UNIT 1

Urban Transportation Problem Travel Demand:

Urban issues
 No Integration between Land use and Transport while preparing Urban Transport Plan.
 No Quantification of Community Impact for Transport Proposals
 No consideration of issues like Non-motorized Trips, Mobility for Weaker Section and
Physically Handicapped,
 Non-consideration of equity issues
 Conventional techniques are used for developing Transport Model

Travel characteristics
(Travel characteristics-i.e. Trip and traveler profile)
There are certain special characteristics of travel demand that require recognition for planning
and design purposes, and these are discussed below:

Spatial and Temporal Variations

The total magnitude of travel demand alone is not sufficient for detailed planning and
management purposes. The spatial and temporal distributions of travel also are important
items of information to be considered in determining supply strategies. The peaking of travel at
certain time periods requires a level of transportation supply that is not needed at other times.
However, due to the nature of supply which cannot be adjusted easily, large investments have
to be made to provide roadway or transit service capacities to accommodate peak period
travel, and this capacity is not utilized efficiently at other times. An imbalance in the directional
distribution of travel also creates similar inefficiencies.

The spatial orientation of trips has important influence on supply requirements and costs. A few
typical spatial distribution patterns of trips in urban areas are listed below:

 Travel along dense corridors, which are usually radial connecting suburbs to central
business district (CBD).
 Diffused travel pattern caused by urban sprawl.
 Suburb to suburb or circumferential travel.
 Travel within large activity centers in CBD and suburbs.

Different modes of transportation may be needed to serve these different travel patterns. For
example, fixed-route public transit service usually is efficient for concentrated travel along a
dense corridor, but it is not ideally suited to serve a diffused travel pattern in a cost-effective
manner.
Choice of domicile and work place, lifestyles and different travel needs of individuals and
families make the comprehension of trip making characteristics of a large metro area very
complex. These complexities may be illustrated through trips made by a typical suburban US
household on a given weekday (Figure 1). Assume that this household has four members,
including two kids who go to a grade school, and two cars. It can be seen that there are at least
11 trips made by this household at different times of day. Most of the trips are auto trips and
two trips are taken in the “walk” mode. Travel demand modeling attempts to capture such
spatial and temporal variations in travel at an aggregate level, such as a zone, in which a
number of households, businesses and offices exist.

Classification of Travel by Trip Purpose and Market Segments

In addition to the spatial and temporal characteristics of travel demand, there are several other
aspects of travel demand which must be recognized. ‘Trip purposes’ such as work, shopping,
and social-recreation; and trip maker’s characteristics such as income and car ownership, are
important factors influencing the elasticity of demand reflecting its sensitivity with respect to
travel time and cost. For example, ‘work’ trips may be more likely to use public transit for a
given level of service than trips of other trip purposes.
For a metropolitan study, it is useful to classify travel according to spatial orientation and trip
purpose as shown in Figure 2. The concept of “market segmentation” is applicable to the
classification of travel based on trip purpose, trip makers’ characteristics, and spatial-temporal
concentration. This concept is used in the field of ‘marketing’ for developing different types of
consumer products targeted to match different tastes and preferences of potential
users/buyers of these products. The concept of market segmentation is applicable to public
transportation planning. A single type of transit service is not suitable for all transit market
segments. For example, express buses may be needed for a commuter market segment.
Taxicabs serve a different market segment. NCHRP Report 212 by Woodruff, et al. examined
this subject in depth.

Evolution of planning Process

The planning process:

 Links transportation goals to the goals of land use, cultural preservation, social, economic,
environmental, and quality of life for the area covered by the plan;
 Uses data to examine current transportation operations and identify future transportation
needs;

 Helps planners and Tribal governments make well-informed decisions on how to spend money
set aside for transportation projects;
 Involves Tribal communities, Federal government agencies, State and local governments,
metropolitan and regional planning organizations, special interest groups, and others; and

 Results in workable strategies to achieve transportation investment goals over both the long
term (20 years or more) and the short term (three to five years).

Supply and demand -Systems approach

The concept of demand and supply are fundamental to economic theory and is widely applied
in the field to transport economics. In the area of travel demand and the associated supply of
transport infrastructure, the notions of demand and supply could be applied. However, we
must be aware of the fact that the transport demand is a derived demand, and not a need in
itself. That is, people travel not for the sake of travel, but to practice in activities in different
locations
The concept of equilibrium is central to the supply demand analysis. It is a normal practice to
plot the supply and demand curve as a function of cost and the intersection is then plotted in
the equilibrium point as shown below

The demand for travel T is a function of cost C is easy to conceive. The classical approach
defines the supply function as giving the quantity T which would be produced, given a market
price C. Since transport demand is a derived demand, and the benefit of transportation after on
the nonmonetary terms (time in particular), the supply function takes the form in which C is the
unit cost associated with meeting a demand T. Thus, the supply function encapsulates response
of the transport system to a given level of demand. In other words, supply function will answer
the question what will be the level of service of the system, if the estimated demand is loaded
to the system. The most common supply function is the link travel time function which relates
the link volume and travel time.

Travel demand:

 Travel demand is expressed as the number of persons or vehicles per unit time that can be expected
to travel on a given segment of a transportation system under a set of given land-use, socioeconomic,
and environmental conditions.
 Forecasts of travel demand are used to establish the vehicular volume on future or modified
transportation system alternatives.

Trades Analysis
This approach to demand estimation is based on the extrapolation of past trends.

Travel Demand Forecasting:

Trends and Issues While there are other methods used to estimate travel demand in urban
areas, travel demand forecasting and modeling remain important tools in the analysis of
transportation plans, projects, and policies. Modeling results are useful to those making transportation
decisions (and analysts assisting in the decision-making process) in system and facility design and
operations and to those developing transportation policy. NCHRP Report 365 (Martin and McGuckin,
1998) provides a brief history of travel demand forecasting through its publication year of 1998; notably,
the evolution of the use of models from the evaluation of long-range plans and major transportation
investments to a variety of ongoing, everyday transportation planning analyses. Since the publication of
NCHRP Report 365, several areas have experienced rapid advances in travel modeling:
 The four-step modeling process has seen a number of enhancements. These include the more
widespread incorporation of time-of-day modeling into what had been a process for modeling
entire average weekdays; common use of supplementary model steps, such as vehicle
availability models; the inclusion of non-motorized travel in models; and enhancements to
 procedures for the four main model components (e.g., the use of logic destination choice
models for trip distribution).
 Data collection techniques have advanced, particularly in the use of new technology such as
global positioning systems (GPS) as well as improvements to procedures for performing
household travel and transit rider surveys and traffic counts.
 A new generation of travel demand modeling software has been developed, which not only
takes advantage of modern computing environments but also includes, to various degrees,
integration with geographic information systems (GIS).
 There has been an increased use of integrated land use transportation models, in contrast to the
use of static land use allocation models.
 Tour- and activity-based modeling has been introduced and implemented.
 Increasingly, travel demand models have been more directly integrated with traffic simulation
models. Most travel demand modeling software vendors have developed traffic simulation
packages.

Estimation of Travel Demand

In urban transportation planning process is to estimate the amount of traffic expected in the
future. Future travel is determined by forecasting future land use in terms of the economic
activity and population that the land use in each traffic analysis zones (TAZ) will produce.

With the land-use forecasts established in terms of number of jobs, residents, auto ownership,
income, and so forth, the traffic that this land use will add to the highway and transit facility
can be determined.

This is carried out in a four-step process that includes the determination of

1. The number of trips generation.
2. The origin and destination of trips/trip distribution.
3. The mode of transportation used by each trip /modal split(for example, auto, bus, rail),
4. The route taken by each trip/network assignment.

Overall planning process

 Transportation planning is the process of defining future policies, goals, investments,
and designs to prepare for future needs to move people and goods to destinations.

 The urban transportation planning process involves two separate tasks. The first is to
determine the project cost, and the second is to estimate the amount of traffic expected
in the future.

 Urban transportation planning involves the evaluation and selection of highway or

transportation facilities to serve present and future land uses. For example, the
construction of a new shopping center, airport, or convention center will require
additional transportation services. Also, new residential development, office space, and
industrial parks will generate additional traffic, requiring the creation or expansion of
roads and transit services.
  Transportation planning is more than listing highway and transit projects. It requires
developing strategies for operating, managing, maintaining, and financing the area’s
transportation system to achieve the community’s long-term transportation goals.

 Urban transportation planning is concerned with two separate time horizons.

1. The first is a short-term emphasis intended to select projects that can be
implemented within a one- to three-year period. These projects are designed to
provide better management of existing facilities by making them as efficient as
possible.
Short-term projects involve programs such as traffic signal timing to improve flow,
car and van pooling to reduce congestion, park-and-ride fringe parking lots to
increase transit ridership, and transit improvement.
2. The second time horizon deals with the long-range transportation needs of an area
and identifies the projects to be constructed over a 20-year period.
Long-term projects involve programs such as adding new highway elements,
additional bus lines or freeway lanes, rapid transit systems and extensions, or
access roads to airports or shopping malls.

Urban Transportation planning

As listed in the figure presented before, a brief outline related to various steps of planning is
presented in the following paragraphs.

Organization
These are the organizations responsible for the planning and implementation of the plans in
different areas.
– State Metropolitan Planning Organizations, like MSRDC, MMRDA, MAHADA, CIDCO etc.
– To look after the planning and making sure to achieve the goals and objectives reflecting
current community values.

Prospectus
These organizations should prepare plans that are clear in vision and clearly indicate the
responsibilities of different coordinating agencies so that there is no overlap of works and
interference.
– Establishes a multiyear framework for the planning process.
– Defines planning procedures, important issues, responsibilities and elements of planning.

Unified planning work progress

– Describe all anticipated urban transportation and transportation‐related planning activities
– Documents works to be performed

Transportation plan
The transportation plan includes planning at two levels:
a) Long‐range plan
b) Transportation System management Plan

Long Range Element

Includes
– Provision of facilities, major changes in existing facilities and long‐range policy actions.
– Identification of Planning tools for analysis of alternatives
– Evaluation of alternatives (Travel Demand Forecasting used as a planning tool for evaluation)
– Decision making for selecting most promising alternatives

Transportation System Management Element

Caters to short range transportation needs and makes the existing system efficient. Basic
strategies to increase efficiency are:
– Efficient use of existing road space
– reducing vehicle use in congested areas
– improving transit service
– improving internal management efficiency

Long term Urban Transportation planning

 Long Term Strategy plan which examines the traffic implications of alternative land use
options and recommends the best pattern of staging development
 Consider and evaluate different strategies for urban development to determine the
optimal urban transport system with maximum benefits to the community at least
investment
 To work out a rational balance between residential and work place so that journey to
work trip is contained
  To work out a financially feasible transport system that is compatible to environment
and with preferences to the community where the option of Public Transport can also
be examined and developed.

