TRANSPORTATION ENGINEERING
AND PLANNING
(110 401367)
SPRING 2019-2020
Lecture. No. 7
Trip generation
Dr. Hamza Alkuime
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Topic 2 : Transportation planning
References
■ Nicholas Garber and Lester Hoel ,Traffic & Highway
Engineering, 5th Edition.. Cengage Learning, 2015
➢ Chapter 12 : Forecasting Travel Demand
❖ Section 11.4
■ Daniel J Findley, Christopher Cunningham, Bastian J.
Schroeder, Thomas H. Brown, Highway Engineering:
Planning, Design, and Operations, 2016, Elsevier
➢ Chapter 2.2: Planning concepts and Four-step process overview
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Topic 2 : Transportation planning
References
■ Nicholas Garber and Lester Hoel ,Principles of Highway
engineering and traffic analysis, 5th Edition, 2012
➢ Chapter 8 : Travel Demand and traffic forecasting
■ Partha chakroborty and Animesh Das, Principles of
transportation engineering, 2012,
➢ Chapter 9: Transportation demand analysis
■ Dušan Teodorović and Milan Janić,Transportation
engineering theory, practice and modeling , 2017,
➢ Chapter 8: Transportation demand analysis
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Review
Planning-Level Demand Estimation
■ The traffic that this land use will add to the highway and
transit facility can be determined using the four-step
process
■ The urban traffic forecasting process involves
➢Trip generation (How many trips )
➢Trip distribution (From where to where)
➢Modal split (On what mode)
➢Network assignment (On what route)
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Review
Travel Forecasting Process
■ Four-step process”
➢ Trip generation
❖ How many trips
➢ Trip distribution
❖ From where to where
➢ Modal choice
❖ On what mode
➢ Traffic assignment
❖ On what route
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1. Trip generation
■ Trip generation estimates the number of trips
generated for a given land use, based on prior data of
traffic generators in the same land use category
– The number of trips are typically a function of the type of land use, as well
as its size .
■ Trip generation is the first and most important analysis
step in travel forecasting, for both short- and long-term
planning analyses.
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Trip generation
The main methods for estimating generated trips
Trip generation estimation
ITE Trip Generation Manual
Rates Based on Activity
Units
Develop a customized
equation
Cross-Classification (e.g., planning model)
Trip generation
Rates based on activity units
■ Rate: it refers to the number of trips
per day per activity center.
■ Trip generation rates and
characteristics in the United States
are collected and distributed in the
ITE Trip Generation Manual
➢It contains data for a total of 172 land use
types, based on a sample of more than
5500 sites across the United States
➢The ITE manual applies to single land use,
homogeneous sites or zones and covers
significantly more than 100 land uses
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ITE Trip generation manual
Land use types
Common land use types featured in the manual include:
■ Port and terminal
➢ 6 land uses, including water ports, airports, transit stations, etc.
■ Industrial
➢ 9 land uses, including general light and heavy industrial,
manufacturing, warehousing, etc.
■ Residential
➢ 19 land uses, including single-family homes, various types of
apartments, senior living, mobile home parks, etc.
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ITE Trip generation manual
Land use types
■ Recreational
➢ 35 land uses, including various parks, fitness facilities, movie theaters, racetracks, etc.
■ Institutional
➢ 17 land uses, including schools, churches, military facilities, museums, libraries, etc.
■ Medical
➢ 4 land uses—hospital, nursing home, clinic, and animal hospital
■ Office
➢ 11 land uses, including general office, medical offices, government offices, post
office, etc.
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ITE Trip generation manual
Land use types
■Retail
➢ 43 land uses, including various supply stores, convenience
stores, supermarkets, sporting goods stores, apparel stores,
pet stores, etc.
■Services
➢ 24 land uses, including restaurants, fast food, coffee
shops, gas stations, banks, etc.
