UNIT 4

TRIP GENERATION & DISTRIBUTION

PREPARED BY
GAUTHAM KRISHNA
INTRODUCTION
• The first phase of the transportation planning process deals with
survey, data collection and inventory.
• The trip generation models strive to predict the number of trips
generated by a zone.
• These models try to mathematically describe the decision to travel
phase of the sequential demand analysis procedure.
• The analysis and model building phase starts with the step
commonly known as trip generation.
TRIP GENERATION
• Trip generation is the first stage of the classical first generation
aggregate demand models.
• The trip generation aims at predicting the total number of
trips generated and attracted to each zone of the study area.
• In other words this stage answers the questions to how many
trips originate at each zone, from the data on household and
socioeconomic attributes.
TRIP DISTRIBUTION
• The decision to travel for a given purpose is called trip generation.
These generated trips from each zone are then distributed to all
other zones based on the choice of destination.
• This is called trip distribution which forms the second stage of travel
demand modeling.
• There are a number of methods to distribute trips among
destinations; and two such methods are growth factor model and
gravity model.
• Growth factor model is a method which responds only to relative
growth rates at origins and destinations and this is suitable for
short-term trend extrapolation.
• In gravity model, we start from assumptions about trip making
behavior and the way it is influenced by external factors.
MODE CHOICE
• Mode choice models estimate how many people will use public
transit and how many will use private automobiles. The most
common form of the mode choice model is the logit model.
• The logit mode choice relationship states that the probability of
choosing a particular mode for a given trip is based on the relative
values of a number of factors such as cost, level of service, and
travel time.
• The most difficult part of employing the logit mode choice model is
estimating the parameters for the variables in the utility function.
The estimation is often accomplished using one or more
multivariate statistical analysis programs to optimize the accuracy of
estimates of the coefficients of several independent variables
ROUTE ASSIGNMENT
• Trip assignment involves assigning traffic to a transportation
network such as roads and streets or a transit network.
• Traffic is assigned to available transit or roadway routes using
a mathematical algorithm that determines the amount of
traffic as a function of time, volume, capacity, or impedance
factor.
• There are three common methods for trip assignment: all or
nothing, diversion, and capacity restraint.
Trip generation
• Trip generation is the first stage of the classical first generation
aggregate demand models.
• The objective of the trip generation stage is to understand the
reasons behind the trip making behaviours and to produce
mathematical relationship to synthesize the trip making
pattern on the basis of observed trips, land use data,
household characteristics and trip attraction the trip made to
a particular urban location or activity.
• The three major techniques used for Trip Generation Analysis
are

• Cross Classification,

• Multiple Regression Analysis,

• and Experience Based Analysis.

FACTORS AFFECTING TRIP GENERATION
• Distance of zone from town centre
• Land use characteristics
• Income
• Vehicle ownership
• Family size
• Accessibility to public transport
• Employment opportunity
Trip generation analysis
• Understanding on trip generated according to land use in an
urban area
• Trip – info related to a journey
• Trip – for analysis it’s a 1 way movement from origin to
destination
• The entire urban area is divided into zones
• The principal task of trip generation is to relate the intensity
of trip making to and from land use parcels or zones to
measure the type and intensity of land use
• The no of trips related to land use are :
• Trip production analysis and trip attraction analysis
• Types of trips
• Divided into 2 – HOME BASED TRIPS & NON-HOME BASED
TRIPS
• HBT – one trip end is household( origin or destination)
• NHBT – between workplace and shopping area
ZONE 1 – R – RESIDENTIAL, ZONE 2 – W – WORKPLACE,
ZONE 3 – S/R – PARTIALLY SHOPPING & PARTIALLY
RESIDENTIAL

W
R
b

g c

h e
d

R
CLASSIFICATION OF TRIPS –
TIME OF DAY
• Classified as peak and offpeak periods
• The proportion of the trips vary by different purposes usually
with the time

Purpose AM / peak % OFF PEAK %

Work 52000 52 12000 12

Return 2700 2.7 42000 42

home
BASED ON SOCIO-ECONOMICS
• CLASSIFIED AS:
• Income level
• Car ownership
• Household size

• Ex: 5 levels of income, 3 levels of car ownership, 6 household

size, how many classes in total?

• The total number of classes = 5*3*6 = 90

Growth factor method
• Increase or decrease in number of trips by a factor
• Basic equation Ti = Fi *ti
• Where Ti – future trips in zone
• ti – current trips in zone
• Fi – growth factor in zone I
• Fi = f( Pi^d, Ii^d, Ci^d / f( Pi^c, Ii^c, Ci^c )
• P – POLULATION
• I – INCOME
• C- CAR OWNERSHIP
• d – design period
• c – current period
Consider a zone with 250 households with car and 250
households without car. Assuming average trip generation
rates of each group is known, find the number of future trips
given: car owning HH – 6 trips per day and non car HH – 2.5
trips per day
• SOLUTION:
• Current number of trips per day = (250*2.5) + (250 * 6)
• ti = 2125 trips per day
• Assuming in future all HH have cars (income and population
remaining constant)
• Fi = Ci^d / Ci^c
• = 100 percent / 50 percent
• = 1/0.5 = 2
• The number of future trips = Fi * ti
• Ti = 4250 trips per day
 Trip rates
• Trip rate analysis refers various models those are based on the
determination of the average trip-production or trip-attraction rates
associated with the trip generators within the region. An example is
given in Table to display the trip generation rates associated with
various land-use categories
Land use category Persons per 1000sq ft Person trips Trips per 1000
sqft

residential 2744 6650 2.4

commercial 3100 9589 3.1
services 10524 55620 5.9
Whole sale 3215 4520 1.9
manufacturing 1520 1782 1.1
Public buildings 3100 12540 3.9
Cross classification / category
analysis
• Cross-classification models are may be extensions of the trip-
rate models.
• They can be calibrated as area or zone-based models, in trip
generation studies they are used as disaggregate models.
• In the residential-generation context, household types are
classified according to a set of categories that are highly
correlated with trip making.
• Three to four explanatory variables, each broken into about
three discrete levels, are sufficient.
• Typically household size, automobile ownership, household
income and some measure of land development intensity are
used to classify household types.
Steps in calibrating a cross
classification model are:
• Group the households by desired characteristics (no of

persons, car ownership etc)

