta – Mexico Midblock
3. Mexico- Legehar Midblock
4. Wuhalimat – Haihulet Midblock

The traffic volume data for the above four mid-blocks was summarized for all class of vehicles
and reported as hourly volume in the Table 13 below.

Table 13: Directional Hourly traffic volume for Mid-Blocks

MEXICO – LIDETA MEXICO – LEGEHAR TORHAILOCH – LIDETA WUHALIMAT-

MID BLOCK MID BLOCK MIDBLOCK HAIHULET MIDBLOCK
Mexico to Lideta to Mexico to Legehar Torhailoch Lideta to Wuhalimat Hiahulet to
Time Lideta Mexico Legehar to Mexico to Lideta Torhailoch to Haihulet Wuhalimat
7:00-8:00 AM 600 857 948 834 1571 778 848 1463
8:00-9:00 AM 700 945 1019 935 1831 783 1015 1715
9:00-10:00 AM 713 777 931 1233 1262 727 1059 1576
10:00-11:00 AM 822 658 964 1237 1073 859 956 1611
11:00-12:00 AM 830 605 759 1238 973 801 1188 1621
12:00 -1:00 PM 751 622 777 1140 925 876 1173 1399
1:00-2:00 PM 596 603 822 938 902 720 1052 1658
2:00-3:00 PM 793 683 849 1090 1053 755 1068 1541
3:00-4:00 PM 666 923 942 1154 1048 834 1172 1597
4:00 -5:00 PM 776 791 943 1023 1019 970 1155 1405
5:00-6:00 PM 750 692 954 1267 1090 1049 1240 1509
6:00 -7:00 PM 848 620 733 1143 822 1262 1151 1186

The traffic volume analysis of each midblock is discussed below;

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4.1.1.1 Torhailoch –Lideta midblock directional traffic volume

This section of the road starts at the Torhailoch roundabout and passes through with the busiest
business center Lideta area. This road section carries traffic from the residential areas of the
western section of city (Alemgena-Ayertena- Alembank-Betel e.t.c) to the commercial body
district of Mexico and Lideta. Figure 16 below shows that the traffic volume for the direction
Torhailoch to Lideta is peak during the morning and it decrease to the mid day and shows a
slight increase and become nearly steady from 2:00 PM to 5:00 PM. The traffic volume from
Lideta to Torhailoch during the morning time is by far less than the traffic volume for the
Torhailoch –Lideta direction and relatively stable until the evening peak period. During the
evening peak period the traffic volume increases; but, interestingly the traffic volume increase
during the evening peak period in this directions is by far less than the morning peak volume in
the Torhailoch-Lideta direction.

Figure 16: Traffic Volume for Torhailoch-Lideta Mid-Block

When we look in to the traffic volume by vehicle type, we can see that about 70% of the vehicles
moving in both directions are private cars, taxi, mini bus taxi and mid buses which indicate that
most of the trip purpose could be from home to work place and other activities. Furthermore, out
of 13,561 vehicles moving from Torhailoch to Lideta, only 10,409 vehicles (only 77%) returned
back from Lideta to Torhailoch direction. Which means about 23 % (3,125) vehicles didn’t return
back to Torhailoch direction and the vehicles could take other route.

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Figure 17: Traffic Volume by vehicle type

4.1.1.2 Lideta to Mexico midblock directional traffic flow

This road section is a link for the traffic from Torhailoch (residential areas) and the Mercato -
Abinet area with the Mexico business district. The Lideta - Mexico direction shows the morning
peak flow and an evening peak flow where the latter is less than the morning peak flow. The
morning peak flow of Lideta- Mexico direction follows the same trend as that of Torhailoch –
Ledeta direction in Figure 16 above which explains that the morning flow from Lideta to Mexico
is mainly from Torhailoch – Lideta midblock. However, if we see the value of traffic volume in
the morning peak the traffic volume in Lideta-Mexico is lower than that of Torhailoch –Lideta
traffic volume. This is mainly due to the fact that the traffic from Torhailoch some vehicles
diverted to Abinet –Piazza and Abinet –Bus station direction. For Mexico –Lideta direction
during the morning the volume is small but starting from early the morning the traffic volume
increases and reaches its peak flow at the midday. This flow is mainly the traffic flow toward the
Abinet-Mercato –Bus station business district. During the evening peak period both direction
shows a peak volume; however, the evening peak volume of Lideta –Torhailoch direction is less
than the morning peak volume.

Figure 18: Traffic Volume for Lideta Mexico Mid-Block

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Unlike the Torhailoch-Lideta midblock, the traffic volume analysis by volume for Lideta-Mexico
midblock shows that the total number of vehicles moving in both direction almost equal even
though the flow period is staggered. Furthermore, in this road section again most of the vehicle
share is occupied by the three vehicle classes, privet cars, mini bus taxi and mid buses.

Figure 19: Traffic Volume by Vehicle Type

4.1.1.3 Mexico – Legehar midblock direction traffic volume

In this road section the traffic flow shows a morning peak at 8:00 AM and shows a decreasing
trend till mid day and then a gently increase the evening peak period which spans for about two
hours and it drops. For the Legehar-Mexico direction, the traffic volume shows a unique trend
that the morning peak reached at about 9:00 AM by sharply increasing from its lowest point and
the peak volume last for long period until mid day sharp drop. After mid day the traffic volume
steadily increases to the evening peak which is the maximum flow.

Figure 20 shows that the traffic volume from Legehar direction dominates at most of the time
and the highest flow is from the Legehar-Mexico direction. In general, the trend of peak flow
happening at the morning and evening period and the one of the directions dominate either in
the morning or evening peak or vice versa.

Figure 20: Traffic Volume for Mexico-Legehar Mid-Block

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The traffic volume analysis by vehicle type for this road section shows that the volume for
Legehar to Mexico dominates and similar to the other road sections passenger cars and
minibus taxi takes the huge share in the traffic volume.

4.1.1.4 Wuhalimat – Haihulet midblock directional traffic flow

The traffic flow in this road section is totally unbalanced and the traffic volume in the Haihulet-
Wuhalimat direction is higher than that of Wuhalimat to Haihulet. The trend in the traffic volume
for Haihulet-Wuhalimat direction is that it has a peak flow starting from the early morning and
the flow almost steady or decreases vey gently to the evening. That is in this direction peak
flow is during morning period and the flow is almost steady. However, for the traffic volume in
the Wuhalimat –Haihulet direction the volume is less than that of the Haihulet –Wuhalimat
direction but the traffic volume in this direction is still quite higher volume compared with the
other read sections discussed before. The trend in the traffic volume in this section is from
steady to gently increasing toward the evening peak period.

The higher and steady flow in the Haihulet-Wuhalimat direction is also manifested in the
Legehar-Mexico traffic flow above in section 4.4.1.3. This is because most of the traffic coming
in the Legehar direction is from Megenagna-Haihulet-Wuhalimat and Bole side.

Figure 21: Traffic Volume for Wuhalimat-Haihulet

Like the other road sections, the analysis of traffic volume by vehicle type showed that the
dominant vehicle types are Privet cars, 4WD and Minibus Taxi.

4.1.2 Total traffic volume

The total traffic volume during the 12 hours day time count was summed and both directional
volume is shown in Figure 22 below. Accordingly, it shows the volume for Haihulet-Wuhalimat
direction is the highest and the second highest traffic volume is Torhailoch –Lideta direction.
This result indicates that these two roads carry the huge traffic loads from the two ends of
residential areas.

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Figure 22: Total directional Vehicle volume for the day light 12-hour count

When we look in to the total traffic volume of both directions for each midblock, the trend of the
traffic volume with the time of the day it shows a trend a morning and evening peak periods. The
total volume of Haihulet-Wuhalimat is higher than any of the other midblock throughout the day
time. The Mexico-Lideta midblock traffic volume shows the lowest value throughout the day
periods. However, the traffic volume for Torhailoch-Lideta midblock during the morning peak
period is nearly equal to that of Haihulet –Wuhalimat midblock but during the evening peak
period the volume is less than it. Traffic volume difference between these mid-blocks creates an
interesting question of how the travel rate in these sections behaves and is there a relation
between the volume and congestion.

Figure 23: Total both direction traffic volume (veh) of mid-blocks

4.2 Intersections level of service (LOS) analysis

According to the methodology described above, first, it is necessary to justify that the
intersections and the road sections to be analyzed are in congested state based on accepted
standards and norms. Accordingly, in order to check whether the intersections are congested or

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not, analysis was made using aaSIDRA program. In order to analyze the LOS using the
program, installation was made with the options right-hand driving rule and HCM 2000 metric
version which represent the driving rule of Ethiopia. The HCM 2000 metric version was chosen
because it is widely accepted Highway capacity manual throughout the world with only minor
modifications and calibration. As only the level of service (LOS) will be determined for an
indicative result leaving the other out puts of the program, calibration was not taken as an issue
for the purpose.

Due to the availability of traffic flow data the level of service (LOS) was made only on three
intersections specifically where travel time data was collected. However, for the Mexico
roundabout, where travel time measurement was made for congestion analysis is not
considered for the level of analysis due to the absence of directional traffic flow data. Even
though, level of service analysis was not conducted by the researcher of this study at Mexico
roundabout, a secondary data was consulted and the result was taken from Tewodros (2007)
where the level of service analysis was conducted as part of his study in 2007.

The three intersections along the study corridor for which the LOS analyzed were:

Intersection 1: URAEL INTERSECTION

Intersection 2: LEGEHAR INTERSECTION

Intersection 3: HAIHULET INTERSECTON

In order to conduct the analysis the geometric and directional hourly traffic volume data were
prepared as an input for the program as summarized below in the Table 14. However,
recommended and default values were take for other input data; for instance critical gap,
saturated flow.

Table 14: Input geometric and traffic demand data.

Width Total Traffic Volume

Number Number Lane
Int of (Veh/Hr)*
Intersection Approach Leg of Entry of Exit Width
. Media
Lanes Lanes (m)
No n (m) LT TH RT
Meskel Approach 3 3 3.2 4.5 40 734 105
Wuhalimat Approach 3 3 3.2 4.5 21 809 421
URAEL
1 INTERSECTION Atlas Hotel Approach 3 3 3.2 1.5 757 551 32
Kasanchis Approach 3 3 2.6 1.0 63 571 63
Mexico Approach 3 3 3.2 4.8 191 1107 207
LEGEHAR Meskel Square Approach 4 4 3.2 NA 215 1155 266
2 INTERSECTION Railway Station approach 3 3 2.6 1.5 160 148 214
Piazza Approach 3 3 2.6 1.2 176 115 176
Wuhalimat Approach 4 3 3.2 4.8 887 148
HAIHULET Megenagna Approach 4 3 3.2 4.8 63 1456 95
3 INTERSECTION Bole Brass Approach 2 2 3.2 NA 187 177 136
British Embassy Approach 2 2 2.6 NA 221 338

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N: B: * this volume is total of both light and heavy vehicles, but for the program the actual separate values
were used for light and heavy vehicles
* This flow is a peak period flow

Even though these intersections are constructed with traffic signal theoretically; practically they
are intentionally made non-operational by the city Road Authority due to the fact that the timing
or phase is not properly designed. Hence, during the analysis period all the intersections were
considered as un-signalized - Give-way intersection type. Accordingly the analysis run and the
results of the analysis are summarized below in Table 15 and the outputs of the analysis for
each intersection are attached in the Appendix B.