Short term Urban Transportation planning

Design and Implement Transport projects that becomes an integral parts of long term transport
plan.
 Improve the existing situation by optimizing the transport system with least cost
through development of TSM.
 Control the movement of people and goods on the urban transport network in safe
and efficient manner.
 No. of measures adopted for preparation of Short Term Transport Plan
Traffic Engineering Techniques
Lorry Routes
Traffic Restraint
Parking Control
Bus Priority
Public Transport Pricing and Marketing
Pedestrian Scheme

Steps in short term transportation planning

Demand function

 Necessary understand the where to invest in new facilities and what type of facilities to
invest
 Two interrelated elements need to be considered
 Overall regional traffic growth/decline
 Potential traffic diversions
 Travel demand modeling aims to establish the spatial distribution of travel explicitly by means of
an appropriate system of zones
Independent Variables

Travel attributes Travel attributes such as time, cost, frequency, and comfort

Assumptions in demand Estimation

 The flow of persons per hour traveling from an origin to a destination ~demand! decreases in
relation to increases in travel time and cost of that journey on the road network;
 The routes of travel selected from each origin to each destination have equal travel times and
costs, and no unused route has a lower travel time and cost; and
 Route travel times and costs reflect total vehicle flows on the links of the road network,
resulting from these origin-destination-route choices.
Sequential Approaches / Modal
The travel demand is modeled in sequential steps of trip generation, trip distribution, modal
split and assignment.

Simultaneous Approaches / Modal

–If two or more steps in the sequential approach are combined then it results in a simultaneous
model.
– Simultaneous demand models are also called as direct demand models – Examples
• Combined modal split and distribution model
• Intercity travel demand model
Aggregate Techniques/Modal
– The demand model that uses summaries of data is an aggregate model
– The traditional four stage urban travel demand model is an aggregate travel demand model
as it uses zonal summaries or aggregate data

Disaggregate Techniques/Modal
– The demand model that uses the data on individual decision making unit as it is and explains
the behavior of the decision making unit when confronted with alternatives is a disaggregate
model
 Unit 2
Data Collection and Inventories

Inventories and surveys are made to determine traffic volumes, land uses, origins and destinations of
travelers, population, employment, and economic activity. Inventories are made of existing transportation
facilities, both highway and transit/transportation. Capacity, speed, travel time, and traffic volume are
determined. The information gathered is summarized by geographic areas called traffic analysis zones
(TAZ).
The size of the TAZ will depend on the nature of the transportation study, and it is important that the
number of zones be adequate for the type of problem being investigated. Often, census tracts or census
enumeration districts are used as traffic zones because population data are easily available by this
geographic designation.

Collection of data
The data collection phase provides information about the city and its people that will serve as the basis
for developing travel demand estimates. The data include information about
 Economic activity ----- employment, sales volume, income, etc.
 Land use ----- type, intensity
 Travel characteristics ----trip and traveler profile, and
 Transportation facilities ----capacity, travel speed, etc.
This phase may involve surveys and can be based on previously collected data.

Overview

The four-stage modeling, an important tool for forecasting future demand and performance of
a transportation system, was developed for evaluating large-scale infrastructure projects.
Therefore, the four-stage modeling is less suitable for the management and control of existing
software. Since these models are applied to large systems, they require information about
travelers of the area influenced by the system. Here the data requirement is very high, and may
take years for the data collection, data analysis, and model development. In addition,
meticulous planning and systematic approach are needed for accurate data collection and
processing.

There are three important aspects of data collection namely; Survey design, Household data
collection, and data analysis.

Organization of surveys and Analysis

Survey design
Designing the data collection survey for the transportation projects is not easy. It requires
considerable experience, skill, and a sound understanding of the study area. It is also important
to know the purpose of the study and details of the modeling approaches, since data
requirement is influenced by these. Further, many practical considerations like availability of
time and money also has a strong bearing on the survey design.
In this section, we will discuss the basic information required from a data collection, defining
the study area, dividing the area into zones, and transport network characteristics.

Information needed
Typical information required from the data collection can be grouped into four categories,
enumerated as below.
1. Socio-economic data: Information regarding the socio-economic characteristics of the
study area. Important ones include income, vehicle ownership, family size, etc. This
information is essential in building trip generation and modal split models.
2. Travel surveys: Origin-destination travel survey at households and traffic data from
cordon lines and screen lines (defined later). Former data include the number of trips
made by each member of the household, the direction of travel, destination, the cost of
the travel, etc. The latter include the traffic flow, speed, and travel time measurements.
These data will be used primarily for the calibration of the models, especially the trip
distribution models.

Figure 6:1: zoning of a study area

3. Land use inventory: This includes data on the housing density at residential zones,
establishments at commercial and industrial zones. This data is especially useful for trip
generation models.
4. Network data: This includes data on the transport network and existing inventories.
Transport network data includes road network, traffic signals, junctions etc. The service
inventories include data on public and private transport networks. These particulars are
useful for the model calibration, especially for the assignment models.
Urban area
1. Population not less than 5,000.
2. Non-agricultural workers not less than 75% of the total workers.
3. Population density not less than 400 per sq. km.
Towns with population of 0.1 million and above are termed as cities.
Transportation Survey
The first stage in the formulation of a transportation plan is to collect data on all factors are
Likely to influence travel pattern. The work involves a number of surveys so as to have:
1. An inventory of existing travel pattern.
2. An inventory of existing transports facilities.
3. An inventory of existing land use and economic activities.
Study area
Transportation planning can be at the national level, regional level or at the urban level.
For planning at the urban level, the study area should embrace the whole conurbation
containing the existing and potential continuously built-up areas of the city.
The imaginary line representing the boundary of the study area is termed as the ‘external
cordon’. The area inside the external cordon line determines the travel pattern to a large extent
and as such is surveyed in great detail.
Selection of External Cordon Line
The selection of the external cordon line for urban transportation planning should be done
carefully with due to consideration to the following factors:
1. The external cordon line should circumscribe all areas, which are already built up, and
those areas, which are considered likely to be developed during the planning period.
2. The external cordon line should contain all areas of systematic daily life of the people
oriented towards the city center and should in effect be the commuter shed.
3. The external cordon line should -be continuous and uniform in its courses so that
movements cross it only once. The line should intersect roads where it is safe and
convenient for carrying out traffic survey.
4. The external cordon line should be compatible with the previous studies of the areas
studies planned for the future.
Zoning
The defined study area is sub-divided into smaller areas called zones or traffic zones.
 The purpose of such a subdivision is to facilitate the spatial quantification of land use
and economic factors, which influence travel pattern. Subdivision into zones further
helps in geographically associating the origins and destinations of travel.
 Zones within the study area are called internal zones and those outside the study area
are called external zones.
 In large study projects, it is convenient to divide the study area into sectors, which are
sub divided into zones. Zones can themselves be sub divided into sub- zones depending
upon the type of land use.
 A convenient system of coding of the zones will be useful for the study. One such system
is to divide the study area into 9 sectors. The central sector CBD is designated 0, and the
remaining eight are designated from 1 to 8 in clockwise manner. The prefix 9 is reserved
for the external zones. Each sector is subdivided into 10 zones bearing numbers from 0
to 9.
It would be helpful, if the following points are kept in view when dividing the area into
Zones:
1. The zones should have a homogenous land use so as to reflect accurately the associated
trip making behavior.
2. Anticipated change in land use should be considered when sub- dividing the study area
into zones.
3. It would be advantages, if the subdivision follows closely that adopted by other bodies(
e.g. census department) for data collection. This will facilitate correlation of data.
4. The zones should not too large to cause considerable errors in data. At the sometime,
they should not be too small either to cause difficulty in handling and analyzing the
data.As a general guide, a population of 1000-3000 may be the optimum for a small
 area, and a population of 5000- 10000 may be the optimum for large urban areas. In
residential areas, the zones may accommodate roughly 1000 households.
5. The zones should preferably have regular geometric form for easily determining the
centroid, which represent the origin and destination of travel.
6. The sectors should represent the catchment of trips generated on a primary route.
7. Zones should be compatible with screen lines and cordon lines.
8. Zone boundaries should preferably be watersheds of trip making.
9. Natural or physical barriers such as canals, rivers, etc. can form convenient zone
boundaries.
10. In addition to the external cordon lines, there may be a number of internal cordon lines
arranged as concentric rings to check the accuracy of survey data.

Screen lines
Running through the study area are also established to check the accuracy of data collected
from home- interview survey. Screen lines can be convenitally located along physical or natural
barriers having a few crossing points. Examples of such barriers are river, railway lines, canals,
etc.

Types of Movements
The basic movements for which survey data are required are:
1. Internal to internal.
2. External to internal.
3. Internal to external.
4. External to external.
For large urban areas, the internal to internal travel is heavy whereas for small areas having a
small population (say less than 5000) the internal to internal travel is relatively less. Most
details of internal to internal travel can be obtained by home interview survey.
The details of internal- external, external internal and external- external travels can be studied
by cordon surveys.

Types of source of data

Data Collection:
The data can be collected:
1. At home.
2. During the trip end.
3. At the destination of the trip.
When collected at home, the data can be wide ranging and can over all the trips made during a
given period. The data collected during the trip is necessary of limited scope since the
procedure yields data only on the particular trip intercepted.
At the destination end, the direct interview types of surveys provide data on demand for
parking facilities and or the trip ends at major traffic attraction centers such as factories, offices
and commercial establishments.

The following are the surveys that are usually carried out:
1. Home- interview survey.
2. Commercial vehicles surveys.
3. Intermediate public transport surveys.
4. Public transport surveys.
5. Road –side – interview surveys.
6. Post- card- questioner surveys.
7. Registration- number surveys.
8. Tag- on- vehicle surveys.

The information to be collected from home-interview survey can be broadly classified in two
Groups:
1. Household information.
2. Journey or trip data.

The household information needs to contain data with regard to:

a- Size of household.
b- Age of all the numbers of the households.
c- Sex.
d- Structure of households.
e- Employee.
f- Occupation.
g- Place of work.
h- No. of vehicles owned.
i- Household income.