• Calculate a trip rate (trips / household) for each category

To predict the future trips for a
zone
• Determine the number of households in each category

• Multiply the appropriate trip rate by number of households in

each category

• Sum the results across all the categories to obtain an estimate

of total number of trips for the zone

example
Family size 0 1 2+

1 1 2.7 4.4

2 1.5 5.1 7

3 3.1 7.2 9.4

4 3.2 8 11.7

5 5.2 9.2 13.4

limilations
• Ignoring the within category variances, eventho majority of

variation arises from variation rather than between cells

• The accuracy of the estimates of trip rates depends on the

number of households in each cell

• Difficulties to account for land use accessibility factors because

these variables are difficult to divide into meaningful ranges and

become the number of cells quickly become too large

TRIP PRODUCTION
STATISTICAL ANALYSIS
• There is a strong mover towards trip end modeling on a
person rather than a household basis in international
modeling practice.
• In any modeling approach some link between planning data
(generally person-based) and the trip end model is required,
referred to herein as the family structure model); this is a
significant complication.
• While the descriptions below appear to make the person
model seem complicated, in fact the model itself is extremely
simple, comprising a trip rate for each person, the rate varying
by person characteristics. The rate may also vary by household
characteristics, although this seems only likely to be an issue
for a few trip purposes.
• The model specifications below are non-standard but draw on
other model specifications and the generally consistent
findings on influential variables to create a convenient
forecasting framework.
• Although the current model makes use of household trip
rates, it is strongly recommended to consider a movement
over to person trip rate, while retaining the facility to include
household effects where justified.
Home-Based Work (HBW)
• Model Variations in trip rates are likely to be as follows: 
• Person effects: A function of whether or not the person is
employed; also expect the trip rate to vary by work structure
(full/part-time; contractor ...1) because these dictate the need
to make a commuting journey;
• Household effects: Number of cars possible but unlikely;
inverse correlation with number of children possible (the
school trip substituting for the work trip), but this seems too
detailed a refinement and of little policy interest; possible
correlation with location, but again unlikely.
• Conclusion: We should seek a person trip rate model sensitive
primarily to work structure.
Home-Based Education
(HBEd) Model
• Similar to the HBW model, variations are likely to be primarily
due to person type:
• Person effects: expect to vary by age of child (essentially
starting to reduce from school leaving age); then lower rates
for young adults in higher education; then tiny rates for older
adults;
• Household effects: no particular interactions are expected
with household characteristics
Example – category analysis
• Zone 100 has 48HH which are high income

• Also half of them have 3 vehicles

• So 48*0.5 = 24HH with high income owns 3 vehicles

• Also these HH makes 3.25 trips per day

• So 24HH making 3.25 trips per day gives a total of 78 trips

• Also one third of these trips are work based trips

• So there are 26 work trips for zone 100

The question

• How do we know in zone 100

• 48HH are high income category

• Half of them have 3 vehicles

• No of trips made is 78

• No of work trips is 26
There are 2 methods

1. By conducting survey in each zone

2. By graphical method
Graphical method
• Use graphs representing generic travel pattern for small –
medium sized communities

 Graphs representing income

 Graphs representing vehicle ownerships

 Graphs representing no of trips

Questions to answer

• How many houses are there in each category

• How many trips are made by each category

• What is the trip based purposes

 Example ZONE – 200HH – LOW INCOME – 2 VEH = 200*1.5=300
HH income No of vehicles No of trips
15 1 1
21 2 1
23 2 2
24 1 2
28 1 2
30 1 2
31 2 2
32 1 2
39 3 3
46 3 4
50 2 3

Average trips for Low income with 1 vehicle = (2+2+1)/3 = 1.7

Average trips with low income 2 vehicles = 3/2 = 1.5
A zone with 100 low income HH with one vehicle will have = 100*1.7 = 170 trips
per day
Graphs used
• Zone income vs HH in each income category – I%
• Vehicle or auto ownership vs HH in each income category – A
%
• Trips per households vs income category – T%
• Trips purpose vs income category - % by purpose
Determine the no of HBW, NHB, HBO for medium
income HH residing in zone 100 which has 44 HH
and 44000 average income
• No of HH in category = 40%
• A0 = 4%, A1 = 58%, A2+ = 38%
• No of HH with 0 vehicles = 400 * 40% * 4%
• = 6.4
• “ “ with 1 vehicle = 400* .4 * .58 = 92.8
• “ “ with 2+ vehicles = 400 * .4 * .38 = 60.8
• Based on trips
• T0 = 2, T1 = 8, T2+ = 13
• TRIPS PER HH = 6.4*2 + 92.8 * 8 + 60.8 * 13 = 1546 TRIPS
• TRIPS ON PURPOSE
• HBO = 51%
• NHB = 32%
• HBW = 17%
• HBO = 51% * 1546 = 789 TRIPS
• NHB = 32% * 1546 = 495 TRIPS
• HBW = 17% * 1546 = 263 TRIPS