Table 15: summary of output for level of service analysis for intersections

Degree of
Int. Intersection Approach Leg Saturation LOS Remark
No (V/C)

Meskel Approach 2.308 F

URAEL Wuhalimat Approach 3.544 F
1
INTERSECTION Atlas Hotel Approach 6.300 F
Kasanchis Approach 1.969 F
Mexico Approach 1.792 F
LEGEHAR Meskel Square Approach 1.792 F
2
INTERSECTION Railway Station approach 1.33 F
Pizza Approach 1.239 F
Wuhalimat Approach 2.464 F
HAIHULET Megenagna Approach 3.706 F
3
INTERSECTION Bole Brass Approach 1.731 F
British Embassy Approach 2.331 F
Lideta Approach 1.468 F This result is taken
from Tewodros
Legehar Approach 1.468 F (2007) and hence,
MEXICO Wabe shebele Approach 0.910 D the result represents
4 only the 2007 LOS
ROUNDABOUT* Agazian Approach 1.116 F and obviously the
LOS of these legs by
Sarbet Approach 0.560 A
now most likely
De Affric Approach 0.552 A decreased.
N:B: * the LOS of this roundabout is not analyzed by the researcher due to lack of traffic flow data. Hence, the result
above is taken from a secondary source Tewodros (2007) which is the LOS of 2007.

4.3 Congestion analysis

The travel time, traffic volume and vehicle occupancy data were used to analyze the congestion
along the study corridor. The congestion analysis was based on the travel time approach and
hence the following congestion measures were analyzed. These are; Average travel speed,
travel rate, delay rate, delay ratio, total segment delay, buffer index and travel planning time
index. Accordingly, the analysis result of each parameter is shown in the subsequent sections.

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4.3.1 Travel time

Figure 24, through Figure 26 below shows the average travel time at 15-min interval for the
segments selected. According to the result, the morning and evening peak periods recored the
higher travel time and the lowest lowest travel time recorded during the mid day or lunch time.
However, the full day travel time recorded of Lideta-Mexico leg and the Urael Intersection legs
shows that the travel time during the morning peak period is higher than the evening peak
period travel time.

For the Lideta-Mexico entry leg segment of Mexico roundabout, the morning peak period travel
time is more than five times the lowest travel time and about two time the evening peak period
travel time. The travel time data for the Legehar intersection shows that the entry leg which is
Mexico-Legehar leg has the higher travel time than the adjucent exit leg of Legehar-Mexico
segment.

Figure 24: Average Travel Time at Lideta to Mexico entry leg of Mexico Roundabout (350m length)

Figure 25: Average Travel Time (Sec) for Legs at Legehar Intersection (100 m length)

The result of the travel time for the three entry leg segments of Urael intersection shows that all
the three segments follow the same trend and according to the Figure 26 mornig peak period

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travel time is about seven times the lowest travel time value at the lunch time and evening peak
period travel time is about five time the lowest travel time.

Figure 26: Average Travel time (sec) for Atlas-hotel, Wuhalimat and Kasanchis legs at Urael Intersection
(250, 150 and 60m length respectively)

4.3.2 Average speed and travel rate

The average speed calculation at the congested road sections considered in this study is shown
in Figure 27. The result shows that during the morning period travel speeds at the sections are
almost below 5 Km/hr up to the mid day. However, during mid day the travel speed increased to
the maximum value. During the mid day the Lideta –Mexico section shows the highest travel
speed and for Legehar –Mexico section until 5:00 PM the travel speed is higher. This is mainly
the later section is an exit lane for Legehar intersection. The Kasanchis –Urael leg shows the
least travel speed at all the time than the other legs even though it shows a peak at the mid day.

Figure 27: Average Travel Speed (Km/Hr)

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The travel rate which is the inverse of travel speed and the very important parameter in
congestion analysis is calculated and shown below in Figure 28. The figure shows that the
travel rate during the night peak period is higher than the morning peak period except for
Haihulet-Urael and Lideta Mexico leg. The travel rate for Kasanchis-Urael leg is the highest of
all the other legs throughout the day.

Figure 28: Average Travel Rate (Min/Km)

4.3.3 Delay rate, delay ratio and delay per traveler

The delay calculation was conducted with reference to the daily least travel rate or the travel
time recorded during the highest travel speed period which is taken as acceptable or free flow.
As both posted speed and free flow speed can’t be applicable at these legs the definition of
Lomax (1997) was used and the travel rate at the uncongested condition was taken as
acceptable travel rate. Accordingly, the results of delay rate, delay ratio and delay per traveler
are shown in the figures below from Figure 29 through Figure 32.

Figure 29: Delay Rate for all intersection (min/Km)

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Similar trend is seen in delay rate between travel rate and delay rate for the intersections and
hence a much delay is happened during the travel compared with the morning. Interestingly, for
Atlas-Urael leg the higher delay rate was recorded from 2:30 PM – 4:00 PM and it decreases
however, for Mexico –Legehar leg the highest delay rate was recorded after 5:00 PM. For the
morning peak period the highest delay rate was about 25min/Km where as the highest delay
rate which is at the evening peak period amounts about 40 min/km.

The figure below Figure 30 shows that the delay ratio which is the ratio of delay rate to actual
travel rate for all the legs studied. Accordingly, though the delay rate amount is different for the
morning and the evening peaks, the delay ratio are almost the same. The delay ratio for most of
the sections for longer period is about 0.8. However, Mexico-Legehar and Legehar-Mexico legs
shows the least delay ratio until the evening peak time 5:00 PM and then it becomes almost 0.9

Figure 30: Delay Ratio for all intersection

Figure 31: Delay Ratio for Legehar intersection

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Delay per traveler or annual-hour delayed per traveler is shown in Figure 32 for the six legs
considered and the delay hour calculated for the road section considered and hence it is not
possible to compare the different legs as their length were different. However, the result shows
that a person traveling the 250m long Atlas –Urael leg of Urael junction at 3:00 PM only once
per day will lose about 40 hours of his life in the congestion.

Figure 32: Delay per Traveler (annual-hour)

4.3.4 Total segment delay (vehicle-min and person-min)

Total segment delay measured in Vehicle-min and or Person-hour is the measure of congestion
intensity. It shows how the congestion is serious and indicates the extent of the congestion that
how much peoples being affected with the congestion. Figure 33 shows the total segment delay
in Vehicle-Min for the leg length considered. The total segment delay shown in Figure 33 is
calculated for the legs based on their legs which are not equal. Hence, the result should not be
compared instead it should be read for a single leg only at once.

Figure 33: Total Segment Delay

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However, in order to compare the six legs the total delay was divided by the length of the
segment and the delay was converted to a unit length delay. Accordingly, Figure 34 shows that
during the morning peak period the congestion severity at Haihulet-Urael leg is the highest and
it is nearly five time that of Mexico-Lideta leg. The highest congestion severity at Kasanchis –
Urael leg starts lately at 10:30 AM and goes until the mid day however, immediately after the
lunch time the congestion starts and become peak in the evening peak hour of 4:00 PM. In
general the comparison shows that the three legs considered at Urael Junction shows the most
sever congestion the Mexico & Legehar Intersection.

Figure 34: Total Segment delay density (Veh-min)/meter

Figure 35: Total Segment delay (Person-Min)

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Figure 36: Total Segment Delay density (Person-Min)/meter)

Buffer index (BI) and Planning Time Index are measures of trip reliability and measure the
reliability of congestion with in a section or a corridor. Buffer index expresses the amount of
extra buffer time needed to be on time for 95% of the trips. Whereas planning time index
expresses the total travel time that should be planned when an adequate buffer time is included.

Table 16 and Figures 37 and 38 show the calculated buffer index and planning time index for
the five segments analyzed. Accordingly, the Atlas –Urael and Mexico-Lideta mid blocks are
less reliable than the other legs. The buffer index for Mexico-Lideta and Atlas-Urael legs are two
folds of the other legs.

Table 16: Buffer Index & Travel Time Index

Mexico – Haihulet- Atlas –

Lideta – Legehar Urael Urael Kasanchis –
Mexico Leg Leg Leg Leg Urael Leg
th
95 Percentile
482.26 210.16 178.53 550.53 150.49
Travel Time (min)
Average Travel
213.64 120.47 109.03 241.23 95.74
Time (Min)
Buffer Index (%) 126% 74% 64% 128% 57%
Planning Time
1.64 2.28 1.57
Index 2.26 1.74

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Figure 37: Buffer Index

Figure 38: Planning Time Index

Table 17 and Figure 39 below show the economic cost calculation of the congestion for the six
road sections considered in this study for only the considered road length. The cost calculation
is only based on the vehicle idle time which is converted in to cost using the average rental cost
and the fuel cost. In order to calculate the fuel cost a hourly fuel consumption for heavy,
medium and small vehicles was taken from Akcelik (2003) and the values were averaged based
on the vehicle composition of each. furthermore, for the vehicle idle time cost estimation, the
current rental cost of different vehicles were collected from car rental offices and the weighted
average rental cost was determined for each segment depending on the vehicle composition
and proportion. Accordingly, the result shows the only at Haihulet-Urael leg & Atlas Urael legs
congestion costs about 12 million each per year and the congestion at only one leg of 350m
long Mexico- Lideta approach costs about 7million per year for only idle vehicle and fuel cost.

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Table 17: Fuel & vehicle idle cost

Lideta - Mexico - Kasanchi

Mexico Legehar Legehar - Haihulet- Atlas - s Urael
Leg Leg Mexico Leg Urael Leg Urael Leg Leg

Total Segment Delay

per day (Vehicle-min) 15,745.79 7,082.91 2,409.95 27,326.79 29,187.41 11,200.67
Total Segment Delay
per day (Vehicle-Hr) 262.43 118.05 40.17 455.45 486.46 186.68
Weighted Average
Fuel Consumption
(Lit/hr) 1.30 1.29 1.26 1.23 1.25 1.25
Total Fuel Consumed
(lit) 341.39 152.36 50.81 559.93 605.95 233.35
Unit Cost of fuel (birr) 21 21 21 21 21 21
Total Fuel Cost per
day 7,169.21 3,199.61 1,066.96 11,758.55 12,724.92 4,900.29
Average daily Rental
cost of Vehicles
(birr/day) 636.66 618.44 587.29 559.87 574.90 574.00
Total Daily Vehicle
cost (birr) 20,884.92 9,125.78 2,948.63 31,874.06 34,958.22 13,394.13
Total Daily Vehicle &
Fuel cost cost
(birr/day) 28,054.13 12,325.39 4,015.59 43,632.61 47,683.14 18,294.42
Total yearly Vehicle &
Fuel cost cost ('000
birr/year) 7,209.91 3,167.62 1,032.01 11,213.58 12,254.57 4,701.67

Figure 39: Yearly Fuel & Vehicle cost due to delay

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4.4 Questioners respondents profile

The questioner respondents profile is summarized in the Table 18 below and the each
questioner data was discussed and presented at appropriate section in the analysis and
result part of this thesis. Table 18 shows that about 61% of the distributed questioners were
returned and the profile of the respondents showed that most of them were aged between
25-35 and the average distance of home to work place was from 3-10 Km.