Journey data will contain information all trips made during the previous 24hr. with regard to:
a. Origin and destination of trip.
b. Purpose of trip.
c. Modes of travel.
d. Time at start of trip.
e. Time at finish of trip.

Inventory of Transport Facilities

The inventory of existing transport facilities should be undertaken to identify the deficiencies in
the present system and the extent to which they need to be improved. The inventory consists
of:
- Inventory of streets forming the transport network. Link width length, no. of lanes. Nodes
complete geometric of intersection.
- Traffic volume composition peak and off peak.
- Studies on travel time by different modes.
- Inventory of public transport buses.
- Inventory of rail transport facilities.
- Parking inventory
- Accident data.

Inventory of Land Use and economic Activities

1. Inventory of Land Use Since travel characteristics are closely related to the land use
pattern, it is of utmost important that an accurate inventory of land-use be prepared.
Data on intensity of usage of land for different purposes, such as residential, industrial,
 commercial, recreational, open space, etc. in each of the traffic zones are to be
collected from concerned departments/ organization.
2. Inventory of Economic Activities Aggregate data on demographic and socioeconomic
activities should be collected other sources to include the following:
- Population of the planning area and various zones.
- Age, sex, and composition of the family.
- Employment statistics.
- Housing statistics.
- Income.
- Vehicle ownership.

Road side Interviews

These provide trips not registered in a household survey, especially external-internal trips. This
involves asking questions to a sample of drivers and passengers of vehicles crossing a particular
location. Unlike household survey, the respondent will be asked with few questions like origin,
destination, and trip purpose. Other information like age, sex, and income can also be added,
but it should be noted that at road-side, drivers will not be willing to spend much time for
survey.

Home interview surveys

Home-interview survey is one of the most reliable type of surveys for collection of origin and
destination data. The survey is essentially intended to yield data on the travel pattern of the
residents of the household and the general characteristics of the household influencing trip
making. The information on the travel pattern includes number of trips made, their origin and
destination, purpose of trip, travel mode, time of departure from origin and time of arrival at
destination and so on. The information on household characteristics includes type of dwelling
unit, number of residents, age, sex, race, vehicle ownership, number of drivers, family income
and so on. Based on these data it is possible to relate the amount of travel to household and
zonal characteristics and develop equations for trip generation rates.
It is impractical and unnecessary to interview all the residents of the study area. Since travel
patterns tend to be uniform in a particular zone. The size of the sample is usually determined
on the basis of the population of the study area. And the standards given by the Bureau of
Public Roads as shown in below table.
Standard Practice now is instead to calculate the sample size which will achieve the desired
precision for key indicators at the required level of confidence. One such equation is given by
Traffic Appraisal manual.

n = required number of households in an area of interest

E = accuracy level
P =Proportion of households in the area with attributes of interest.
The usual procedure is for an interviewer to call on a household on a scheduled data and to
leave a copy of the home interview questionnaire. This questionnaire is broadly divided into :
a) General household characteristics – number of residents, vehicles owned, income, dwelling
type.
b) Characteristics about family members – occupation, sex, age.
c) Individual travel information – trip origin and destination, purpose, land – use, travel time
and transport mode.

Commercial vehicle surveys

A similarly styled survey of non-residential land uses could be designed to collect information
on goods movements, but transport resources are rarely allocated to such an ambitious project.
Instead, urban freight flows are usually measured indirectly from commercial vehicle survey.
Commercial vehicle surveys are conducted to obtain information on journeys made by all
commercial vehicles based within the study area. The addresses of the vehicle operators are
obtained and they are contacted. Forms are issued to drivers with a request that they record
the particulars of all the trips they would make.

Sampling Techniques

Statistics is a tool for converting data into information:

• But where then does data come from? How is it gathered? How do we ensure its
accurate? Is the data reliable? Is it representative of the population from which it was
drawn? This chapter explores some of these issues.
Sampling Plans…
We will focus our attention on these three methods:
• Simple Random Sampling,
• Stratified Random Sampling, and
• Cluster Sampling
Simple Random Sampling…
A simple random sample is a sample selected in such a way that every possible sample
of the same size is equally likely to be chosen.
 Drawing three names from a hat containing all the names of the students in the class is
an example of a simple random sample: any group of three names is as equally likely as picking
any other group of three names.
• Random sample of 100 cokes bottles today at the coke plant.
• Random sample of 50 pine trees in a 1000 acre forest.
• Random sample of 5 deer in a national forest.
Stratified Random Sampling…
• A stratified random sample is obtained by separating the population into mutually
exclusive sets, or strata, and then drawing simple random samples from each stratum.
Cluster Sampling…
• A cluster sample is a simple random sample of groups or clusters of elements (vs. a
simple random sample of individual objects).
• This method is useful when it is difficult or costly to develop a complete list of the
population members or when the population elements are widely dispersed
geographically. Used more in the “old days”.
• Cluster sampling may increase sampling error due to similarities among cluster
members.
Sampling Errors..
Two major types of error can arise when a sample of observations is taken from a population:
• Sampling error refers to differences between the sample and the population that exist
only because of the observations that happened to be selected for the sample. Random
and we have no control over.
Non-Sampling Errors…
Non-sampling errors are more serious and are due to mistakes made in the acquisition of data
or due to the sample observations being selected improperly. Most likely caused be poor
planning, sloppy work, act of the Goddess of Statistics, etc.
1) Errors in data acquisition,
2) Non response errors and Selection bias.

Expansion factors
Sample expansion the second step in the data preparation is to amplify the survey data in order to
represent the total population of the zone. This is done with the help of expansion factor which is
defined as the ratio of the total number of household addressed in the population to that of the
surveyed. A simple expansion factor Fi for the zone i could be of the following form.

where a is the total number of household in the original population list, b is the total number of
addresses selected as the original sample, and d is the number of samples where no response was
obtained.

Accuracy Checks
SURVEY DATA CHECKS: The data collected for transportation planning by any survey can be
check out by following methods:
Accuracy Check
 Data accuracy is the foundation dimension of data quality.
  Accuracy
 Timeliness
 Relevance
 Completeness
 Understood by users
 Trusted by users etc.
Screen Line Checks
 Traffic counts taken at selected screen lines are useful for comparing model generated
travel pattern with actual volumes of traffic crossing screen line and making
adjustments.
 This check is useful for calibration and validation of model
 Check is carried out data collected by home interview survey
 It is line separating study area.
Consistency check
 Data constitutes summarizes the validity, accuracy, usability and integrity of related
data applications.
 This confirming the data reliability.
Cordon line checks
 Cordon line is the boundary of study area
 The data collected from internal to external, external to internal and external to
external
 Cordon line points can be use to compare the trips calculated and observed.
 This check is very useful for data adjustment for study area

Use of secondary sources

Population and economic data

Once a zone system for the study area is established, population and socioeconomic forecasts prepared
at a regional or statewide level are used. These are allocated to the study area, and then the totals are
distributed to each zone. This process can be accomplished by using either a ratio technique or small-
area land-use allocation models.
The population and economic data usually will be furnished by the agencies responsible for planning and
economic development, whereas providing travel and transportation data is the responsibility of the traffic
engineer. For this reason, the data required to describe travel characteristics and the transportation
system are described as follows.
The transportation facility inventories provide the basis for establishing the networks that will be studied to
determine present and future traffic flows. Data needs can include the following items:
• Public streets and highways
—Rights of way
—Roadway and shoulder widths
—Locations of curbed sections
—Locations of structures such as bridges, overpasses, underpasses, and major culverts
—Overhead structure clearances
—Railroad crossings
—Location of critical curves or grades
—Identification of routes by governmental unit having maintenance jurisdiction
—Functional classification
—Street lighting
• Land use and zoning controls
• Traffic generators
—Schools
—Parks
—Stadiums
—Shopping centers
—Office complexes
• Laws, ordinances, and regulations
• Traffic control devices
—Traffic signs
—Signals
—Pavement markings
• Transit system
—Routes by street
—Locations and lengths of stops and bus layover spaces
—Location of off-street terminals
—Change of mode facilities
• Parking facilities
• Traffic volumes
• Travel times
• Intersection and roadway capacities
In many instances, the data will already have been collected and are available in the files of city, county,
or state offices. In other instances, some data may be more essential than others. A careful evaluation of
the data needs should be undertaken prior to the study.

—Income—–Employment—vehicle ownership.

Socio-economic data: Information regarding the socio-economic characteristics of the study area.
Important ones include income, vehicle ownership, family size, etc. This information is essential in
building trip generation and modal split models.
Household characteristics This section includes a set of questions designed to obtain socioeconomic
information about the household. Relevant questions are:number of members in the house, no.of
employed people, number of unemployed people, age and sex of the members in the house etc.,
number of two-wheelers in the house, number of cycles, number of cars in the house etc., house
ownership and family income.
 Unit 3
Trip generation and distribution:

UTPS approach

The first phase of the transportation planning process deals with surveys, data collection and
inventory. The next phase is the analysis of the data so collected and building models to
describe the mathematical relationship that can be discerned in the trip‐making behaviour. The
analysis and model building phase starts with the step commonly known as Trip Generation.

Trip Generation:
Trip generation is a general term used in the transportation planning process to calculate the
number of trip ends in a given area. The objective of the trip generation stage is to understand
the reasons behind the trip making behaviour and to produce mathematical relationships to
synthesize the trip‐making pattern on the basis of observed trips, land‐use data, transportation
system characteristics, trip maker characteristics and household characteristics.

Trip Types:
Trips can be defined based on the nature of movement between the zones and across the
cordon lines. They are also categorized based on the location of trip ends. Categorizations in
use are:
• Intra‐zonal trips
• Inter‐zonal trips
• Through trips
• Home‐based trips
• Non‐home based trips

Intra‐zonal, Inter‐zonal and through trips are already defined while discussing transportation
surveys. The first activity in travel‐demand forecasting is to identify the various trip types
important to a particular transport‐planning study. The trip types studied in a particular area
depend on the types of transport‐planning issues to be resolved. The first level of trip
classification used normally is a broad grouping into home‐based and non‐home‐based trips.

Home‐ based trips: Home‐based trips are those trips having one end of the trip (either origin or
destination) at the home of the persons making the trip.

Non‐home‐based trips: Non‐home based trips are those trips having neither end at the home
of the person making the trip. The trip ends are classified into productions and attractions.

Trip Production: A production is the home end of any trip that has one end at the home (i.e. of
a homebase trip), or is the origin of a trip with neither end home based (i.e. of a non – home
based trip).
Trip attraction models

: An attraction is the non‐home end of a home‐based trip, and is the destination of a trip with
neither end home‐based (i.e. of a non‐home‐based trip).