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ITE Trip generation manual
Sample Data Page in Trip Generation Manual
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ITE Trip generation manual
Sample Data Page in Trip Generation Manual
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ITE Trip generation manual
Sample Data Page in Trip Generation Manual
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ITE Trip generation manual
Generated trips estimation process
■The process including:
1. Choosing land use
2. Choosing analysis period
3. Choosing independent variable
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ITE Trip generation manual
Generated trips estimation process
The general guidelines for use of the ITE manual are as follows:
1. Use equation first
➢ if the model statistical fit defined through the R2 is
greater than 0.75.
2. Use average rate,
➢ If standard deviation is less than 1.1 times the average
rate.
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ITE Trip generation manual
Example-1
Estimate the number of trips (T) entering
and exiting a university with 20,000
student ?
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ITE Trip generation manual
Example-1 Solution
1. Check the regression model
❖ R2=0.95 > 0.75 , thus use the model
2. Use the regression model
❖ T = 1.38 (x) +2108.83
– T =Vehicle Trip Ends
– X =No. of students
❖ T = 1.38 (20,000) +2108.83
❖ T= 29,708.83
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ITE Trip generation manual
Example-1 Solution
4. Directional Distribution: 50% entering, 50%
exiting
➢ % of entering trips
❖ 0.5* 29708 = 14,854
➢ % of exiting trips
❖ 0.5* 29708 = 14,854
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ITE Trip generation manual
Example-2
A 500-acre site is being developed to support 400
single-family detached houses and a swimming
pool with a clubhouse.
Estimate the number of trips (T) exiting the
subdivision during a typical am peak hour.
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ITE Trip generation manual
Example-2
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ITE Trip generation manual
Example-2
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ITE Trip generation manual
Example -3
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ITE Trip generation manual
Example -3
Rate: it refers to the number
of trips per day per activity
center.
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ITE Trip generation manual
Example -3
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ITE Trip generation manual
Example -4
■A commercial center in the downtown
contains several retail establishments and
light industries. Employed at the center are
220 retail and 650 non-retail workers.
Determine the number of trips per day
attracted to this zone.
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ITE Trip generation manual
Example -4 Rate: it refers to the number of trips per day
per employee type (retail and nonretail)
Estimated trips is function with employee type (retail and nonretail)
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ITE Trip generation manual
Example -4
Employed at the
center are 650
non-retail and 220
retail and workers.
Estimated HBW trips
650 × 1.7 + 220 × 1.7 = 1479 𝑡𝑟𝑖𝑝/day
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ITE Trip generation manual
Example -4
Employed at the
center are 650
non-retail and 220
retail and workers.
Estimated HBO trips
650 × 2 + 220 × 5 = 2400 𝑡𝑟𝑖𝑝/day
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ITE Trip generation manual
Example -4
Employed at the
center are 650
non-retail and 220
retail and workers.
Estimated NHB trips
650 × 1 + 220 × 3 = 1310 𝑡𝑟𝑖𝑝/day
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ITE Trip generation manual
Example -4
Estimated Total trips
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Trip generation
The main methods for estimating generated trips
Trip generation estimation
ITE Trip Generation
Manual,
Rates Based on
Activity Units
Develop a customized
equation
Cross-Classification (e.g., planning model)
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Develop a customized equation (e.g., planning model)
Case studies in Jordan
■ Alkuime, H, 2015. Trip Attraction Model For Universities In Jordan.
Master of Science in Civil Engineering in Transportation, Jordan
University of Science and Technology
■ Abu-Ameerh, S. 2007. Trip attraction model for hospitals in Amman.
Master of Science in Civil Engineering in Transportation. Jordan
University, Jordan.