Table 18: Questionnaire respondents’ profile

Frequency Percent
Questioner

Distributed 70

Returned 43 61.0%

Total
Under 25 7 16%
Age Group

25- 35 23 53%
36-45 9 21%
above 46 4 9%
Total 43 100%
Male 34 79%
Sex

Female 9 21%
Total 43 100%
Mode of Movement

Personal drive 17 40%

Public Transport 23 53%

Using driver but personal
vehicle 3 7%

Others 0 0%
Total 43 100%
Average distance from

1km- 3km 4 9%
home to work place

3km-7km 15 35%
7km-10 km 16 37%
10km – 14 km 4 9%
Above 14 km 4 9%
Total 43 100%

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4.5 Traffic congestion and traffic accident trend analyses

4.5.1 Traffic accident trend in Addis Ababa

Traffic accident is known to be one of the major transportation problems in Addis Ababa and the
subsequent loss of life; injury and property damage are significantly high. Figure 40 and 41
show the trend of the traffic accident within hours of a day. Accordingly, traffic accident is the
highest during the morning and evening peak periods. In addition, Figure 41 shows the number
of the traffic accident was increasing during the years from 1996-2005. As the data is a sum of
all accidents in the city, the severity of the accidents is hidden. However; as congestion is
usually involves a lower speed, the possible accident severity during traffic congestion would be
a light collisions.

Figure 40: Cumulative Accident trend with the time of a day

Figure 41: Traffic Accident by times for individual years

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4.5.2 Traffic volume vs. traffic accident

As Figure 23 shows the trend flowed by the total (bi directional) traffic volume or flow was
almost similar for the three midblock; namely, Mexico-Legehar, Torhailoch-Lideta and Lideta
Mexico. However, the trend for Wuhalimat –Haihulet midblock is somehow different from the
other three mid-blocks and it keeps almost constant but continuously falling and rising traffic
volume trends are shown.

To make the comparison between the traffic trends between the traffic accidents and traffic
volume it is logical to take the average traffic volumes of the midblock as most of the midblock
show the same trend in traffic volume variation. Hence, the average hourly traffic volume/flow is
plotted against with the average hourly traffic accident data in Addis Ababa city as shown below
in Figure 42. As the average hourly traffic accident value is by far less than the average traffic
volume the physical gap between the plots of the two curves were wide and hence to make the
diagram compressive, all traffic accident data were multiplied with a factor of 2.5 and the value
was plotted as shown in Figure 42.

Figure 42: Average Accident & Traffic Volume Trend

The result of Figure 42 shows a surprising trend between the traffic volume and traffic accident
that during the morning and evening peak periods both traffic flow and accident increases where
as during the mid day time both traffic flow and accident decreases.

4.5.3Travel time vs. traffic accident

Table 19 shows the average travel rate (min/km) for all the six legs assessed and the average
of the six travel rate together with the average hourly traffic accident rate (divided by 200)
historical data average for ten years. Figure 43 shows the corresponding diagram of travel rate
and accident rate along the time of a day. The result shows that both parameters travel rate and
traffic accident also follow the same trend as traffic accident and traffic volume.

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Table 19: Travel Rate (min/km) and Traffic Accident Data

Lideta Mexico Average Average

- - Legehar Haihulet Atlas - Kasanchi Travel Traffic
Mexic Legeha - Mexico - Urael Urael s -Urael Time Accident
o Leg r Leg Leg Leg Leg Leg (min/km)

08:00-09:00 AM 17.89 17.89 28.97

09:00-10:00 AM 18.04 16.37 13.80 21.14 17.34 28.66

10:00-11:00 AM 13.05 19.52 18.78 23.53 18.72 28.39

11:00-12:00 AM 16.76 10.38 13.18 28.35 17.17 28.98

12:00-01:00 PM 5.24 11.19 15.95 20.00 13.09 25.01

01:00-02:00 PM 3.32 7.49 4.95 11.17 6.73 23.46

02:00-03:00 PM 4.72 11.73 5.09 11.13 17.23 29.33 13.20 25.29

03:00-04:00 PM 8.10 12.61 4.41 15.53 33.75 36.19 18.43 27.58

04:00-05:00 PM 8.30 19.83 4.20 8.84 9.86 40.33 15.23 27.43

05:00-06:00 PM 34.06 21.18 9.64 21.63 25.95

Figure 43: Travel Rate Vs Traffic Accident trend in a day

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4.5.4 Traffic accident correlation with traffic volume and travel time
In order to see the correlation between traffic volume and travel time with traffic accident, linear
equations and regression coefficients were determined as shown in Figure 44 and Figure 45
respectively. Accordingly, the regression coefficient of travel time with traffic accident is greater
than the regression coefficient of traffic volume. Hence, the result indicates that travel time has
a relationship with traffic accident.

Though the regression coefficient of traffic volume is not significant due to the outlier values,
values above the regression line are so close and follow a trend. Therefore, the result cannot be
undermined.

Figure 44: Traffic volume/ flow Vs Traffic Accident

Figure 45: Travel Rate Vs Traffic Accident

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 Assessing & Quantifying the Level of Traffic Congestion at Major Intersections in Addis Ababa

4.5.5 Accident spots and congestion spots

The traffic accident spots as identified by the Road Safety Agency are collected and plotted on a
GIS map of Addis Ababa (the black dots) these accident spots are only those spots with traffic
accident rate of greater than 50 accidents per year. The congestion spots were collected using
questioner and the result was plotted on the GIS map as shown in the Figure 46 below. As
questioners were given randomly, the congestion spots identified could not be the entire
congestion spots in Addis Ababa city. However, the result shows that all the identified
congestion spots are also identified as black spots by the road safety agency. Furthermore, the
traffic and accident spots follow a trend that the east-west and north-south axis and
concentrated at the city centers.

Accident & Congestion spot

Congestion Spot

Accident Spot as identified by

Road Safety Agency

Figure 46: GIS plotting of traffic and conjunction spot

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5. DISCUSSION
5.1 Traffic volume/flow trend at mid block and congestion
The directional traffic flow analysis for the four mid-blocks shows some interesting trends that
the two mid-blocks namely; Lideta-Mexico mid block and Mexico-Legehar midblock shows the
theoretical traffic flow trend which is a morning and evening peak. However, for the other two
mid block Torhailoch –Lideta and Wuhalimat-Haihulet midblock the directional traffic flow or
volume shows a different trend which indicates special features in these two mid-blocks.

The Torhailoch- Lideta midblock shows a traffic flow trend in which each direction has one peak.
Furthermore, the traffic volume in the two direction is not balanced which is the total traffic
volume from Torhailoch to Lideta is by far greater than the returning direction. This indicates
that there are vehicles which change their route during the evening period. When we look in to
the vehicles which change the route all of them are from the vehicle classes of car, taxi, 4WD,
minibus taxi and mid bus. The main reason for this flow unbalance or change of route by
vehicles in the evening peak period is traffic congestion due to midblock at Torhailoch round
about. Unlike the opposite direction this bottleneck is a narrow two lane with highly failed
pavement and hence, the vehicle speed highly reduced and the congestion is so intense and
vehicles are forced to change their route and also minibus taxis were unwilling to serve in this
route during the evening peak. Figure 47 shows the bottleneck at the Torhailoch roundabout.

Figure 47: The Bottleneck at Torhailoch roundabout

The Haihulet –Wuhalimat midblock traffic volume trend also shows nearly constant traffic
volume flow throughout the day. The traffic volume from the Haihulet to Wuhalimat direction is
higher than the reverse direction throughout the day and become equal at the evening. When
compared with the other three midblock this section have the highest traffic volume with about
18,281 vehicles in the 12-hour day time and the next higher traffic volume is the Torhailoch –
Lideta midblock. These results indicates that the road sections connecting the residential areas
show the highest traffic flow and each direction show different peak periods. which means one
of the directions shows either the morning or evening peak period and vice-versa.

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5.2 Travel rate and travel delays

The intersections considered in the study corridor are oversaturated according to the level of
service analysis especially during the peak period. Since the travel time observation segments
were short and at the entry of intersection, it expected to have a lower average speed. However,
the result showed that during the morning peak period vehicles move below 4.5Km/hr which is
about less than a speed of a walking old man. As Figure 27 shows average speed is higher for
a section for which the observed road length is longer. Accordingly, the Mexico-Lideta and
Atlas-Urael legs show a relatively higher speed whereas for the shortest leg which is Kasanchis-
Urael leg the average speed is small and hence the corresponding delay values become
exaggerated.

According to the delay analysis a traveler is expected to spend an extra time or delay of about
20min to travel one Km length of an intersection during the morning peak period and about 35
min during the evening peak period. Even though, the amount of the delay times during the
morning time seems different but the delay ratio which is the ratio of delay rate to the actual
travel time is nearly equal at both the morning and evening peak hour. An average delay ratio of
0.8-0.9 was observed to all intersection and during both peak periods. That is only 10-20% of
the time we invested at an intersection is needed to pass the intersection at uncongested
condition and the delay is four fold of the time required to transverse the section.

Up on severity analysis using the parameter total delay (Vehicle-Hr or Person –hr) the result
showed that an average total delay of about 1400 Vehicle-min or 15000 person-hr was lost
every 15-min during the morning peak period for only the considered length of the road.
However, during the evening peak period a relatively lower average total delay of about 800
vehicles –min or 7500 person hours lost for the segments except the Atlas-Urael leg. The
congestion severity or intensity for Atlas-Urael segment or leg is highly significant especially
during the evening peak and reach to the value of about 2900 veh-hr or 20000 person-hr.
However, if we aggregate the total delay within the day for only the road length considered, the
total veh-min delay at the six legs will be about 92,950 Veh-min ( 1550 Vehicle-hr or 193 Veh-
day) or the total person-min at the six legs will be 845,230 person-min (14,087 person-hr or
1,760 person-day). This means due to the congestion at only these six intersections in a single
day about 193 vehicles and 1,760 peoples are idle for the full day.