This points towards one aspect related to trips generated, i.e. the total number of trips
produced in any area should be equal to the total number of trips attracted in that area. If trips
produced are Pi in zone ‘i’ and trips attracted are Aj towards zone ‘j’, then

Trip purpose:
Trips are made with different purposes and a classification of trips by purpose is necessary. Trip
classifications that have been used in the major transport‐planning studies for home based trips
are:
 Work
 Education
 Social
 Recreational and sports
 Shopping
 Others

Trip Mode:
Trip mode is the type of vehicle used by a person for traveling between an origin and
destination.

Trip Time:
Trip time is the time taken while moving between a set of origin and destination.

Trip Length:
Trip length is the distance traveled between a set of origin and destination.
Factors influencing trip production
Households may be characterized in many ways, but a large number of trip‐production studies
have shown that the following variables are the most important characteristics with respect to
the major trip types such as work and shopping trips:
1. The number of workers in a household, and
2. The household income or some proxy of income, such as the number of cars per household.
Various factors that create an effect on the production of the trips are discussed below:

Population and its characteristics: The size of the population in an area obviously has an effect
on the total number of trips supposed to be produced from that area. This further can be
looked at in terms of:

a. Number of households in an area: More are the households more will be the trips.
b. Size and composition of the household: Bigger is the size of the household, it is
possible that more trips will be produced by that household. The composition defines
the members of a household involved in different activities that necessitates travel. This
may be related to work, education, shopping, recreation, etc and thus produces
different types of trips from a single household.
c. Population density: This is one factor that is discussed in detail with respect to the use
of different modes or travel. In general, if the density is more, more will be the trips
from that area.

Household Income: Disposable household income will define the possibility of trip making
by the members of a household.

Factors influencing attraction rates

1. Land use activities: Land use activities in an area define the type of the trips that will be
attracted towards that area. If the area is homogeneous in nature then the trips made to that
area will be of same nature but heterogeneous activities will attract different types of trips.
2. Employment opportunities: The employment potentiality of any area is defined by the type
of activity undertaken in that area. The industrial, shopping unit or an office establishment
directly governs the trip attraction rate.
3. Floor area allotted for the activities: Another factor to which the trip attraction rate can be
related is the floor space in the premises of industries, shops and offices. This will allow the
estimation of different trips which can be made to an area.

Factors affecting trip making patterns

1. Study Area characteristics: Certain characteristics of the study area affect the trip making
pattern in that area. These characteristics are discussed below.
a. Location of an activity: The distance of the activity zone from the household is an important
determinant of the amount of travel that people might like to do if needed. The further the
activity centre, the less the number of trips are likely to be to that activity.
b. Accessibility of an activity: Accessibility to an activity is governed by the type of network
facilities available in that area and the affordability of those facilities to the masses.
Commercial trip rates Bypass Trips

Trip generation analysis

Methods of trip generation

There are three main methods of generating trips from the study area. They are:
• Expansion factor method
• Least square regression analysis method
• Category analysis method

Expansion factor method

The expansion factor method uses past growth rates as a means of predicting future trips from
the estimated present trips. Such correlations can be developed with:
• Growth of traffic
• Rise in population
• Agricultural and industrial production
• Total mileage of roads
• Fuel consumption estimates
• Per capita income
• National GDP, etc.
Due to fast changing scenario and the complexities involved in the estimation of future values,
the use of this method is confined to short term forecasting in small urban or rural areas.

Category Analysis
Category Analysis or cross‐classification technique is simply a technique for estimation of the
trip production characteristics of households which have been sorted in a number of separate
categories according to a set of properties that characterize the household. The method was
developed by Wotton and Pick and has been used in some transportation studies in U.K. A
multidimensional matrix defines the categories, each dimension in the matrix representing one
independent variable. The independent variables themselves are classified into a definite
number of discrete class intervals. Category analysis may also be used for estimating trip
attractions.

Assumptions
The technique is based on the following assumptions:
(i) The household is the fundamental unit in the trip generation process, and most journeys
begin or end in response to the requirements of the family.
(ii) The trips generated by the household depend upon the characteristics of that household
and its location relative to its required facilities or activities.
(iii) Households with one set of characteristics generate different rates of trips from households
with other set of characteristics.
(iv) Trip generation rates are stable over a time so long as factors external to the household are
the same as when the trips were first measured.

Trip attraction
Trip attraction rates can be made by analyzing the urban activities that attract trips.
Trips are attracted to various locations, depending on the character of, location, and
amount of activities taking place in a zone.
Three tools are used for this end too, but obviously types of independent variables used
are different.

Zonal Models

A Sample Zonal Attraction Model

The sample model estimate relative attractiveness by regressing factored values of sample trips
(aggregated to the zone level) on relevant zonal characteristics. The choice of explanatory
variables is constrained in a manner similar to trip productions models - model significance,
policy sensitivity, and forecastability. These models are summarized in Box 1.

Zonal Based Trip Generation Models

Zonal Based Models use zonal averages to estimate trip making.
One problem with this approach is that zonal averages may be deceptive depending on the
distribution of a given parameter.
For example, two zones with the same average income could have very different income
distribution and presumable different travel performances.
These models are referred to as AGGREGATE Models

House hold models

Household Based Trip Generation Models

Household Based Models use the household as the unit for estimating trip making.
The underlying assumption of household based models is the assumption that household with
the same characteristics (for example, same number of adults and cars, and same income) tend
to have the same travel tendencies
As we have discussed, this assumption might or might not be true depending on the
characteristic of the land use in which the household is located
Based on this assumption average trip making is determined for each household category
These models are called DISAGGREGATE Models

A Sample Household Trip Production Model

A category model was estimated for household trips by purpose from the trip data and
demographic characteristics of the 200 sample households. Category variables are selected
based on ability to significantly discriminate trip rates between categories, general policy
sensitivity, and the availability of future data. Category models are less restrictive than
regression models but require that the joint distribution of population variables be forecast. A
variety of methods have been used with iterative proportional fitting perhaps the most direct.
The role of activity system forecasts is clear, as is the need for quality forecasts of automobile
ownership since this variable is typically most highly correlated with total trips per household.
The resulting estimated trip rates are displayed in Table 4 (to simplify presentation, rates from
Martin and McGuckin (1998) are utilized). Aggregation proceeds directly since the model is
linear. Once the joint distribution of households is known for the base or forecast year, the cell
counts are multiplied by the estimated trip rates to obtain the total number of trips per zone.

Trip Rate Analysis

Trip rate is estimated on characteristics of the trip generators with in the zone. Production
rates are determined using the characteristics of the residential land uses and attraction rates
using the characteristics of the nonresidential land uses

Trip Distribution

Introduction
Once estimate of the trips generated, i.e. produced from and attracted to the various zones, are
made the next step is to determine the direction of travel of these trips. The number of trips
produced in any zone of the study area has to be apportioned to the zones to which these trips
are attracted. Say, there are pi trips produced from zone i by trip maker’s category ‘q’ and aj is
the number of trip ends attracted to zone j, then the number of trips between zone ‘i’ and zone
‘j’ (i.e. tq
i‐j) would be estimated using trip distribution technique. This can be represented in a matrix
form as given below:

The horizontal axis of the trip matrix shown represents the zones of attractions (i.e.
destinations) and the vertical axis represents the zones of production (i.e. origin) numbered
from 1 to n. The total of any individual row, i, represents the total number of trips produced in
zone i (pi) and the total of any individual column, j, represents the number of trips attracted to
zone j (aj). Therefore,

Based on the future land uses in the zones and the area, the future trips going to be produced
or attracted from or to any zone are computed. These may be denoted as Pi and Aj
respectively.
Methods of trip distribution
There are two categories of trip distribution methods, namely,
(i) Growth factor methods
(ii) Synthetic methods
Growth factor methods have been used in earlier studies and have yielded now to the more
rational synthetic models. Both of these categories are further divided as follows:

Growth factor methods:

(i) Uniform factor method
(ii) Average factor method
(iii) Detroit Method
(iv) Fratar method
(v) Furness method
(vi) Time function iteration method

Synthetic methods:
(i) Gravity Model
(ii) Tanner Model
(iii) Intervening opportunities model
(iv) Competing opportunities model

Growth Factor Methods The growth factor methods are based on the assumption that the
present travel patterns can be projected to the design year in the future by using expansion
factors. This can be represented by the
general formula as given below:
Ti‐j = ti‐j x F
Where, Ti‐j = number of trips from zone I to zone j in design year (future) ti‐j = number of trips
from zone I to zone j in observed base year F = growth factor

Uniform growth factor method

This is the oldest of the growth factor methods and considers the growth rate for the whole
area. A single growth factor, F, for the entire study area is calculated by dividing the future
estimated number of trip ends for the design year by the trip ends in the base year. The future
trips between zone i and j, Ti‐j, are then calculated by applying the uniform growth factor F to
the base year trips between zones i and j, as,
Ti‐j = ti‐j x F
Let us take one example: An O‐D trip matrix for three zones in an area is given below. The
future trips produced and attracted from or towards these zones are 360, 1260 and 3120
respectively. It is required to distribute the future trips among these zones.

Uniform growth factor F= 4740/1300 = 3.646

Multiplying the cells in the matrix by the uniform growth factor, the following matrix results:

Uniform growth factor (New) = 4740/4740 = 1.0 STOP

It can be noted that all the trips have been distributed among the zones. As the new growth
factor is equal to 1.0, the iteration can be stopped here. But the total number of trips
generated from each zone, as calculated, do not tally with the known future trip values for
those zones. This is because of the assumption of a uniform growth rate for all the zones. The
method, therefore, suffers from the following
disadvantages:
(i) The assumption of a uniform growth rate for the entire study is not correct,
because each zone will have its own growth rate and the rate of growth of
traffic movement between any two zones will be different.
(ii) The method under‐estimates movements where present day development is
limited and over‐estimates movements where present day development is
intensive.
(iii) If the present trip movement between any two zones is zero, the future trip
movement also becomes zero as per this method. This may rarely be the
case in reality.