■ Al-Jabari, O. 2009. Trip Attraction Model For Fast Food Restaurants In
Amman, Master of Science in Civil Engineering in Transportation,
Jordan University, Jordan
■ Al-Nawaiseh, H 2010. Trip Attraction Model For Private Schools In
Amman, Master of Science in Civil Engineering in Transportation,
Jordan University, Jordan
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Develop a customized equation (e.g., planning model)
Trip Attraction Model For Universities In Jordan
Regression models were developed to predict vehicles trips within the day or
peak hours, based on different characteristics of universities such as:
1. Number of students.
2. Number of administrative staff.
3. Number of academic staff.
4. Gross floor area of universities.
5. Number of studying rooms and labs.
6. Number of courses
7. Number of lectures
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Develop a customized equation (e.g., planning model)
Trip Attraction Model For Universities In Jordan
University Name Type Province
Jordan University of Science & Tech. (JUST) Public Irbid
Al al-Bayt University (AABU) Public Al-Mafraq
Jadara University Private Irbid
Irbid National University Private Irbid
Philadelphia Private University Private Amman
Zarqa University (ZU) Private Zarqa
Jarash University Private Jarash
Ajloun National Private Univ. private Ajloun
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Develop a customized equation (e.g., planning model)
Trip Attraction Model For Universities In Jordan
6000
R² = 0.976
Average Attracted Daily Trips
5000
4000
3000
2000
1000
0
0 5000 10000 15000 20000 25000 30000
Number of Students
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Develop a customized equation (e.g., planning model)
Trip Attraction Model For Universities In Jordan
Developed models R2
0.968
LN(VTD) = 0.472+0.297*LN(GFA)+0.3256*LN(NS)+0.360*LN(NL)
LN(VAD) = -0.205+0.296*LN(GFA)+ 0.325*LN(NS)+0.36*LN(NL) 0.967
0.985
LN(VTH) = -2.643+0.985*LN(NS)
LN(VAH) = -2.380+0.926*LN(NS) 0.976
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Trip generation
The main methods for estimating generated trips
Trip generation estimation
ITE Trip Generation
Manual,
Rates Based on
Activity Units
Develop a customized
equation
Cross-Classification (e.g., planning model)
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Cross – Classification
■ Cross-Classification: a technique developed by FHWA
to determine the number of trips that begin or end at
the home
➢Home based trips
■ The two variables most commonly used are average
income and auto ownership.
➢Other variables that could be considered are household size and
stage in the household life cycle
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Cross – Classification
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Cross – Classification
Example - 5 : Developing Trip Generation Curves from Household Data
A travel survey
produced the data
shown in Table
12.1. Twenty
households were
interviewed
Based on the data provided,
develop a set of curves
showing the number of trips
per household versus income
and auto Ownership?
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Cross – Classification
Example - 5 : Developing Trip Generation Curves from Household Data
Step 1 : Produce a matrix that shows the number and percentage of
households as a function of auto ownership and income grouping
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Cross – Classification
Example - 5 : Developing Trip Generation Curves from Household Data
Step 2 : Produce a matrix shows Average Trips per Household versus
Income and Car Ownership
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Cross – Classification
Example - 5 : Developing Trip Generation Curves from Household Data
Step 2: Plot Average Trips per Household versus Income and Car
Ownership
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Cross – Classification
Example - 5 : Developing Trip Generation Curves from Household Data
SOURCE: Modified from Computer Programs
for Urban Transportation Planning, U.S.
Department of Transportation, Washington, D.C.,
April 1977
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Cross – Classification
Example - 5 : Developing Trip Generation Curves from Household Data
SOURCE: Modified from Computer Programs
for Urban Transportation Planning, U.S.
Department of Transportation, Washington, D.C.,
April 1977
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Cross – Classification
Example - 5 : Developing Trip Generation Curves from Household Data
Addition work (Step 3): Determine the percentage of trips by each trip purpose for
each income category
* Data is not
given in the
example
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Cross – Classification
Example -6 : Computing Trips Generated in a Suburban Zone
Consider a zone that is located in a suburban area of a
city. The population and income data for the zone are
as follows.