5.3 Traffic congestion effect on Accident

The traffic accident trend within the time of a day has been studied by different researchers
Girma (2000), Fanuel (2006) and Bitew (2002). Based on the peak trends of both traffic volume
and traffic accident during morning and evening time, all the previous researchers conclude that
there is a relationship between traffic flow and traffic accident. However, none of the researches
showed the relationship between traffic congestion parameters and traffic accident. It is true that
the more vehicles in the road the more likely collision would happen. However, the researcher of
this paper doesn’t believe that traffic flow or volume is the right parameter to be related with
traffic accident. For instance at a freeway we can have the highest traffic flow or volume than
other road sections. However, more of traffic accidents can happen at other section of the road.

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As it is indicated on Girma (2000) and other report data “Drivers error or behavior” is highly
quoted as the main (93%) cause of traffic accident in Addis Ababa accounting more than 93% of
the accident. The main mistakes listed under driver’s error and causing about 85% of the total
accident are; driving on the wrong side, failure to give way, following too close, improper
overtaking, speeding, improper turning.

As it can be seen together with other road and environmental factors, behavioral factors
contribute a lot for the traffic accident in Addis Ababa. Hence, the researcher of this paper
believes that having the other road parameters constant, traffic accident would be more related
with the behavioral and vehicle to vehicle headway factors than the traffic volume or flow. One
of the factors that affect driver behavior is the stress and frustration resulted from delay due to
traffic congestion. A questioner result showed that out of 20 drivers interviewed 17 (85%)
responded that the traffic congestion make them to stress and frustrate which make them to
misbehave and commit wrong driving.

The effect of traffic congestion on drivers or commuters can be easily understood by the amount
of delay or by the travel rate. Hence, correlation was made with the traffic accident, traffic
volume and travel rate as shown above in Figure 44 and Figure 45 respectively. According to
the result, traffic accident is shows a higher R2 or goodness of fit result for travel time than
traffic volume or flow which indicates better relation or fitness with travel rate.

Further to the correlation between the travel rate and traffic accident, the assessment of traffic
accident spots and congestion spots shows a clear relationship among the traffic accident and
traffic congestion in Addis Ababa. The traffic spots plotted in the GIS map of Figure: 46 are
identified by Ethiopian Road Safety Agency and the traffic congestion spots are identified from
the questioner result. Plotting the two spot on a single GIS map shows that most of the traffic
accident and congestion happen at or near intersection and all the identified congestion spots
fits with the accident black spots.

Therefore, the link between the traffic accident and the traffic congestion in Addis Ababa is so
significant and the researcher believes efforts made to mitigate the traffic congestion will also
minimize the traffic accident.

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6. CONCLUSION
Based on the findings of the analysis in this study, the following points are concluded.

1. The traffic flow from the residential area of the two ends (namely the east: Ayat-CMC-
Megenaga-Kotebe e.tc and the west: Ayertena-betel-Alembank e.t.c) are peak during
the morning period and only one of the lane is congested during one of the peak period.
2. Traffic congestion during the evening peak hour is more than the morning peak hours
and during the mid day the roads are almost uncongested.
3. as seen in Torhailoch intersection, traffic congestion or bottlenecks have a impact on the
traffic flow pattern
4. The intersection in the East-West study corridor of Addis Ababa are performing above
their capacity and during the peak periods the degree of saturation is almost greater
than 2 for most of the intersection and the level of service is F.
5. During both morning and evening peak periods about 80-90% of the travel time needed
to negotiate the entry lanes of an intersection is a delay.
6. The average traffic congestion intensity in Addis Ababa expressed in Veh-min or person-
min is very high and the result shows on average about 18,500 Vehicle-min or 38
vehicle-days and 169,000 Per-min or 352-person-day are wasted at each intersection
legs or congestion spot per day.
7. For only Urael intersection about 141 veh-day and 1165 person-day are wasted per
day at its three legs.
8. On average the cost of wasted fuel & idle vehicle time at each entry leg of an
intersection is above 7.7 million/year and only the three legs of Urael intersection costs
more than 28 million/year.
9. The correlation analysis and the spot analysis indicate that the traffic congestion in Addis
Ababa is strongly linked with the traffic accident.

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APPENDIXES

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APPENDIX A: Travel Time, Traffic Volume and

Vehicle Occupancy data

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Table 20: AVERAGE TRAVEL TIME AT CONGESTED SEGEMENTS (in sec)

Lideta to Wuhalimat Legehar to Mexico to Atlas to Kasanchis

Mexico to Urael Mexico Legehar Urael to Urael
Segment Length 350m 150m 100m 100m 250m 60m
Time
8:30-8:45 AM 549.6
8:45-9:00 AM 201.9
9:00-9:15 AM 149.8
9:15-9:30 AM 398.1 116.30
9:30-9:45 AM 478.3 151.00 208.0 75.8
9:45-10:00 AM 489.6 174.80 206.0 76.4
10:00-10:15 AM 453.6 178.60 156.0 74.8
10:15-10:30 AM 315.6 192.80 390.0 39.6
10:30-10:45 AM 140.0 157.90 975.0 110.9
10.45-11.00 AM 187.4 173.30 206.0 113.6
11.00-11.15 AM 470.2 83.40 141.0 84.4
11.15-11.30 AM 365.4 128.30 213.0 105.4
11.30-11.45 AM 287.8 89.20 216.0 96.8
11.45-12.00 AM 284.2 72.60 221.0 121.6
12.00-12.15 AM 182.2 118.20 246.0 108.9
12.15-12.30 AM 96.2 116.90 320.0 69.8
12.30-12.45 AM 101.0 95.50 216.0 56.3
12.45-1.00 PM 60.4 72.20 175.0 53.0
1.00-1.15 PM 61.8 65.40 60.4 23.1
1.15-1.30 PM 68.8 65.40 72.2 22.8
1.30-1.45 PM 74.0 68.00 78.0 40.9
1.45-2.00 PM 74.0 71.00 86.5 69.8
2.00-2.15 PM 77.2 72.00 132.1 81.1
2.15-2.30 PM 90.4 74.00 24.9 50.2 157.2 79.6
2.30 -2.45 PM 120.8 76.20 33.0 102.8 220.8 100.9
2.45 - 3.00 PM 108.0 178.50 33.7 58.1 523.8 40.7
3.00 -3.15 PM 117.6 162.90 34.4 93.5 622.8 108.3
3.15 -3.30 PM 137.6 141.40 23.6 70.5 409.0 56.8
3.30 -3.45 PM 203.6 139.60 23.9 70.5 572.4 85.7
3.45 -4.00 PM 221.2 115.00 23.9 68.1 421.0 73.6
4.00 -4.15 PM 201.2 106.14 23.9 68.1 214.4 107.2
4.15 - 4.30 PM 242.4 98.00 24.4 161.6 147.2 122.0
4.30 - 4.45 PM 127.2 75.70 25.9 117.5 112.4 92.8
4.45 - 5.00 PM 126.8 38.50 26.7 128.6 117.6 77.4
5.00 -5.15 PM 91.90 37.7 176.8
5.15 - 5.30 PM 74.00 116.0 178.3
5.30 -5.45 PM 94.90 173.5 283.8
5.45 -6.00 PM 86.40 181.0 178.6

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Table 21: DIRECTIONAL TRAFFIC VOLUME AT MIDBLOCKS (in PCU)

MEXICO - LIDETA MEXICO - LEGEHAR TORHAILOCH - WUHALIMAT- HAIHULET

MID BLOCK MID BLOCK LIDETA MIDBLOCK MIDBLOCK URAEL JUNCTION
Mexico to Lideta to Mexico to Legehar to Torhailoch Lideta to Wuhalimat Hiahulet to Kasanchis Atlas to
Time
Lideta Mexico Legehar Mexico to Lideta Torhailoch to Haihulet Wuhalimat to Urael Urael
7:00-7:15 AM 233 249 251 245 393 211 205 357 184 128
7:15-7:30 AM 216 334 345 268 542 266 239 413 218 152
7:30-7:45 AM 215 297 352 296 575 325 304 497 330 281
7:45-8:00 AM 216 290 337 248 497 323 406 519 335 277
8:00-8:15 AM 219 300 368 214 495 211 279 549 339 356
8:15-8:30 AM 212 316 330 295 661 254 325 519 351 315
8:30-8:45 AM 263 308 312 348 663 284 369 499 356 381
8:45-9:00 AM 245 289 297 283 469 311 360 508 267 282
9:00-9:15 AM 218 305 297 380 490 299 308 430 306 357
9:15-9:30 AM 243 257 282 323 418 184 367 446 259 278
9:30-9:45 AM 269 241 289 379 310 267 334 445 313 348
9:45-10:00 AM 202 203 270 370 403 238 315 598 284 291
10:00-10:15 AM 241 210 339 338 445 228 271 515 304 281
10:15-10:30 AM 259 212 304 389 309 266 264 428 286 267
10:30-10:45 AM 285 220 266 355 365 324 316 523 344 325
10.45-11.00 AM 246 201 249 397 274 319 343 450 380 357
11.00-11.15 AM 252 237 258 381 363 246 453 557 484 455
11.15-11.30 AM 261 199 214 306 311 301 266 498 319 298
11.30-11.45 AM 260 184 196 395 318 257 325 487 367 349
11.45-12.00 AM 299 165 237 371 261 315 389 415 431 405
12.00-12.15 AM 301 209 196 352 280 328 367 410 401 376
12.15-12.30 AM 241 198 201 389 348 255 358 395 417 390
12.30-12.45 AM 237 199 231 300 355 302 326 542 373 343
12.45-1.00 PM 232 186 274 290 239 290 331 337 382 366

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DIRECTIONAL TRAFFIC VOLUME AT MIDBLOCKS (in PCU) Continued

MEXICO - LIDETA MEXICO - LEGEHAR TORHAILOCH - WUHALIMAT- HAIHULET

MID BLOCK MID BLOCK LIDETA MIDBLOCK MIDBLOCK URAEL JUNCTION
Mexico to Lideta to Mexico to Legehar to Torhailoch Lideta to Wuhalimat Haihulet to Kasanchis Atlas to
Time
Lideta Mexico Legehar Mexico to Lideta Torhailoch to Haihulet Wuhalimat to Urael Urael
1.15-1.30 PM 205 199 272 224 241 256 329 533 374 348
1.30-1.45 PM 191 169 196 274 296 251 315 598 353 338
1.45-2.00 PM 191 177 254 346 362 215 272 462 315 290
2.00-2.15 PM 261 244 245 328 359 244 322 483 355 347
2.15-2.30 PM 237 222 212 276 346 275 297 477 332 311
2.30 -2.45 PM 233 147 261 329 368 272 355 508 359 371
2.45 - 3.00 PM 268 260 303 352 315 259 302 389 318 313
3.00 -3.15 PM 192 566 287 310 348 279 363 458 394 385
3.15 -3.30 PM 209 179 291 374 341 307 392 445 426 418
3.30 -3.45 PM 246 243 282 362 345 323 357 561 369 373
3.45 -4.00 PM 195 218 268 316 312 242 288 422 313 302
4.00 -4.15 PM 261 268 279 310 315 263 343 444 397 302
4.15 - 4.30 PM 244 265 326 273 356 323 335 401 350 381
4.30 - 4.45 PM 265 286 282 361 334 361 328 469 391 329
4.45 - 5.00 PM 242 221 347 327 350 347 379 378 341 274
5.00 -5.15 PM 256 259 302 382 407 399 317 422 394 318
5.15 - 5.30 PM 239 224 311 356 376 239 381 544 358 359
5.30 -5.45 PM 250 243 284 476 360 433 388 453 305 345
5.45 -6.00 PM 286 191 280 390 300 332 450 439 468 330
6.00 -6.15 PM 291 218 265 365 305 445 399 389 275 344
6.15 -6.30 PM 286 201 239 441 314 493 336 378 374 279
6.30 -6.45 PM 316 210 216 378 282 420 336 328 314 325
6.45-7.00 PM 269 211 218 244 250 337 335 352 243 193
TOTAL 11,692 11,431 13,182 15,959 17,653 14,180 16,083 22,090 16,531 15,609