Average growth factor method

In this method, an average growth factor between the two zones related to two trip ends is
calculated based on the growth factors of the zones at both the ends of the trip. This factor
thus represents theaverage growth associated both with the origin and the destination zones.
The trip values in the given matrix are then multiplied with the computed growth factor values
between the origin and destination
ti‐j= present trips from zone i to zone j
Fi = Pi/pi = growth factor related to trips produced from zone i
Fj = Aj/aj = growth factor for trips attracted to zone j
If after the iteration the trips produced or attracted (calculated) agrees with the trips produced
or attracted (for future) for each zone, the process is stopped. But if it is not so, then new
growth factors for each zone are computed and a new iteration is started. The process
continues till the zonal growth factors come out to be either 1.0 or very near to 1.0.
As the growth factors of zones are not either 1.0 or near to 1.0, the next interaction starts till
the desired accuracy is reached.
The average factor method has the same disadvantages of the uniform factor method. The
multiplying factor has no real significance and is only a convenient tool to balance the
movements. There is no explanation of the movement between zones and the factors causing
the movement. It has the additional disadvantage that a large number of iterations are
required. As in the case of the uniform factor method, if ti‐j is zero, Ti‐j also becomes zero.
Because of these drawbacks the method is rarely used except for updating existing table and
for quick results.

Detroit method
This method is the further improvement on average factor method and takes into account the
growth factor for zones and average growth factor for the entire study area. The trips are
computed as:

The trips for zone connectivity 2‐1, 3‐1 and 3‐2 will remain same as the reverse flow. The new
O‐D trip matrix would be as given below:
New growth factor for the whole study area F1 = 4740/4491 = 1.055
As the growth factors of zones are not either 1.0 or near to 1.0, the next interaction starts till
the desired accuracy is reached.

Fratar method
According to this method, the total trips for each zone are distributed to the inter‐zonal
movements, as a first approximation, according to the relative attractiveness of each
movement. This relative attractiveness is considered in the form of Locational factor (L). The
trips distributed can be computed as follows:

Where Fi =growth factor for zone ‘i’

Fj =growth factor for zone ‘j’
Li = Location factor for zone ‘i’ =
Lj = Location factor for zone j =
Now let us take another example to illustrate the actual procedure.
The process is repeated to obtain a second iteration using values of new growth factors and
inter‐zonal movements obtained from the first iteration till the growth factors for different
production and attraction zones become equal to or nearly equal to 1.0.
The procedure is laborious except for simple problems, but can be conveniently tackled by a
computer. It has the same drawbacks as observed in other growth factor methods. It is unable
to forecast trips for those areas which were predominantly under‐developed during the base
year. It does not take into account the effect of changes in accessibility for various zones of the
study area.

Furness method
The method requires the estimates of future traffic originating and terminating at each zone,
thus yielding origin growth factor and destination growth factors for each zone. The traffic
movements are made to agree alternately with the future traffic originating in each zone and
the estimated future traffic terminating in each zone, until both these conditions are roughly
satisfied. Thus,

Time Function iteration models

This method assumes that the trip distance is influenced by the journey time and row and
column totals are nothing but trip ends. The method starts with converting the given O‐D trip
matrix into a unity matrix and finding the growth factors for different production zones for the
given future trips produced from these zones. The procedure after this remains the same as
that of Furness method. Final trip matrix is then converted into travel time index matrix, which
provides the effect of travel time between the zones. The method can be understood from the
example taken below.
Example: The trip frequency observed in actual for the given O‐D trip matrix is as below:
Iteration‐3: Scale trip O‐D matrix using attraction growth factors.
The procedure will continue till equilibrium is achieved. Now, the final O‐D trip matrix is used to
compute the travel time index matrix. This can be computed as follows:
Cell value of travel time index matrix = ti‐j from original matrix / Ti‐j from final matrix
Say, the travel time index matrix is:
The values obtained as such can be termed as friction factor or deterrent factor. This can be
represented as:
Friction factor = f (1/Travel time)
This can help in identifying settlement pattern.
Trips computed for different travel times are as follows:

The trip frequency data, observed and calculated, is presented as a frequency polygon. If the
two data sets do not match then new O‐D adjustment factors are calculated and further these
are used to compute the final origin and destination trip values.

Gravity Models
The gravity model gets its name from the fact that it is conceptually based on Newton’s law of
gravitation. Accordingly it is heuristically derived for synthesizing trip interchanges. It states
that the trip interchange between zone ‘i’ and zone ‘j’ is directly proportional to the product of
the population of the two zones and is inversely proportional to a function of spatial separation
of zones under consideration.
This can be represented by the relationship given as below:

Where Ti‐j = Trip interchanges between zones ‘I’ and ‘j’

Pi = Population in zone ‘i’
Pj = Population in zone ‘j’
f(di‐j) = Travel time factor function or friction factor, in terms of travel distance, travel time,
travel cost, etc. This may take different functional forms. One such form is (di‐j)α
K = A constant, usually independent of ‘i’ and ‘j’
α = An exponential constant, whose value is usually found to lie between 1 and 3
This is known as unconstrained Gravity model. It shows following shortcomings:
a. If population is doubled, the trips produced will quadruple.
b. No constraint is taken in the analysis.
c. Aggregation of zone is considered but characteristics of the zone are not considered.
Modification was made to the above unconstrained Gravity Model by introducing the
employment opportunities of the destination zone to make it synonymous with trip attractions.
So the modified unconstrained Gravity Model is represented as:

Further, the constrained gravity models were proposed so as to take into consideration the
effect of production or attraction zones. Accordingly, these were termed as Production
constrained Gravity model or attraction constrained gravity model. The formulation is given
below:

Calibration Process:
1. Assume values for Bj and α, say Bj = 1.0 and α = 2. Now calculate Ai.
2. Calculate Bj for calculated Ai values and α.
3. Re‐iterate till the new Ai and Bj values are very near to the Ai and Bj values of previous iteration.
4. Use final values of Ai and Bj with value of α to compute Ti,p and Tj,a.
5. Form an O‐D trip matrix.

Limitation:
The limitation of procedure described is it requires that two criteria be satisfied by a base year
calibration. These two criteria are: agreement between observed and simulated trip length
constraint equation .A principal difficulty wit this calibration procedure is that the travel time
factor function and associated trip length frequency distribution are assumed to be constant for
each zone of a study area.

Opportunity Models

Opportunity model are based on the statistical theory of probability as the theoretical
foundation. The concept has been pioneered by Schneider and developed by subsequent
studies. The two well known models are:
(i) The intervening opportunities models ;
(ii) The competing opportunities model.

Intervening opportunities model

The basic hypothesis of the intervening opportunity model can be represented by the general
formula as given below;

Where ti‐j= predicted number of trips between zone i and zone j

aj = total number of trip attractions at the destination zone j
vj = Number of intervening opportunities met upto the destination zone j
k = constant
It states that the number of trips from an origin zone to a destination zone is directly
proportional to the number of opportunities at the destination zone and inversely proportional
to the number of intervening opportunities. This is also known as ‘Stouffer Model’.

Schneider Modification
Modified hypothesis states that the probability that a trip will terminate in some destination
point is equal to the product of the probability that the destination met is acceptable and the
probability that an acceptable destination closer to the origin has not been found. This can be
formulated for a small destination zone ‘dv’ as:
Pr(dv) = [1 – Pr(v)] . l. dv
Where Pr(dv) = probability that a trip will terminate when dv destination opportunities are
considered
Pr(v) = Cumulative probability that a trip will terminate by the time ‘v’ possible destinations are
considered
v = Cumulative total of the destinations already considered
l = a constant probability of a destination being accepted it is considered
The integration of this will yield the following:
Pr(v) = [1 – ki . exp(‐l.v)]
Where ki = a constant for zone ‘i’, which ensures that all the trips produced at zone ‘i’ are
distributed
In the intervening opportunities model, it is assumed that the trip interchange between an
origin and a destination zone is equal to the total trips emanating from the origin zone
multiplied by the probability that each trip will find an acceptable terminal at the destination. It
is further assumed that the probability that a destination will be acceptable is determined by
two zonal characteristics, namely the size of the destination zone and the order in which it is
encountered as trips proceed from the origin. The probability function in above may then be
expressed as the difference between the probability that the trip origins at i will find a suitable
terminal in one of the destinations, ordered by closeness to i, up to and including j, and the
probability that they will find a suitable terminal in the destinations up to but excluding j. The
following equation represents mathematically this concept as:
 Unit 4
Mode choice and traffic assignment:

Mode choice

The choice of transport mode is probably one of the most important classic models in transport
planning. This is because of the key role played by public transport in policy making. Public
transport modes make use of road space more efficiently than private transport. Also they have
more social benefits like if more people begin to use public transport, there will be less
congestion on the roads and the accidents will be less. Again in public transport, we can travel
with low cost. In addition, the fuel is used more efficiently. Main characteristics of public
transport is that they will have some particular schedule, frequency etc.

On the other hand, private transport is highly flexible. It provides more comfortable and
convenient travel. It has better accessibility also. The issue of mode choice, therefore, is
probably the single most important element in transport planning and policy making. It affects
the general efficiency with which we can travel in urban areas. It is important then to develop
and use models which are sensitive to those travel attributes that influence individual choices
of mode.

Mode choice behavior

Mode choice is a key aspect of travel demand modeling. The choice of travel mode is a complicated
behavioral process and as such is a core focus in Travel Behavior

Factors influencing the choice of mode

The factors may be listed under three groups:

1. Characteristics of the trip maker : The following features are found to be important:
(a) car availability and/or ownership;
(b) possession of a driving license;
(c) household structure (young couple, couple with children, retired people etc.);
(d) income;
(e) decisions made elsewhere, for example the need to use a car at work, take children to
school, etc;
(f) residential density.

2. Characteristics of the journey: Mode choice is strongly inuenced by:

(a) The trip purpose; for example, the journey to work is normally easier to undertake by public
transport than other journeys because of its regularity and the adjustment possible in the long
run;
(b) Time of the day when the journey is undertaken.
(c) Late trips are more di_cult to accommodate by public transport.

3. Characteristics of the transport facility: There are two types of factors.One is quantitative
and the other is qualitative. Quantitative factors are:
(a) relative travel time: in-vehicle, waiting and walking times by each mode;
(b) relative monetary costs (fares, fuel and direct costs);
(c) availability and cost of parking
Qualitative factors which are less easy to measure are:
(a) comfort and convenience
(b) reliability and regularity
(c) protection, security
A good mode choice should include the most important of these factors.