➢Number of dwelling units: 60
➢Average income per dwelling unit: $44,000
Determine the number of trips per day generated in
this zone for each trip purpose, assuming that the
characteristics depicted in Figures 12.2 through 12.5
apply in this situation.
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Cross – Classification
Example -6 : Computing Trips Generated in a Suburban Zone
Figure 12.2 Figure 12.3
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Cross – Classification
Example -6 : Computing Trips Generated in a Suburban Zone
Figure 12.5
Figure 12.4
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Cross – Classification
Example -6 : Computing Trips Generated in a Suburban Zone
Step 1 : Determine the percentage of households in each economic category
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Cross – Classification
Example -6 : Computing Trips Generated in a Suburban Zone
Step 2 : Determine the number of trips per household per day for each income–auto ownership category.
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Cross – Classification
Example -6 : Computing Trips Generated in a Suburban Zone
Step 3: Determine the number of trips per household per day for each income–auto ownership category
58% of medium-income families
own one auto per household.
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Cross – Classification
Example -6 : Computing Trips Generated in a Suburban Zone
Step 4: Calculate the total number of trips per day generated in the zone
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Cross – Classification
Example -6 : Computing Trips Generated in a Suburban Zone
Step 4: Calculate the total number of trips per day generated in the zone
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Cross – Classification
Example -6 : Computing Trips Generated in a Suburban Zone
Step 5 : Determine the percentage of trips by trip purpose
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Trip types
■ Trip production
➢All the trips of
home based
➢The origin of the
non home-based
trips
■ Trip attraction
➢Trips do not
classify as trip
production
Image source: https://www.civil.iitb.ac.in/~vmtom/1100_LnTse/203_lnTse/plain/
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Trip generation
■Attracted (Ta) trips
➢Ta is function with land use
■Produced (Tp) trips?
➢Tp is function with population, income, household size,
number of household size, vehicle owned
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Trip generation
Aloc, D. S. & Amar, J. A. N. A. C. Trip Generation Modeling of Lipa City Trip Generation Modelling of Lipa City. (2014). doi:10.13140/2.1.2171.7126
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Example
Study area
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Example
5
1
4
3
Zoning
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Example
5
Attracted trips
1
The land use within
2
Zone 1 are
➢ 120 office space
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3
➢500 factory
➢50 educational seats
➢100 shopping center
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Example
How many trips are attracted (Ta) and produced to a zone 1?
Attracted (Ta) trips
Land use ( By Number of units Trip rates
(column 2 X column
survey) ( By survey) (manual)
3)
office space 120 1.18 472
factory 500 0.43 64.5
educational
50 1.2 108
seats