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AVERAGE VEHICLE OCCUPANCY (PERSON/VEH)

MEXICO - LIDETA MEXICO - LEGEHAR TORHAILOCH - WUHALIMAT-
MID BLOCK MID BLOCK LIDETA MIDBLOCK HAIHULET MIDBLOCK URAEL JUNCTION
Mexico Lideta to Mexico to Legehar to Torhailoch Lideta to Wuhalimat to Hiahulet to Kasanchis Atlas to
Time
to Lideta Mexico Legehar Mexico to Lideta Torhailoch Haihulet Wuhalimat to Urael Urael
7:00-7:15 AM 23.4 15.2 16.8 18.0 16.8 18.6 15.9 8.4 8.4 15.9
7:15-7:30 AM 17.0 21.6 14.8 19.6 19.5 19.5 16.5 10.1 10.1 16.5
7:30-7:45 AM 19.3 17.6 13.7 14.3 14.9 20.5 14.8 8.1 8.1 14.8
7:45-8:00 AM 13.1 16.4 10.4 10.8 13.4 16.4 13.0 10.5 10.5 13.0
8:00-8:15 AM 13.3 12.8 10.6 16.6 14.5 14.4 15.9 9.3 9.3 15.9
8:15-8:30 AM 14.8 14.1 9.7 12.6 14.3 16.3 14.3 8.1 8.1 14.3
8:30-8:45 AM 11.5 10.8 9.3 9.5 17.1 10.3 8.5 9.7 9.7 8.5
8:45-9:00 AM 13.5 9.0 7.9 7.4 16.0 12.2 9.4 10.7 10.7 9.4
9:00-9:15 AM 10.1 12.5 8.0 11.0 15.7 11.1 9.7 9.3 9.3 9.7
9:15-9:30 AM 11.8 15.9 9.3 8.8 13.2 11.3 8.4 9.0 9.0 8.4
9:30-9:45 AM 10.2 12.5 6.2 10.6 13.9 12.6 9.7 7.7 7.7 9.7
9:45-10:00 AM 12.5 10.6 8.0 8.8 12.9 12.1 8.0 10.4 10.4 8.0
10:00-10:15 AM 7.2 12.3 8.7 8.5 12.4 11.8 8.2 6.5 6.5 8.2
10:15-10:30 AM 10.1 11.3 7.9 7.1 13.1 8.6 11.3 7.6 7.6 11.3
10:30-10:45 AM 9.7 14.4 7.7 7.5 13.1 10.0 9.0 10.0 10.0 9.0
10.45-11.00 AM 9.1 10.0 8.2 7.2 13.4 10.8 7.3 7.3 7.3 7.3
11.00-11.15 AM 10.8 12.7 11.2 8.9 13.2 12.2 9.6 8.5 8.5 9.6
11.15-11.30 AM 11.7 11.4 7.0 7.2 6.8 13.2 5.9 9.5 9.5 5.9
11.30-11.45 AM 8.8 12.3 6.8 9.8 11.3 17.3 7.8 6.9 6.9 7.8
11.45-12.00 AM 10.5 12.9 8.9 8.4 11.8 13.8 6.9 7.7 7.7 6.9
12.00-12.15 AM 12.1 9.4 4.9 7.0 13.4 13.4 7.3 8.2 8.2 7.3
12.15-12.30 AM 11.8 11.8 6.0 7.0 12.0 13.9 6.8 7.2 7.2 6.8
12.30-12.45 AM 11.7 7.7 8.1 7.8 11.8 9.9 8.6 8.7 8.7 8.6
12.45-1.00 PM 13.7 9.6 7.8 5.0 12.4 13.5 6.4 11.0 11.0 6.4

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AVERAGE VEHICLE OCCUPANCY (PERSON/VEH) continued

MEXICO - LIDETA MEXICO - LEGEHAR TORHAILOCH - LIDETA WUHALIMAT-

MID BLOCK MID BLOCK MIDBLOCK HAIHULET MIDBLOCK URAEL JUNCTION
Mexico to Lideta to Mexico to Legehar to Torhailoch Lideta to Wuhalimat Haihulet to Kasanchis Atlas to
Time
Lideta Mexico Legehar Mexico to Lideta Torhailoch to Haihulet Wuhalimat to Urael Urael
1.00-1.15 PM 12.2 10.3 8.5 10.5 10.8 16.9 8.1 8.3 8.3 8.1
1.15-1.30 PM 13.1 16.4 9.5 6.4 12.9 18.2 7.5 7.2 7.2 7.5
1.30-1.45 PM 7.2 12.6 12.7 8.6 12.8 18.9 8.4 11.2 11.2 8.4
1.45-2.00 PM 13.9 10.5 11.1 8.0 10.8 17.3 8.9 12.8 12.8 8.9
2.00-2.15 PM 7.2 11.7 9.2 6.9 9.4 18.8 6.3 9.4 9.4 6.3
2.15-2.30 PM 8.5 10.4 12.3 5.8 11.6 14.2 7.6 9.2 9.2 7.6
2.30 -2.45 PM 12.9 5.5 12.1 10.8 13.4 16.2 6.0 6.4 6.4 6.0
2.45 - 3.00 PM 11.4 10.2 8.2 8.4 11.6 16.0 7.5 7.3 7.3 7.5
3.00 -3.15 PM 9.3 24.2 8.7 7.4 10.5 12.6 6.1 7.4 7.4 6.1
3.15 -3.30 PM 12.1 12.0 7.8 8.6 9.8 13.2 8.3 8.0 8.0 8.3
3.30 -3.45 PM 10.2 12.1 7.3 7.7 9.2 15.5 7.8 8.2 8.2 7.8
3.45 -4.00 PM 11.0 10.2 7.6 10.5 9.2 17.7 8.2 8.0 8.0 8.2
4.00 -4.15 PM 13.1 11.4 8.2 11.7 12.3 20.6 7.1 10.2 10.2 7.1
4.15 - 4.30 PM 11.1 11.3 9.6 7.1 17.3 15.7 8.1 9.4 9.4 8.1
4.30 - 4.45 PM 12.6 11.6 10.6 11.8 13.6 14.2 8.2 10.1 10.1 8.2
4.45 - 5.00 PM 12.3 11.7 12.5 12.6 12.0 13.5 8.8 10.5 10.5 8.8
5.00 -5.15 PM 15.5 14.7 8.9 8.5 13.2 16.0 9.1 10.6 10.6 9.1
5.15 - 5.30 PM 13.4 14.6 10.0 11.6 12.9 15.5 9.7 13.8 13.8 9.7
5.30 -5.45 PM 12.1 16.0 9.0 11.9 14.1 19.6 12.3 11.4 11.4 12.3
5.45 -6.00 PM 17.7 13.0 7.1 14.8 17.6 18.1 9.7 11.6 11.6 9.7
6.00 -6.15 PM 14.5 8.3 10.1 12.2 14.8 20.1 11.5 11.6 11.6 11.5
6.15 -6.30 PM 14.7 13.1 8.8 12.9 13.2 17.8 9.0 9.7 9.7 9.0
6.30 -6.45 PM 13.3 12.1 8.1 10.2 12.7 17.5 8.8 13.0 13.0 8.8
6.45-7.00 PM 14.8 10.5 10.7 8.5 5.4 19.8 10.2 10.1 10.1 10.2

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APPENDIX B: Level of Service analysis output using

aaSIDRA software

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Figure 48: Haihulet Intersection Geometry using aaSIDRA

Figure 49: Total Directional Demand flow at Haihulet intersection

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Figure 50: Degree of Saturation at Haihulet Intersection

Figure 51: Leg & lane Level of Service of Haihulet Intersection

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Figure 52: Legehar intersection Geometry using aaSIDRA

Figure 53: Total Directional hourly Demand flow at Legehar intersection

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Figure 54: Lane degree of saturation for Legehar intersection

Figure 55: Leg and lane Level of Service for Legehar Intersection

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Figure 56: URAEL Intersection Geometry using aaSIDRA

Figure 57: Total hourly directional demand flow for URAEL

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Figure 58: Degree of saturation of URAEL intersection

Figure 59: Leg & lane Level of Service at URAEL intersection

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APPENDIX C: CONGESTION ANALYSIS SHEET

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LIDETA -MEXICO - MIDBLOCK ANALYSIS

Corridor Name LIDETA -MEXICO-MIDBLOCK Date:

Corridor Length 350m Page No.

Average Average Delay Per

Average Travel Delay Travel Traffic Total Segment Total Segment
Travel Vehicle Travel Time Traveler Delay
Duration travel Delay (s) Rate Rate Time Volume Delay (Vehicle- Delay (Person-
Speed Occupancy (Person - Min) (Annual Ratio
Time (S) (min/Km) (min/Km) Index (Vec) (persons/veh) Min) Min)
(km/h) Hours)

8:30-8:45 AM 549.65 489.25 2.29 26.17 23.30 9.10 233 10.79 23,024.76 1,899.91 20,494.60 33.98 0.89

8:45-9:00 AM 201.85 141.45 6.24 9.61 6.74 3.34 227 9.03 6,897.40 535.15 4,833.48 9.82 0.70

9:00-9:15 AM 149.75 89.35 8.41 7.13 4.25 2.48 243 12.53 7,596.51 361.87 4,532.54 6.20 0.60

9:15-9:30 AM 398.10 337.70 3.17 18.96 16.08 6.59 195 15.87 20,533.78 1,097.53 17,418.38 23.45 0.85

9:30-9:45 AM 478.30 417.90 2.63 22.78 19.90 7.92 182 12.52 18,157.92 1,267.63 15,864.92 29.02 0.87

9:45-10:00 AM 489.60 429.20 2.57 23.31 20.44 8.11 157 10.60 13,577.70 1,123.07 11,902.67 29.81 0.88

10:00-10:15 AM 453.60 393.20 2.78 21.60 18.72 7.51 167 12.34 15,578.72 1,094.41 13,504.31 27.31 0.87

10:15-10:30 AM 315.60 255.20 3.99 15.03 12.15 5.23 163 11.34 9,724.33 693.29 7,863.28 17.72 0.81

10:30-10:45 AM 140.00 79.60 9.00 6.67 3.79 2.32 172 14.42 5,786.77 228.19 3,290.19 5.53 0.57

10:45-11:00 AM 187.40 127.00 6.72 8.92 6.05 3.10 156 10.00 4,871.55 330.20 3,301.42 8.82 0.68

11:00-11:15 AM 470.20 409.80 2.68 22.39 19.51 7.78 187 12.69 18,589.46 1,277.21 16,201.53 28.46 0.87

11:15-11:30 AM 365.40 305.00 3.45 17.40 14.52 6.05 149 11.45 10,386.25 757.42 8,669.42 21.18 0.83

11:30-11:45 AM 287.80 227.40 4.38 13.70 10.83 4.76 143 12.31 8,446.36 541.97 6,673.74 15.79 0.79

11:45-12:00 AM 284.20 223.80 4.43 13.53 10.66 4.71 126 12.88 7,689.89 469.98 6,055.58 15.54 0.79

By Wondwossen Taddesse 95
 Assessing & Quantifying the Level of Traffic Congestion at Major Intersections in Addis Ababa