Types of modal split models

Trip-end modal split models

Traditionally, the objective of transportation planning was to forecast the growth in demand for
car trips so that investment could be planned to meet the demand. When personal
characteristics were thought to be the most important determinants of mode choice, attempts
were made to apply modal-split models immediately after trip generation. Such a model is
called trip-end modal split model. In this way different characteristics of the person could be
preserved and used to estimate modal split. The modal split models of this time related the
choice of mode only to features like income, residential density and car ownership.
The advantage is that these models could be very accurate in the short run, if public transport is
available and there is little congestion. Limitation is that they are insensitive to policy decisions
example: Improving public transport, restricting parking etc. would have no effect on modal
split according to these trip-end models.

Trip-interchange modal split models

This is the post-distribution model; that is modal split is applied after the distribution stage. This
has the advantage that it is possible to include the characteristics of the journey and that of the
alternative modes available to undertake them. It is also possible to include policy decisions.
This is beneficial for long term modeling.

Aggregate and disaggregate models

Mode choice could be aggregate if they are based on zonal and inter-zonal information. They
can be called disaggregate if they are based on household or individual data.
Trip End Modal Split Model Trip Interchange Modal Split Model
Performs modal split after trip generation Performs modal split after trip distribution
Performed in medium and small sized cities Performed for large urban areas
Assumes that modal patronage is relatively Incorporates measures of relative service
insensitive to the service characteristics of characteristics of competing modes
transport modes
Determined based on socio‐economic Determined based on socio‐economic
characteristics of trip maker characteristics of trip maker
Emphasize on transit captives Emphasize on choice transit riders
E.g. Southeastern Wisconsin Transportation E.g. Toronto Model
Study
 LAND USE

TRIP GENERATION EQUATION

MODAL SPLIT MODEL TRIP DISTRIBUTION MODELS

TRIP ENDS BY MODE ORIGIN DESTINATION VOLUMES

TRIP DISTRIBUTION MODELS MODAL SPLIT MODEL

O-D VOLUMES BY MODE O-D VOLUMES BY MODE

Trip Assignment

Purpose of traffic assignment

The last phase of the four‐step sequential transportation demand forecasting process is
concerned with the trip maker’s choice of path between pairs of zones by a travel mode. This
results in vehicular flows on the multimodal transportation network. This step may be viewed
as the equilibrium model between the demand for travel, estimated earlier in the process, and
the supply of transportation in terms of the physical facilities. In the case of the various possible
mass transit modes, it includes the frequency of service being provided. Incidentally, this
conceptual framework of economic theory is applicable to earlier steps of the process as well
and has been so treated by many researchers.

Traffic assignment is the stage in the transport planning process wherein the trip interchanges
are allocated to different parts of the network forming the transportation system. In this stage:

 The route to be traveled is determined and

 The inter‐zonal flows are assigned to the selected routes.
 The traffic assignment to the network has following applications:
 To determine the deficiencies in the existing transportation system by assigning the
future trips to the existing system.
 To evaluate the effects of limited improvements and additions to the existing
transportation system by assigning estimated future trips to the improved network.
 To develop construction priorities by assigning estimated future trips for intermediate
years to the transportation system proposed for those years.
  To test alternative transportation system proposals by systematic and readily repeatable
procedures.
 To provide design hour traffic volumes on highway and turning movements at
junctions.Thus the assignment process is useful both to the transports planners and the
highway facility designers, to the former because of the need to evaluate how the
proposed transport system will work, and to the latter, for geometric design of
individual links and intersections.
The advent the modern digital computers has facilitated the growth of assignment techniques,
which involve computations too laborious for manual handling.

Transportation Networks
Transportation Network primarily consists of two elements:
• Links
• Nodes
Link is defined as connectivity between two nodes. The links may have traffic movements either
in both the directions or in one direction only. Sometimes the links on which direction of travel
is marked are also known as an Arc.
The nodes are the location in space which provides an opportunity to the vehicle to enter or
leave a system or facilitate the movements in different directions. The node from which an arc
is diverted is termed it’s A‐node and the node to which it is diverted is termed its B‐node. A
representative map of the network system is shown in the figure below.

The above network is represented in the form of links and nodes showing the direction of
movement of the traffic along the links along with the travel attribute value written on the side
of the link. The travel attribute values may be in terms of travel time, travel cost, travel
distance, etc. usually numbers are used to specify the nodes in the network from the point of
view of computational ease. One such network is represented in the figure below.

Connection Matrix:
A connection matrix defines the connectivity between different nodes available in a
transportation network. This helps in building a network in a systematic way. It has rows and
columns. Rows define the originating nodes and columns define the destination nodes. The
numbers 0, 1 and ‐1 denotes no flow along the link, flow along the link and reverse direction
flow along the link, respectively. A connection matrix is shown in the figure below.

Node‐Link Incidence Matrix

Node‐link incidence matrix defines the connectivity from a node to different nodes of the
network. This also uses numbers to represent the connectivity as done in the connection
matrix. This is shown in the figure below.

Network Assignment Methods

The network assignment methods are basically single‐path methods, which allocate trips on
one preferred path between each origin and destination pair. This technique is executed based
on three steps, in combination of what is listed initially as
requirement. These are:

• A driver route selection criteria

• A tree building technique
• Method of allocating trip interchanges between these routes

Driver route‐selection Criteria

Driver’s route‐selection is based on Wardrop’s principles. Wardrop has given two principles, as
follows:

System Optimization criteria:

‘The trip times on all the routes actually used are equal and less than those which would be
experienced by a single vehicle on any unused route’.
The system optimization is based on ‘marginal cost concept’ of economics.
Let us consider a link carrying traffic volume in vehicle/hr. As the traffic volume increases the
travel time on the link also increases. This increase per unit volume becomes more after certain
volume on the link. In terms of marginal travel time, the increase becomes exponential after
certain value of traffic volume. As can be seen from the figure, the increase in average travel
time per mile with increase in traffic volume from nil to 3000 veh/hr is from 5 min/mile to 15
min/mile, whereas, the increase in marginal travel time is from 5 min/mile to 105 min/mile
during the same change in volume. It indicates that the entry of one vehicle at a flow of 3000
veh/hr increases the travel time effect by 90 min/mile (=105 – 15 min/mile).

User Optimization criteria:

‘The average journey time of all motorists is a minimum which implies that the aggregate
vehicle hours spent in traveling is a minimum.’
This criterion indicates towards the ‘average cost concept’ of economics. It means that every
user tries to minimize the individual travel time. Therefore, the user will base their route choice
decision on the average travel time relationship and will select the route according to the
marginal travel time criteria. Reacting to marginal travel time criteria will minimize the total
travel time of all the vehicles using the system.

Route Builiding Algorithm

Next step after finalization and coding of network is the computation of minimum travel path
between the zones. This is already discussed in one of the previous lectures. Some of the
techniques or algorithms used for finding the minimum travel path tree are:
1. Network analysis
2. Moore’s tree building algorithm
3. Shortreed and Wilson’s Modified tree‐building algorithm
4. Dijkstra’s algorithm
5. D’Esopo’s algorithm
Route Builiding Algorithm:

Moore’s Algorithm
For each origin centroid, a label is assigned to each node in the network of the following form:
Node ‘j’ label = [i, d(j)]
Where,
i = the node nearest to zone ‘j’ which is on the travel time path back to the origin
d(j) = the minimum travel time from node ‘j’ back to the origin centroid
Initially, each node is assigned a d(j) magnitude which is very large, say, 999, with the exception of the
origin node where it is set to ‘0’. As the tree is built out from the origin, the following sum is formed for
each node
Node: Node ‘j’ sum = [d(i) + l(i,j)]
Where,
d(i) = travel time from the origin to node ‘i’ which has just been connected to the origin
l(i.j) = travel time along the link which connects node ‘j’ to node ‘i’
If the sum just formed is greater than the d(j) already recorded for node ‘j’, then the node is by‐paased.
If the sum is less than the d(j) existing, then the d(j) is replaced by the newly formed sum and ‘i’ is
changed in the label to reflect the new connecting link for j back to the origin.
This process is continued until all nodes have been reached.

Shortreed and wilson’s Method

(Moore’s modified method / computationally efficient method)

The improved efficiency results from the manner in which the node labels are stored and updated. This
algorithm uses three concepts which are known as the tree table, the link table and the list.

Tree Table: It shows the sequence of node that defines the minimum path from any particular centroid
back to the origin centroid.

Link Table: It defines all the links in the network in terms of other nodes at either end or the travel time
along the link.

List: A table in which all of the links emerging from a specified node are entered along the travel times
or the links.

Steps for minimum path tree:

1. Initialize the tree table with all the total times equal to 999 with the exception of the origin
node which is set equal to 0 and this activity is shown in table above.
2. Add to the list all nodes connected to the nodes just added to the tree table.
3. Test all entries in the list to determine if “Node to” + “Total time from origin” travel time is less
than the “Total travel time” in the tree table and if so enter it in the tree table.
4. Return to 2 and repeat until the list is empty.

Network coding:
 Ideally the network should be the smallest possible, in terms of links and nodes,
adequate for the purpose for which it is required.
 All redundant links and nodes should be removed beforehand. Dead end links apart
from centroid and gateway connectors should be removed.
  For some purposes, especially if minimum path costs only are required, the use of
spider network, in which groups of two or more links from the original network are
combined into a single link representing minimum cost paths between their end nodes,
may be appropriate, to reduce CPU time.
 Numbering the network nodes, including centroids, sequentially without gaps, as
suggested saves time in the initialization process and uses less computers core storage
 It may be assumed that the real network nodes start at node number Ncent+1 and
hence, at step 2(b)(ii), node k is entered into L and ensuring that paths do not pass
through centroids or gateways en route to other nodes.
 It is possible to avoid completely the need for tests to prevent centroids and gateways
from entering the loose ends table by separating their connector links from the real
network links.

Route Choice Behaviour

The most fundamental element of any traffic assignment is to select a criterion which explains
the choice by driver of one route between an origin-destination pair from among the number of
potential paths available.

All-or-Nothing assignment

All or Nothing assignment technique allocates the entire volume interchanging between pairs
of zones to the minimum path calculated on the basis of free‐flow link impedances. This is the
simplest technique and is based on the premise that the route followed by traffic is the one
having the least travel resistance. The resistance itself can be measured in terms of travel time,
distance, cost or a suitable combination of these parameters. The traffic flows are assigned to
the minimum path tree. The assignment algorithm loads the matrix ‘T’ to the shortest path tree
and produces flows VAB on links between node A and B. two basic variations of the algorithm
are given as follows:

Pair to Pair method

This is simplest but not necessarily results in the most efficient one. The steps are: Initialize all
VAB = 0, then for each pair (i, j):
1. Set B to destination ‘j’
2. If (A, B) is the back link of B then increment VAB by Tij i.e. VAB = VAB + Tij
3. Set B to A
4. If A = i, terminate (and process the next pair), otherwise return to step‐2.