shopping center 100 2.1 630
Total attracted trips (Ta) 626
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Example
5
Attracted trips 1
6 Ta= 650
Ta= 620
Ta= 1200
Ta= 560
4
3
Ta= 420
Ta= 350
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Example
5
Produced Trips
1
The land use within
2
Zone 1 are
➢ Number of dwelling units: 60 3
4
➢Average income per dwelling
unit: $44,000
❖ See example - 6 : Computing
Trips Generated in a Suburban
Zone
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Example
5
Produced trips (Tp) 1
6 Ta= 650
Ta= 620
Tp= 250 Ta= 1200 Tp= 550
Tp= 800
2
Ta= 560
Tp= 400
4
3
Tp= 320
Ta= 420
Ta= 350
Tp= 150
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Balancing Trip Productions and Attraction
■ A likely result of the trip generation process is that the
number of trip productions may not be equal to the
number of trip attractions
■ Trip productions, which are based on census data, are
considered to be more accurate than trip attractions
– Trip attractions are usually modified so that they are equal to trip
productions
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Balancing Trip Productions and Attraction
Example - 6 : Balancing the Home-based trips
■ The trip generation
process between zone 1
through zone 3 has
produced
➢600 HBW productions
trips
➢800 HBW attraction
trips
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Balancing Trip Productions and Attraction
Example - 6 : Balancing the Home-based trips
𝑇𝑜𝑡𝑎𝑙 𝑈𝑛𝑏𝑎𝑙𝑎𝑛𝑐𝑒𝑑 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝑡𝑟𝑖𝑝𝑠
■ 𝑇𝑟𝑖𝑝 𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑖𝑜𝑛 𝑓𝑎𝑐𝑡𝑜𝑟 =
𝑇𝑜𝑡𝑎𝑙 𝑈𝑛𝑏𝑎𝑙𝑎𝑛𝑐𝑒𝑑 𝑎𝑡𝑡𝑟𝑎𝑐𝑡𝑖𝑜𝑛𝑠 𝑡𝑟𝑖𝑝𝑠
600
➢𝑇𝑟𝑖𝑝 𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑖𝑜𝑛 𝑓𝑎𝑐𝑡𝑜𝑟 = 800 = 0.75
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Balancing Trip Productions and Attraction
Example - 6 : Balancing the Home-based trips
■ 𝐵𝑎𝑙𝑎𝑛𝑐𝑒𝑑 𝑎𝑡𝑡𝑟𝑎𝑐𝑡𝑖𝑜𝑛 𝑡𝑟𝑖𝑝𝑠 = 𝑇𝑟𝑖𝑝 𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑖𝑜𝑛 𝑓𝑎𝑐𝑡𝑜𝑟 × 𝑈𝑛𝑏𝑎𝑙𝑎𝑛𝑐𝑒𝑑 𝑎𝑡𝑡𝑟𝑎𝑐𝑡𝑖𝑜𝑛𝑠 𝑡𝑟𝑖𝑝𝑠
=(240*0.75)= 180
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Example
Unbalanced Trip 5
Productions and 1
Attractions Ta= 620
6 Ta= 650
Tp= 250 Ta= 1200 Tp= 550
Unbalanced trips
Zone ID Tp= 800
Production Attraction 2
1 250 620 Ta= 560
2 320 560 Tp= 400
3 150 350 4
3
4 400 420 Tp= 320
5 550 650 Ta= 420
Ta= 350
6 800 1200
Tp= 150
Total trips 2470 3800
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Example
Balancing Trip Productions and Attractions
𝑇𝑜𝑡𝑎𝑙 𝑈𝑛𝑏𝑎𝑙𝑎𝑛𝑐𝑒𝑑 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝑡𝑟𝑖𝑝𝑠
■ 𝑇𝑟𝑖𝑝 𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑖𝑜𝑛 𝑓𝑎𝑐𝑡𝑜𝑟 = 𝑇𝑜𝑡𝑎𝑙 𝑈𝑛𝑏𝑎𝑙𝑎𝑛𝑐𝑒𝑑 𝑎𝑡𝑡𝑟𝑎𝑐𝑡𝑖𝑜𝑛𝑠 𝑡𝑟𝑖𝑝𝑠
𝟐𝟒𝟕𝟎
➢𝑇𝑟𝑖𝑝 𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑖𝑜𝑛 𝑓𝑎𝑐𝑡𝑜𝑟 = 𝟑𝟖𝟎𝟎 = 0.65
Unbalanced trips Balanced trips
Zone ID
Production Attraction Production Attraction
1 250 620 250 403
2 320 560 320 364
3 150 350 150 228
4 400 420 400 273
5 550 650 550 423
6 800 1200 800 780
Total trip 2470 3800 2470 2470
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Example
Balanced Trip 5
Productions and 1
Attractions Ta= 620
6 Ta= 650
Tp= 250 Ta= 1200 Tp= 550
Balanced trips
Zone ID Tp= 800
Production Attraction 2
1 250 403 Ta= 560
2 320 364 Tp= 400
3 150 228 4
3
4 400 273 Tp= 320
5 550 423 Ta= 420
Ta= 350
6 800 780
Tp= 150
Total trips 2470 2470
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