Average Average Travel Total Delay Per

Average Travel Delay Travel Traffic Total Segment
Delay Travel Vehicle Time Segment Traveler Delay
Duration travel Rate Rate Time Volume Delay
(s) Speed Occupancy (Person - Delay (Annual Ratio
Time (S) (min/Km) (min/Km) Index (Vec) (persons/veh) (Person-Min)
(km/h) Min) (Vehicle-Min) Hours)
12:00-00:15PM 182.20 121.80 6.92 8.68 5.80 3.02 163 9.36 4,632.45 330.89 3,096.77 8.46 0.67

12:15-12:30 PM 96.20 35.80 13.10 4.58 1.70 1.59 151 11.80 2,856.71 90.10 1,063.10 2.49 0.37

12:30-12:45 PM 101.00 40.60 12.48 4.81 1.93 1.67 162 7.70 2,098.99 109.62 843.75 2.82 0.40

12:45-1:00 PM 60.40 0.00 20.86 2.88 0.00 1.00 146 9.58 1,408.32 - - - -

1:00-1:15 PM 61.80 1.40 20.39 2.94 0.07 1.02 168 10.29 1,781.28 3.92 40.35 0.10 0.02

1:15 -1:30 PM 68.80 8.40 18.31 3.28 0.40 1.14 154 16.37 2,891.08 21.56 352.98 0.58 0.12

1:30 -1:45 PM 74.00 13.60 17.03 3.52 0.65 1.23 132 12.59 2,050.46 29.92 376.84 0.94 0.18

1:45-2:00 PM 74.00 13.60 17.03 3.52 0.65 1.23 149 10.51 1,930.68 33.77 354.83 0.94 0.18

2:00-2:15 PM 77.20 16.80 16.32 3.68 0.80 1.28 188 11.68 2,825.48 52.64 614.87 1.17 0.22

2:15-2:30 PM 90.40 30.00 13.94 4.30 1.43 1.50 170 10.35 2,651.19 85.00 879.82 2.08 0.33

2:30 -2:45 PM 120.80 60.40 10.43 5.75 2.88 2.00 120 5.51 1,330.23 120.80 665.11 4.19 0.50

2:45-3:00 PM 108.00 47.60 11.67 5.14 2.27 1.79 205 10.16 3,749.32 162.63 1,652.48 3.31 0.44

3:00-3:15 PM 117.60 57.20 10.71 5.60 2.72 1.95 407 24.15 19,265.40 388.01 9,370.58 3.97 0.49

3:15-3:30 PM 137.60 77.20 9.16 6.55 3.68 2.28 146 11.95 4,001.41 187.85 2,244.98 5.36 0.56

3:30-3:45 PM 203.60 143.20 6.19 9.70 6.82 3.37 191 12.10 7,841.39 455.85 5,515.16 9.94 0.70

3:45-4:00 PM 221.20 160.80 5.70 10.53 7.66 3.66 179 10.20 6,731.78 479.72 4,893.63 11.17 0.73

4:00-4:15 PM 201.20 140.80 6.26 9.58 6.70 3.33 206 11.37 7,853.86 483.41 5,496.14 9.78 0.70

4:15-4:30PM 242.40 182.00 5.20 11.54 8.67 4.01 199 11.27 9,063.79 603.63 6,805.32 12.64 0.75

4:30-4:45 PM 127.20 66.80 9.91 6.06 3.18 2.11 219 11.63 5,399.99 243.82 2,835.84 4.64 0.53

4:45-5:00 PM 126.80 66.40 9.94 6.04 3.16 2.10 167 11.73 4,141.12 184.81 2,168.54 4.61 0.52

By Wondwossen Taddesse 96
 Assessing & Quantifying the Level of Traffic Congestion at Major Intersections in Addis Ababa

MEXICO - LEGEHAR MIDBLOCK ANALYSIS

Corridor Name MEXICO
MEXICO-LEGEHAR MIDBLOCK Date:

Corridor Length (m) 100 Page No.

Averag Total Total Delay

Average Travel
Average e Travel Delay Travel Traffic Segment Segment Per
Vehicle Time Delay
Duration travel Delay (s) Travel Rate Rate Time Volume Delay Delay Traveler
Occupancy (Person - Ratio
Time (S) Speed (min/Km) (min/Km) Index (Vec) (Vehicle-
(Vehicle- (Person-
(Person- (Annual
(persons/veh) Min)
(km/h) Min) Min) Hours)

2:15-2:30 PM 50.20 50.20 7.17 8.37 0.00 1.00 168 12.32 1,732.14 140.56 1,732.14 3.49 0.00

2:30 -2:45 PM 102.80 102.80 3.50 17.13 8.77 2.05 214 12.07 4,426.57 366.65 4,426.57 7.14 0.51

2:45-3:00 PM 58.10 58.10 6.20 9.68 1.32 1.16 259 8.20 2,055.62 250.80 2,055.62 4.03 0.14

3:00-3:15 PM 93.50 93.50 3.85 15.58 7.22 1.86 239 8.68 3,233.34 372.44 3,233.34 6.49 0.46

3:15-3:30 PM 70.50 70.50 5.11 11.75 3.38 1.40 243 7.77 2,219.51 285.53 2,219.51 4.90 0.29

3:30-3:45 PM 70.50 70.50 5.11 11.75 3.38 1.40 235 7.31 2,017.44 276.13 2,017.44 4.90 0.29

3:45-4:00 PM 68.10 68.10 5.29 11.35 2.98 1.36 225 7.61 1,943.94 255.38 1,943.94 4.73 0.26

4:00-4:15 PM 68.10 68.10 5.29 11.35 2.98 1.36 225 8.16 2,083.94 255.38 2,083.94 4.73 0.26

4:15-4:30PM 161.60 161.60 2.23 26.93 18.57 3.22 262 9.63 6,792.28 705.65 6,792.28 11.22 0.69

4:30-4:45 PM 117.50 117.50 3.06 19.58 11.22 2.34 221 10.58 4,577.09 432.79 4,577.09 8.16 0.57

4:45-5:00 PM 128.60 128.60 2.80 21.43 13.07 2.56 235 12.48 6,288.32 503.68 6,288.32 8.93 0.61

5:00-5:15 PM 176.8 176.80 2.04 29.47 21.10 3.52 245 8.88 6,412.45 721.93 6,412.45 12.28 0.72

5:15-5:30 PM 178.3 178.30 2.02 29.72 21.35 3.55 247 10.04 7,368.19 734.00 7,368.19 12.38 0.72

5:30-5:45PM 283.8 283.80 1.27 47.30 38.93 5.65 232 9.00 9,871.75 1,097.36 9,871.75 19.71 0.82

5:45-6:00 PM 178.6 178.60 2.02 29.77 21.40 3.56 230 7.13 4,883.62 684.63 4,883.62 12.40 0.72

By Wondwossen Taddesse 97
 Assessing & Quantifying the Level of Traffic Congestion at Major Intersections in Addis Ababa

LEGEHAR - MEXICO - MIDBLOCK ANALYSIS

Corridor Name LEGEHAR - MEXICO-
MEXICO- MIDBLOCK Date:

Corridor Length (m) 100 Page No.

Average Total Total Delay

Average Travel Travel
Average Delay Travel Traffic Vehicle Segment Segment Per
Travel Rate Time Delay
Duration travel Delay (s) Rate Time Volume Occupancy Delay Delay Traveler
Speed (min/Km (Person - Ratio
Time (S) (min/Km) Index (Vec) (persons/veh (Vehicle-
(Vehicle- (Person-
(Person- (Annual
(km/h) ) Min)
) Min) Min) Hours)

2:15-2:30 PM 24.90 24.90 14.46 4.15 0.22 1.06 246 5.83 594.91 5.33 31.06 0.09 0.05

2:30 -2:45 PM 33.00 33.00 10.91 5.50 1.57 1.40 270 10.84 1,609.01 42.30 458.32 0.65 0.28

2:45-3:00 PM 33.67 33.67 10.69 5.61 1.68 1.43 302 8.37 1,418.98 50.67 424.29 0.70 0.30

3:00-3:15 PM 34.40 34.40 10.47 5.73 1.80 1.46 263 7.36 1,110.46 47.34 348.63 0.75 0.31

3:15-3:30 PM 23.60 23.60 15.25 3.93 0.00 1.00 319 8.63 1,083.07 - - 0.00 0.00

3:30-3:45 PM 23.90 23.90 15.06 3.98 0.05 1.01 309 7.72 949.93 1.54 11.92 0.02 0.01

3:45-4:00 PM 23.90 23.90 15.06 3.98 0.05 1.01 263 10.53 1,103.57 1.31 13.85 0.02 0.01

4:00-4:15 PM 23.86 23.86 15.09 3.98 0.04 1.01 249 11.69 1,157.49 1.07 12.48 0.02 0.01

4:15-4:30PM 24.43 24.43 14.74 4.07 0.14 1.04 234 7.08 674.07 3.23 22.86 0.06 0.03

4:30-4:45 PM 25.86 25.86 13.92 4.31 0.38 1.10 286 11.84 1,459.84 10.76 127.43 0.16 0.09

4:45-5:00 PM 26.67 26.67 13.50 4.44 0.51 1.13 254 12.61 1,423.61 12.98 163.71 0.21 0.12

5:00-5:15 PM 37.71 37.71 9.55 6.29 2.35 1.60 310 8.45 1,646.89 72.92 616.34 0.98 0.37

5:15-5:30 PM 116.00 116.00 3.10 19.33 15.40 4.92 281 11.64 6,325.40 432.74 5,038.51 6.42 0.80

5:30-5:45PM 173.50 173.50 2.07 28.92 24.98 7.35 365 11.87 12,529.72 911.89 10,825.39 10.41 0.86

5:45-6:00 PM 181.00 181.00 1.99 30.17 26.23 7.67 311 14.83 13,911.66 815.86 12,097.76 10.93 0.87

By Wondwossen Taddesse 98
 Assessing & Quantifying the Level of Traffic Congestion at Major Intersections in Addis Ababa

URAEL JUNCTION : HAIHULET - URAEL LEG ANALYSIS

Corridor Name HAIHULET- URAEL-LEG Date:

Coridor Length (m) 150 Page No.