Once‐Through method (Cascade method)

Allocates cumulative flows on minimum path trees using steps as follows:
1. Set all VA = 0 except for destination ‘j’ for which Vj = Tij.
2. Set B equal to the most distant node from ‘i’.
3. Increment VA by VB: VA = VA + VB (where A is back node of B).
4. Increment VAB by VB: VAB = VAB + VB
5. Set B equal to next most distant node; if B=I then origin has been reached; begin processing
the next origin; otherwise proceed with step‐3.
Capacity Restraint Techniques
Capacity restraint assignment is a process in which the travel resistance of a link is increased
according to a relation between the practical capacity of the link and the volumes assigned to
the link. This technique has been developed to overcome the inherent weakness of all‐or‐
nothing assignment technique which takes no account of the capacity of the system between a
pair of zones. The capacity restraint system, on the other hand, clearly restrains the number of
vehicles that can use any particular corridor and, in fact, the whole system, if the assigned
volumes are beyond the capacity of the network, and redistributes the traffic to realistic
alternative paths.
Because of the iterative nature of the calculations involved, the capacity restraint technique is
carried out entirely by an electronic computer. The procedure is similar to the all‐or‐nothing
assignment as far as the initial data input are concerned. The additional data that is fed is the
capacity of each link. The best paths are determined in the same way as in all‐or‐thing
technique by building the minimum path trees. Traffic is then assigned to the minimum path,
either fully or in stages, and as the assigned volume on each link approaches the capacity of the
link, the new set of travel time of the link is calculated. This results in a new network with a
different minimum path tree, differing significantly from the earlier minimum path tree. As a
consequence, assigning interzonal volumes to the new tree produces a new volume on each
link. This iterative process is
repeated until a satisfactory balance between volume and speed is achieved. Some of the
methods of capacity restraint are given below.

Smock Method
In this method, the all‐or‐nothing assignment is first worked out. In an iterative procedure, the
link travel ties are modified according to the function:

Where To = Original travel time or the travel time on a link when volume equals capacity
TA= adjusted travel time
V = assigned volume
C = computed link capacity
In the second iteration, the adjusted travel times TA are used to determine the minimum paths
or trees. The resulting link volumes are averaged and these are again used to calculate the
adjusted travel time for the next iteration.

WEYNE State Arterial method

This method is one of the earlier capacity restrained assignment methods. It is based on
assigning traffic to various routes between an origin‐destination pair such that the travel time
on these routes are equal and any route between the origin‐destination pair with zero flow will
have a larger travel time. Various steps of this method are as follows;
1. Construct a minimum travel path tree for all origin zones based on travel time computed
from average speeds on links, under typical flow conditions as existing.
2. Assign inter‐zonal volumes to minimum travel path tree on A‐O‐N basis.
3. Compute the link travel times as (ith iteration)

Where, RI = Average assigned volume (from previous iteration) / capacity of the link
Average assigned volume = (Vi‐1 + Vi‐2)/2
To = original travel time on the link
4. Go to step 1 and repeat upto step 3 until equilibrium is reached i.e. Ti / Ti+1 ≈ 1.0 The
limitations of this method are: variations in travel time are taken into consideration, and A‐O‐N
assignment technique is used for assigning the volume to the network.
Multiple Route Assignment
All road users may not be able to judge the minimum path for themselves. It may also happen
that all road users may not have the same criteria for judging the shortest route. These
limitations of the all‐or‐nothing approach are recognized in the multiple route assignment
technique. The method consists of assigning inter‐zonal flow to a series of routes, the
proportion of the total flow assigned to each being a function of the length of the route in
relation to the shortest route. In an interesting approach suggested by Burell, it is assumed that
a driver does not know the actual travel times, but that he associates with each link a supposed
time. This supposed time is drawn from link time. The driver is then assumed to select the route
which minimizes the sum of his supposed link times. Multiple route models have been found to
yield more accurate assignment than all‐or‐nothing assignments

User Equilibrium assignment (UE)

The user equilibrium assignment is based on Wardrop's first principle, which states that no
driver can unilaterally reduce his/her travel costs by shifting to another route. User Equilibrium
(UE) conditions can be written for a given O-D pair as:

where fk is the flow on path k, ck is the travel cost on path k, and u is the minimum cost.
Equation labelqueue2 can have two states.
1. If ck - u = 0, from equation 10.1 fk > 0. This means that all used paths will have same travel
time.
2. If ck - u > 0, then from equation 10.1 fk = 0.
This means that all unused paths will have travel time greater than the minimum cost path.
where fk is the flow on path k, ck is the travel cost on path k, and u is the minimum cost.

Assumptions in User Equilibrium Assignment

1. The user has perfect knowledge of the path cost.
2. Travel time on a given link is a function of the flow on that link only.
3. Travel time functions are positive and increasing.
The solution to the above equilibrium conditions given by the solution of an equivalent
nonlinear mathematical

Where k is the path, xa equilibrium flows in link a, ta travel time on link a, frs k flow on path k
connecting
O-D pair r-s, qrs trip rate between r and sand frs a;k is a definitional constraint and is given by
The equations above are simply flow conservation equations and non negativity constraints,
respectively. These constraints naturally hold the point that minimizes the objective function.
These equations state user equilibrium principle .The path connecting O-D pair can be divided
into two categories : those carrying the flow and those not carrying the flow on which the travel
time is greater than (or equal to)the minimum O-D travel time. If the flow pattern satistices
these equations no motorist can better o_ by unilaterally changing routes. All other routes have
either equal or heavy travel times. The user equilibrium criteria is thus met for every O-D pair.
The UE problem is convex because the link travel time functions are monotonically increasing
function, and the link travel time a particular link is independent of the flow and other links of
the networks. To solve such convex problem Frank Wolfe algorithm is useful.

Diversion Curves
One of the frequently used assignment techniques in initial years was the development and use
of diversion curves. These curves represent empirically derived relationships showing the
proportion of traffic that is likely to be diverted on a new facility (by pass, new expressway, new
arterial street etc.), once such a facility is constructed. The data collected on the pattern of
travel in the past years serve to build up such curves. Diversion curves can be constructed using
a variety of variables such as:
(i) Travel time saved
(ii) Distance saved
(iii) Travel time ratio
(iv) Distance ratio
(v) Travel time and distance saved
(vi) Distance and speed ratio
(vii) Travel cost ratio

Some of the diversion curve methods used in different parts of the world are discussed in the
following paragraphs.

Indiana US method (Brown method)

It compares two paths between same set of origin and destination. One is travel by personal car
and other is travel by transit. A person traveling by car has to cover distance equal to ‘c’,
whereas, person who travels by transit has to walk to the transit stop from home (say b1) and
then again walks from transit stop to the destination (say b2). The distance travel using transit
is say ‘a’.
Bureau of Public Roads method
A well‐known example of diversion curves using travel time ratio to determine the traffic
diverted to expressway is the Bureau of Public Roads Curve. The curve is “S” shaped. The
following formula has been fitted to this type of curves:
California Diversion Curve method
Another well known example using two variables, distance and travel time saved while moving
on a motorway, is the California Diversion Curve.
The following formula has been developed to fit the above curves.

Where P = percentage of vehicles moving on freeway

d = distance saved (in miles) on the freeway
t = time saved (in minutes) on the freeway

Diversion curve assignments have the drawback that only two alternative routes for each pair
of zones are considered. The technique is, therefore, eminently suitable for new bypasses, new
motorways and such new facilities, but is of limited use in a complex urban network. Diversion
curves reflect the travel resistance as measured by present day travel, but their use for future
travel when the pattern can undergo radical changes is doubtful.
 UNIT 5

Plan Preparation and Evaluation:

Travel Forecasts to Evaluate Alternative Improvements, Impacts of New Development on

Transportation Facilities. Master plans, Selection of corridor, , Corridor deficiency analysis,
Economic Impacts of transportation.

Corridor Identification
Initially, the study area was broadly defined to enable a regional assessment of alternative routes.
The study area was bounded by US 287 on the west, WCR 23 on the east, Harmony Road/WCR
74 on the north, and Crossroads Boulevard/WCR 62 on the south. The study area is contained in
both Larimer and Weld counties. While this broad study area was used to assess mobility and
potential effects at a regional level, the study’s logical termini were subsequently refined as
described in Section 2.3 of this report.
SH 392 provides regional access to the towns of Windsor, Severance, and Timnath, and the cities
of Loveland, Greeley, and Fort Collins. The SH 392 corridor crosses through both long-standing
rural farming communities and emerging suburban development. Some of the features present in
the study area include open spaces, trails, and golf courses. Commercial districts are developing
not only along the SH 392 corridor, but also at the I-25/US 34 Interchange, and along Crossroads
Boulevard. Other features of regional significance include the Cache La Poudre River
(commonly referred to as the Poudre River), the Fort Collins-Loveland Municipal Airport, the
Great Western Railway, the Union Pacific Railroad (UPRR), the Budweiser Events Center, and
Fossil Creek Reservoir.
Regional Transportation Plan Vision
The NFRMPO has identified several highways as being “Regionally Significant Corridors” and SH
392 is one of them. The NFRMPO defines a Regionally Significant Corridor as, “A multimodal,
regional system comprised of transportation corridors that connect communities by facilitating
the movement of people, goods, information, and services” (NFRMPO, 2003). Three criteria are
considered in the identification of regionally significant corridors: connectivity, functional
classification, and trip length.
The NFRMPO 2030 Transportation Plan contains the following Corridor Vision for SH 392:
“The vision of the SH 392 Urban corridor is primarily to increase mobility as well as maintain
system quality and improve safety. This corridor serves as a local facility, provides commuter
access, and makes east-west connections within the south Fort
Collins, Windsor, Lucerne and Severance areas. SH 392 serves as Main Street through Windsor.
Future travel modes to be planned for in the corridor include passenger vehicle, bus service,
truck freight, and bicycle and pedestrian facilities. Transportation Demand Management (TDM)
would likely be effective in this corridor. The transportation system
in the area serves towns, cities, and destinations within the corridor as well as destinations
outside of the corridor. The communities along the corridor value high levels of mobility,
transportation choices, connections to other areas, safety, and system preservation…Users of
this corridor want to support the movement of commuters,
Current Planning Efforts
Local jurisdictions are conducting the following transportation planning efforts related to the
SH 392 EOS.
 The NFRMPO is in the process of preparing the NFRMPO 2035 Transportation Plan. A
draft plan is anticipated to be submitted to CDOT in 2007.
  Larimer County is actively coordinating with Fort Collins on the Growth Management
Area (GMA) expansion and on the airport master planning efforts.