Average Average Delay Per

Average Travel Delay Travel Traffic Total Segment Total Segment
Travel Vehicle Travel Time Traveler Delay
Duration travel Delay (s) Rate Rate Time Volume Delay (Vehicle- Delay (Person-
Speed Occupancy (Person - Min) (Annual Ratio
Time (S) (min/Km) (min/Km) Index (Vec) (persons/veh) Min) Min)
(km/h) Hours)

9:15-9:30 AM 116.30 77.80 4.64 12.92 8.64 3.02 656 9.01 11,450.28 850.61 7,659.77 5.40 0.67

9:30-9:45 AM 151.00 112.50 3.58 16.78 12.50 3.92 644 7.67 12,424.88 1,207.50 9,256.95 7.81 0.75

9:45-10:00 AM 174.80 136.30 3.09 19.42 15.14 4.54 741 10.39 22,440.35 1,683.31 17,497.82 9.47 0.78

10:00-10:15 AM 178.60 140.10 3.02 19.84 15.57 4.64 653 6.50 12,625.56 1,524.76 9,903.93 9.73 0.78

10:15-10:30 AM 192.80 154.30 2.80 21.42 17.14 5.01 565 7.64 13,864.28 1,452.99 11,095.74 10.72 0.80

10:30-10:45 AM 157.90 119.40 3.42 17.54 13.27 4.10 677 10.04 17,883.38 1,347.23 13,522.96 8.29 0.76

10:45-11:00 AM 173.30 134.80 3.12 19.26 14.98 4.50 672 7.33 14,227.61 1,509.76 11,066.83 9.36 0.78

11:00-11:15 AM 83.40 44.90 6.47 9.27 4.99 2.17 823 8.54 9,772.15 615.88 5,261.03 3.12 0.54

11:15-11:30 AM 128.30 89.80 4.21 14.26 9.98 3.33 623 9.49 12,645.51 932.42 8,850.87 6.24 0.70

11:30-11:45 AM 89.20 50.70 6.05 9.91 5.63 2.32 685 6.87 6,992.41 578.83 3,974.38 3.52 0.57

11:45-12:00 AM 72.60 34.10 7.44 8.07 3.79 1.89 678 7.66 6,285.11 385.33 2,952.10 2.37 0.47

12:00-00:15PM 118.20 79.70 4.57 13.13 8.86 3.07 656 8.17 10,562.99 871.39 7,122.42 5.53 0.67

12:15-12:30 PM 116.90 78.40 4.62 12.99 8.71 3.04 652 7.20 9,145.95 851.95 6,133.81 5.44 0.67

12:30-12:45 PM 95.50 57.00 5.65 10.61 6.33 2.48 712 8.66 9,817.23 676.40 5,859.50 3.96 0.60

By Wondwossen Taddesse 99
 Assessing & Quantifying the Level of Traffic Congestion at Major Intersections in Addis Ababa

Average Average Travel Total Delay Per

Average Travel Delay Travel Traffic Total Segment
Delay Travel Vehicle Time Segment Traveler Delay
Duration travel Rate Rate Time Volume Delay
(s) Speed Occupancy (Person - Delay (Annual Ratio
Time (S) (min/Km) (min/Km) Index (Vec) (persons/veh) (Person-Min)
(km/h) Min) (Vehicle-Min) Hours)
12:45-1:00 PM 72.20 33.70 7.48 8.02 3.74 1.88 552 10.97 7,284.97 310.04 3,400.33 2.34 0.47

1:00-1:15 PM 65.40 26.90 8.26 7.27 2.99 1.70 651 8.28 5,874.99 291.87 2,416.47 1.87 0.41

1:15 -1:30 PM 65.40 26.90 8.26 7.27 2.99 1.70 730 7.22 5,746.67 327.28 2,363.69 1.87 0.41

1:30 -1:45 PM 68.00 29.50 7.94 7.56 3.28 1.77 737 11.20 9,355.39 362.36 4,058.59 2.05 0.43

1:45-2:00 PM 71.00 32.50 7.61 7.89 3.61 1.84 592 12.77 8,943.15 320.67 4,093.69 2.26 0.46

2:00-2:15 PM 72.00 33.50 7.50 8.00 3.72 1.87 673 9.39 7,587.25 375.76 3,530.18 2.33 0.47

2:15-2:30 PM 74.00 35.50 7.30 8.22 3.94 1.92 627 9.22 7,128.30 370.98 3,419.66 2.47 0.48

2:30 -2:45 PM 76.20 37.70 7.09 8.47 4.19 1.98 729 6.43 5,954.49 458.06 2,945.99 2.62 0.49

2:45-3:00 PM 178.50 140.00 3.03 19.83 15.56 4.64 580 7.30 12,600.90 1,353.33 9,883.06 9.72 0.78

3:00-3:15 PM 162.90 124.40 3.31 18.10 13.82 4.23 700 7.37 14,005.61 1,451.33 10,695.51 8.64 0.76

3:15-3:30 PM 141.40 102.90 3.82 15.71 11.43 3.67 710 8.01 13,395.84 1,217.65 9,748.46 7.15 0.73

3:30-3:45 PM 139.60 101.10 3.87 15.51 11.23 3.63 757 8.25 14,526.20 1,275.55 10,520.05 7.02 0.72

3:45-4:00 PM 115.00 76.50 4.70 12.78 8.50 2.99 602 8.00 9,233.08 767.55 6,142.01 5.31 0.67

4:00-4:15 PM 106.14 67.64 5.09 11.79 7.52 2.76 667 10.22 12,054.28 751.96 7,681.97 4.70 0.64

4:15-4:30PM 98.00 59.50 5.51 10.89 6.61 2.55 610 9.42 9,386.26 604.92 5,698.80 4.13 0.61

4:30-4:45 PM 75.70 37.20 7.13 8.41 4.13 1.97 654 10.08 8,316.22 405.48 4,086.70 2.58 0.49

4:45-5:00 PM 38.50 0.00 14.03 4.28 0.00 1.00 629 10.46 4,219.75 - - 0.00 0.00

5:00-5:15 PM 91.90 53.40 5.88 10.21 5.93 2.39 612 10.55 9,891.83 544.68 5,747.81 3.71 0.58

5:15-5:30 PM 74.00 35.50 7.30 8.22 3.94 1.92 736 13.78 12,513.01 435.47 6,002.86 2.47 0.48

5:30-5:45PM 94.90 56.40 5.69 10.54 6.27 2.46 671 11.38 12,081.30 630.74 7,180.03 3.92 0.59

5:45-6:00 PM 86.40 47.90 6.25 9.60 5.32 2.24 730 11.57 12,167.05 582.78 6,745.39 3.33 0.55

By Wondwossen Taddesse 100

 Assessing & Quantifying the Level of Traffic Congestion at Major Intersections in Addis Ababa

URAEL JUNCTION : KASANCHIS - URAEL LEG ANALYSIS

Corridor Name KASANCHIS- URAEL-LEG Date:

Corridor Length 60m Page No.

Total
Average Average Delay Per
Average Travel Delay Travel Traffic Travel Time Segment Total Segment
Travel Vehicle Traveler Delay
Duration travel Delay (s) Rate Rate Time Volume (Person - Delay Delay
Speed Occupancy (Annual Ratio
Time (S) (min/Km) (min/Km) Index (Vec) (persons/veh) Min) (Vehicle- (Person-Min)
(km/h) Hours)
Min)

9:30-9:45 AM 75.80 53.00 2.85 21.06 14.72 3.32 270 7.7 2,614.94 238.50 1,828.39 3.68 0.70

9:45-10:00 AM 76.40 53.60 2.83 21.22 14.89 3.35 248 10.4 3,282.58 221.55 2,302.96 3.72 0.70

10:00-10:15 AM 74.75 51.95 2.89 20.76 14.43 3.28 247 6.5 1,998.78 213.86 1,389.12 3.61 0.69

10:15-10:30 AM 39.57 16.77 5.46 10.99 4.66 1.74 234 7.6 1,178.53 65.41 499.49 1.16 0.42

10:30-10:45 AM 110.88 88.08 1.95 30.80 24.47 4.86 282 10.0 5,230.72 413.95 4,155.09 6.12 0.79

10:45-11:00 AM 113.60 90.80 1.90 31.56 25.22 4.98 313 7.3 4,343.97 473.67 3,472.12 6.31 0.80

11:00-11:15 AM 84.38 61.58 2.56 23.44 17.10 3.70 394 8.5 4,732.98 404.34 3,454.02 4.28 0.73

11:15-11:30 AM 105.40 82.60 2.05 29.28 22.94 4.62 261 9.5 4,352.36 359.33 3,410.86 5.74 0.78

11:30-11:45 AM 96.80 74.00 2.23 26.89 20.56 4.25 302 6.9 3,347.64 372.71 2,559.15 5.14 0.76

11:45-12:00 AM 121.60 98.80 1.78 33.78 27.44 5.33 352 7.7 5,465.41 579.63 4,440.64 6.86 0.81

12:00-00:15PM 108.86 86.06 1.98 30.24 23.90 4.77 328 8.2 4,864.03 470.45 3,845.26 5.98 0.79

12:15-12:30 PM 69.80 47.00 3.09 19.39 13.06 3.06 342 7.2 2,864.49 267.90 1,928.81 3.26 0.67

12:30-12:45 PM 56.29 33.49 3.84 15.63 9.30 2.47 304 8.7 2,470.46 169.66 1,469.73 2.33 0.59

By Wondwossen Taddesse 101

 Assessing & Quantifying the Level of Traffic Congestion at Major Intersections in Addis Ababa

Average Average Travel Delay Per

Average Travel Delay Travel Traffic Total Segment Total Segment
Delay Travel Vehicle Time Traveler Delay
Duration travel Rate Rate Time Volume Delay (Vehicle- Delay
(s) Speed Occupancy (Person - (Annual Ratio
Time (S) (min/Km) (min/Km) Index (Vec) (persons/veh) Min) (Person-Min)
(km/h) Min) Hours)
4.08 14.72 8.39 2.32 313 11.0 3,032.30 157.54 1,727.84 2.10 0.57
30.20
12:45-1:00 PM 53.00
9.34 6.42 0.09 1.01 324 8.3 1,033.89 1.76 14.53 0.02 0.01
0.32
1:00-1:15 PM 23.13
9.47 6.33 0.00 1.00 303 7.2 831.56 - - 0.00 0.00
0.00
1:15 -1:30 PM 22.80
5.28 11.35 5.02 1.79 292 11.2 2,228.06 87.97 985.25 1.26 0.44
18.08
1:30 -1:45 PM 40.88
2.92 20.56 14.22 3.25 254 12.8 3,999.22 216.75 2,767.03 3.56 0.69
51.20
1:45-2:00 PM 74.00
1.87 32.15 25.82 5.08 296 9.4 5,364.75 458.55 4,308.02 6.45 0.80
92.95
2:00-2:15 PM 115.75
1.97 30.42 24.08 4.80 275 9.2 4,626.30 397.38 3,663.01 6.02 0.79
86.70
2:15-2:30 PM 109.50
2.18 27.54 21.21 4.35 307 6.4 3,262.58 390.62 2,512.28 5.30 0.77
76.34
2:30 -2:45 PM 99.14
2.20 27.22 20.89 4.30 264 7.3 3,148.95 330.88 2,416.33 5.22 0.77
75.20
2:45-3:00 PM 98.00
1.50 39.93 33.60 6.30 325 7.4 5,738.18 655.15 4,828.06 8.40 0.84
120.95
3:00-3:15 PM 143.75
1.59 37.64 31.31 5.94 354 8.0 6,400.37 664.93 5,323.40 7.83 0.83
112.70
3:15-3:30 PM 135.50
1.72 34.88 28.55 5.51 311 8.2 5,368.12 532.70 4,393.43 7.14 0.82
102.77
3:30-3:45 PM 125.57
1.86 32.30 25.97 5.10 258 8.0 4,001.28 401.99 3,216.75 6.49 0.80
93.49
3:45-4:00 PM 116.29
1.38 43.33 37.00 6.84 347 10.2 9,216.77 770.34 7,869.70 9.25 0.85
133.20
4:00-4:15 PM 156.00
1.33 45.06 38.72 7.11 308 9.4 7,844.01 715.59 6,741.40 9.68 0.86
139.40
4:15-4:30PM 162.20
1.55 38.67 32.33 6.11 353 10.1 8,254.03 684.82 6,902.08 8.08 0.84
116.40
4:30-4:45 PM 139.20
1.75 34.27 27.94 5.41 288 10.5 6,191.48 482.76 5,047.28 6.98 0.82
100.58
4:45-5:00 PM 123.38

By Wondwossen Taddesse 102

 Assessing & Quantifying the Level of Traffic Congestion at Major Intersections in Addis Ababa

URAEL JUNCTION : ATLAS HOTEL - URAEL LEG ANALYSIS

Corridor Name ATLAS HOTEL- URAEL-LEG Date:

Corridor Length 250m Page No.

Total
Average Average Delay Per
Average Travel Delay Travel Traffic Segment Total Segment
Travel Vehicle Travel Time Traveler Delay
Duration travel Delay (s) Rate Rate Time Volume Delay Delay
Speed Occupancy (Person - Min) (Annual Ratio
Time (S) (min/Km) (min/Km) Index (Vec) (persons/veh) (Vehicle- (Person-Min)
(km/h) Hours)
Min)

9:30-9:45 AM 208.0 147.60 4.33 13.87 9.84 3.44 319 9.7 10,759.87 784.74 7,635.37 10.25 0.71

9:45-10:00 AM 206.0 145.60 4.37 13.73 9.71 3.41 273 8.0 7,524.22 662.48 5,318.09 10.11 0.71

10:00-10:15 AM 156.0 95.60 5.77 10.40 6.37 2.58 261 8.2 5,536.85 415.86 3,393.10 6.64 0.61

10:15-10:30 AM 390.0 329.60 2.31 26.00 21.97 6.46 248 11.3 18,245.28 1,362.35 15,419.60 22.89 0.85

10:30-10:45 AM 375.0 314.60 2.40 25.00 20.97 6.21 300 9.0 16,931.49 1,573.00 14,204.39 21.85 0.84

10:45-11:00 AM 206.0 145.60 4.37 13.73 9.71 3.41 334 7.3 8,321.83 810.51 5,881.84 10.11 0.71

11:00-11:15 AM 141.0 80.60 6.38 9.40 5.37 2.33 423 9.6 9,544.73 568.23 5,456.06 5.60 0.57

11:15-11:30 AM 213.0 152.60 4.23 14.20 10.17 3.53 277 5.9 5,766.29 704.50 4,131.16 10.60 0.72

11:30-11:45 AM 216.0 155.60 4.17 14.40 10.37 3.58 324 7.8 9,064.18 840.24 6,529.57 10.81 0.72

11:45-12:00 AM 221.0 160.60 4.07 14.73 10.71 3.66 377 6.9 9,606.66 1,009.10 6,981.13 11.15 0.73

12:00-00:15PM 246.0 185.60 3.66 16.40 12.37 4.07 352 7.3 10,521.15 1,088.85 7,937.91 12.89 0.75

12:15-12:30 PM 320.0 259.60 2.81 21.33 17.31 5.30 364 6.8 13,109.36 1,574.91 10,634.97 18.03 0.81

12:30-12:45 PM 216.0 155.60 4.17 14.40 10.37 3.58 320 8.6 9,958.65 829.87 7,173.92 10.81 0.72

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Average Average Travel Delay Per

Average Travel Delay Travel Traffic Total Segment Total Segment
Delay Travel Vehicle Time Traveler Delay
Duration travel Rate Rate Time Volume Delay (Vehicle- Delay
(s) Speed Occupancy (Person - (Annual Ratio
Time (S) (min/Km) (min/Km) Index (Vec) (persons/veh) Min) (Person-Min)
(km/h) Min) Hours)

12:45-1:00 PM 175.0 114.60 5.14 11.67 7.64 2.90 338 6.4 6,287.57 645.58 4,117.46 7.96 0.65

1:00-1:15 PM 60.4 0.00 14.90 4.03 0.00 1.00 348 8.1 2,836.05 - - 0.00 0.00

1:15 -1:30 PM 72.2 11.80 12.47 4.81 0.79 1.20 322 7.5 2,888.79 63.33 472.13 0.82 0.16

1:30 -1:45 PM 78.0 17.60 11.54 5.20 1.17 1.29 312 8.4 3,409.81 91.52 769.39 1.22 0.23

1:45-2:00 PM 86.5 26.10 10.40 5.77 1.74 1.43 267 8.9 3,427.19 116.15 1,034.10 1.81 0.30

2:00-2:15 PM 132.1 71.70 6.81 8.81 4.78 2.19 323 6.3 4,487.10 385.99 2,435.47 4.98 0.54

2:15-2:30 PM 157.2 96.80 5.73 10.48 6.45 2.60 291 7.6 5,761.00 469.48 3,547.49 6.72 0.62

2:30 -2:45 PM 220.8 160.40 4.08 14.72 10.69 3.66 348 6.0 7,649.45 930.32 5,556.94 11.14 0.73

2:45-3:00 PM 523.8 463.40 1.72 34.92 30.89 8.67 291 7.5 18,991.86 2,247.49 16,801.89 32.18 0.88

3:00-3:15 PM 622.8 562.40 1.45 41.52 37.49 10.31 360 6.1 22,807.54 3,374.40 20,595.63 39.06 0.90

3:15-3:30 PM 409.0 348.60 2.20 27.27 23.24 6.77 391 8.3 22,083.10 2,271.71 18,821.93 24.21 0.85

3:30-3:45 PM 572.4 512.00 1.57 38.16 34.13 9.48 348 7.8 25,906.99 2,969.60 23,173.26 35.56 0.89

3:45-4:00 PM 421.0 360.60 2.14 28.07 24.04 6.97 281 8.2 16,230.89 1,688.81 13,902.28 25.04 0.86

4:00-4:15 PM 214.4 154.03 4.20 14.30 10.27 3.55 272 7.1 6,925.94 698.26 4,975.05 10.70 0.72

4:15-4:30PM 147.2 86.80 6.11 9.81 5.79 2.44 347 8.1 6,863.29 501.99 4,047.10 6.03 0.59

4:30-4:45 PM 112.4 52.03 8.01 7.50 3.47 1.86 308 8.2 4,732.05 267.08 2,189.85 3.61 0.46

4:45-5:00 PM 117.6 57.17 7.65 7.84 3.81 1.95 253 8.8 4,374.24 241.07 2,127.06 3.97 0.49

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APPENDIX D: TRAFFIC ACCIDENT DATA

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 Assessing & Quantifying the Level of Traffic Congestion at Major Intersections in Addis Ababa

TRAFFIC ACCIDENT DATA

Year Average Traffic

Time of a day Total
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Accident
01:00-02:00 AM 51 44 54 51 60 63 50 76 69 92 610 152.50
02:00-03:00 AM 47 21 36 43 54 33 48 64 79 76 501 125.25
03:00-04:00 AM 28 23 36 34 36 29 45 49 58 82 420 105.00
04:00-05:00 AM 12 25 38 31 24 32 34 37 65 57 355 88.75
05:00-06:00 AM 63 75 72 78 57 59 67 90 98 86 745 186.25
06:00-07:00 AM 161 181 232 191 180 167 158 222 228 171 1,891 472.75
07:00-08:00 AM 380 442 566 461 485 432 428 535 528 345 4,602 1,150.50
08:00-09:00 AM 435 497 583 517 540 484 591 673 763 710 5,793 1,448.25
09:00-10:00 AM 433 488 602 517 475 523 585 624 699 785 5,731 1,432.75
10:00-11:00 AM 395 442 617 478 463 515 582 670 746 770 5,678 1,419.50
11:00-12:00 AM 407 459 648 481 531 518 567 616 754 814 5,795 1,448.75
12:00-01:00 PM 380 425 479 402 407 465 512 533 639 759 5,001 1,250.25
01:00-02:00 PM 373 397 485 432 434 433 425 531 558 623 4,691 1,172.75
02:00-03:00 PM 342 447 548 509 479 459 447 535 687 605 5,058 1,264.50
03:00-04:00 PM 453 504 589 494 492 532 518 581 680 673 5,516 1,379.00
04:00-05:00 PM 450 504 593 561 488 453 483 590 685 679 5,486 1,371.50
05:00-06:00 PM 443 457 573 471 478 428 476 568 639 657 5,190 1,297.50
06:00-07:00 PM 301 335 429 339 330 352 357 396 530 688 4,057 1,014.25
07:00-08:00 PM 344 385 457 390 385 401 346 390 499 499 4,096 1,024.00
08:00-09:00 PM 239 245 297 286 263 287 312 287 422 484 3,122 780.50
09:00-10:00 PM 173 157 226 216 250 206 230 204 293 355 2,310 577.50
10:00-11:00 PM 123 121 190 179 156 162 168 158 218 221 1,696 424.00
11:00-12:00 PM 104 123 140 95 121 100 109 120 148 162 1,222 305.50
12:00 -01:00 AM 65 55 91 89 105 70 84 87 104 150 900 225.00
Total 6,202 6,852 8,581 7,345 7,293 7,203 7,622 8,636 10,189 10,543 80,466

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By Wondwossen Taddesse 107