 Weld County is currently planning improvements to both WCR 7 and WCR 13.

 Fort Collins recently approved a proposal to expand the boundary of the City's GMA to
include the Fossil Creek Cooperative Planning Area, an area generally located
immediately west of I-25, both north and south of Carpenter Road. Once Larimer County
and the City sign a revised Intergovernmental Agreement (IGA), the City will formally
amend the City's Comprehensive Plan and the Structure Plan Map to depict the
amended GMA boundary. In addition to approving the GMA boundary amendment, the
City is pursuing annexation of the enclave and working with property owners (in
particular, those with properties near the I-25 interchange) regarding the appropriate
land uses on the current Structure Plan Map.

 Windsor is currently conducting a revitalization plan with a traffic and parking

component. Current recommendations include diverting truck traffic around the
downtown area, signalization of the Main Street/5th Street intersection, decreasing
intown posted travel speed, examining a roundabout at the east entrance to downtown
Windsor, and altering the on-street parking configuration to allow parallel parking.

 The Fort Collins/Loveland Municipal Airport updated their Master Plan in April 2006.

 On March 23, 2005, the Timnath Board of Trustees approved a resolution adopting the
North Area Comprehensive Plan Amendment for the Town. This Plan provides the
principles, goals, policies, and future land use plan. The intent of the Comprehensive
Plan is to preserve and enhance the Town’s identity, while still allowing for it to grow
and flourish in a manner that is acceptable to its residents.
Project Purpose
A primary goal of this study was to identify ROW needs for future transportation improvements
to meet travel demand in 2030. Based on the project need as described below, the project
purpose was to identify the mobility needs in 2030 and develop solutions that meet this need.
This study will guide future roadway improvement projects and ongoing development for the
SH 392 corridor. The primary goal of the transportation improvement was to ensure that
adequate provisions were made to the SH 392 corridor to meet regional transportation mobility
needs for 2030 and beyond.
In addition to the primary purpose of the EOS, other factors were also considered. These
include making provisions for transportation solutions that minimize effects to the natural,
cultural, and social environment of the surrounding communities, that provide for the safe
movement of people and goods, and that make full use of the EOS to identify other
opportunities to address the needs of SH 392.
The EOS allowed CDOT to examine various alternatives for meeting those mobility needs on this
major east-west connection between Loveland/Fort Collins and Windsor/Greeley. The study
incorporated a context sensitive solutions approach to balance mobility needs with potential
environmental and socio-economic effects.
The EOS served as a planning document that identified the ROW necessary for future
transportation needs resulting in a recommended “footprint” characterized by each alignment.
This footprint may be used by local planning agencies and CDOT to preserve a roadway corridor
for future improvement projects and guide ongoing development.

Two Stage Modal Split

Vandertol et al. have developed a simple two stage model which recognizes explicitly the
existence of both captive and of choice transit riders. The model first identifies both the
production and attraction trip ends of transit captives and choice transit riders separately. The
ntwo groups of trip makers are then distributed from origin to destinations. The choice transit
riders are then split between transit and car according to a choice modal split model, which
reflects the relative characteristics of the trip by transit and the trip by car.
In most cities, the transit captive is severely restricted in the choice of both household and
employment locations. Studies in a number of cities have shown that the trip ends of the transit
captives tend to be clustered in zones that are well served by public transport. The challenge is
to develop it to formulate a technique that uses information normally available in urban areas.
Zonal work trip productions disaggregated by the captive and the choice transit riders may be estimated
from: Pi q = hitpq
Where, Pi q = no of work trips produced in zone i by type q trip makers
hi = the no of households in zone i
tpq = work trip production rate for trip maker group q which is a function of economic status of a
zone and the average no of employees per household.
The work trips attracted to each zone j by trip maker type q may be estimated from
ajq= [prcq] [rct] [etj]
Where, ajq= the no of work trips of type q trip maker attracted to zone j
[prcq] = A row vector of the probability of the trip maker type q being in occupation in category type c.
[rct] = A c*t matrix of the probabilities of an occupation category type c within an industry type t.
[etj] = a t*j matrix of the no of jobs within each industry type t in each zone j.

Linear Regression Analysis

Linear regression analysis is a well‐known statistical technique for fitting mathematical relationships
between dependent and independent variables. This technique has been exploited fruitfully in a number
of transportation planning studies carried out so far and has become a very powerful tool in the hands
of the transportation planner. In the case of trip generation equations, the dependent variables are the
various measurable factors that influence trip generation. The general from of the equation obtained is :
Yp = a + b1X1 + b2X2 + b3X3 ,…..+ bn Xn
Where Yp = number of trips for specified purpose p (dependent variable)
X1, X2, X3…… Xn = independent variables relating to various trip influencing factors
b1, b2, b3…, bn = multiplicative coefficients of the respective independent variables X1, X2, X3,…Xn
obtained by linear regression analysis, representing the relative influence of the
variables on the trips generated
a = Disturbance term, which is a constant, and represent that portion of the value of Yp not
explained by the independent variables and is additive in nature

Multiple Regression Analysis

The majority of trip-generation studies performed have used multiple regression analysis
to develop the prediction equations for the trips generated by various types of land use.
Most of these regression equations have been developed using a stepwise regression
analysis computer program. Stepwise regression –analysis programs allow the analyst to
develop and test a large number of potential regression equations using various
combinations and transformations of both the dependent and independent variables. The
planner may then select the most appropriate prediction equation using certain statistical
criteria. In formulating and testing various regression equations, the analyst must have a
thorough understanding of the theoretical basis of the regression analysis.

Toronto Modal
Trip interchange modal split models allocate trips between public transport and private transport after
trip distribution stage. The split between two modes is assumed to be a function of the following
variables between each pair of zones:
1. Measures of system characteristics, like
• Relative Travel Time (RTT)
• Relative Travel Cost (RTC)
• Relative Travel Service (RTS)
2. Economic status of the Trip maker, like
a. Low‐income group
b. Medium income group
c. High income group

Relative Travel Time

(RTT) by competing modes can be computed as:
RTT=(x1+ x2+ x3+ x4+ x5)/( x6+ x7+ x8)
This represents the door to door travel time to train to that of automobiles
X1 = Time spent in transit vehicle
X2 =Transfer time between transit vehicles
X3 = Time spent in waiting for a transit vehicle
X4 = Walking time to a transit vehicle
X5 = Walking time from transit vehicle
X6 = Auto driving time
X7 = Parking delay at destination
X8 = Walking time from parking place to destination
Relative Travel Cost (RTC) by competing modes is computed as:
RTC=x9/{( x10+ x11+ x12/2).1/x13}
Where, X9 =Transit Fare
X10 =Cost of gasoline
X11 =Cost of oil charge and lubrication
X12 =Parking Cost at destination
X13 = Average Car Occupancy

Relative Travel Service

(RTS) is computed as:
RTS= (x2+ x3+ x4+ x5)/( x7+ x8)
The application of Toronto model is in the forecasting of person trips.
The difference in the two methods lies in the method of introducing service characteristics in the model.
It is introduced in terms of ratio of accessibility index (macroscopic way) in Southeastern Wisconsin
method and directly as ratio of travel times, etc. in Toronto model.
Logit Analysis

The basic idea underlying modern approaches to travel demand modeling is that travel is the
result of choices made by individuals or collective decision-making units such as households.
Individuals choose which activities to do during the day and whether to travel to perform them,
and, if so, at which locations to perform the activities, when to perform them, which modes to
use, and which routes to take. Many of these choice situations are discrete, meaning the
individual has to choose from a set of mutually exclusive and collectively exhaustive
alternatives.
The mathematical transformation of logit model is as given below:
Pi=exp(Vi)/{exp(Vi)+ exp(Vj)}
Where, Vi and Vj are measured utilities of options i and j respectively.

Binary Choice Logit Model

These are the simplest type of mode choice models. These models compare the travel choices between
two modes. Say,cijm is the generalized cost of travel between zone ‘i’ and zone ‘j’ using a mode ‘m’, then
If cij2- cij1= positive, then mode‐1 would be chosen
If cij2- cij1 = negative, then mode‐2 would be chosen
If cij2- cij1= zero, then both the modes have equal probability of being chosen
The probability of choosing mode for a trip between zones ‘i’ and ‘j’ is given by:

Multinomial Logit Model

This is the simplest and most practical discrete choice model. It can be generated assuming that the
random residuals are distributed IID Gumbel such that,

Where the utility functions usually have the linear in the parameters form and parameter β is related
to the common standard deviation of the Gumbel variate by:
β2 = π2/6 σ2

Aggregate Model

Modal split models of 1960’s and early 1970’s in most cases were based on an ‘aggregate’
approach, which examined the mode choice of trip makers and their trips in groups based on
similar socioeconomic and/or trip characteristics. These mode choice models usually involved
two modes only - auto and transit. A detailed stratification scheme was used, and the share of
each mode was determined for each stratified group of trips, which then was correlated with
selected independent variables. The dependent variable was ‘percent transit’ applicable to a
group of trips of similar characteristics made by similar trip makers. Commonly used
independent variables include: the ratio of travel time by transit to that by automobile; the
ratio of travel cost by transit to that by automobile; and the ratio of accessibility by transit to
that by automobile. The relationship of the dependent variable, percent transit, with the
independent variable, say ratio of travel times, commonly was expressed by a set of curves.
These curves sometimes were referred to as modal diversion curves.

Disaggregate Behavioral Logit Models

During late 1970’s a new approach known as disaggregate behavioral method was developed
and refined by a number of researchers. This approach recognized each individual’s choice of
mode for each trip instead of combining the trips in homogeneous groups. The underlying
premise of this modeling approach is that an individual trip maker’s choice of a mode of
travel is based on the principle called ‘utility maximization’. Another premise is that the
utility of using one mode of travel for a trip can be estimated using a mathematical function
referred to as the ‘utility function’, which generates a numerical utility value/score based on
several attributes of the mode (for the trip) as well as the characteristics of the trip maker.
Examples of a mode’s attributes for a trip include travel time and costs. The utilities of
alternative modes also can be calculated in a similar manner. A trip maker chooses the mode
from all alternatives that has the highest utility value for him/her.