Transportation Behavioral
Modelling: An Introduction
Arnab Jana, PhD
Assistant Professor
Centre for Urban Science & Engineering
Indian Institute of Technology Bombay
September 21 to 23, 2019

Why are we interested in Travel Demand Modelling?
Forecast - Transportation Demand

Changes in the Attributes
Transportation system

PeopleUser
Vehicle/ Carrier
Roadway/ Facility
Environment2

Contents• Introduction to four step model• Choice Models• Activity Based Modelling Approach3

Introduction• Travel is considered as a derived Demand• Components of Transportation System
1.
2.
3.
4.

User
Vehicle/ Carrier
Roadway/ Facility
Environment• Transportation systems problems
1.
2.
3.
4.

Congestion
Pollution
SafetyParking4Four step model

5

Land use – activitysystemcharacteristics❶Trip Generation(How many trips)❷TripDistribution(Where do theygo?)❸Mode Choice(By what mode?)

Outputs

Transportation
systemCharacteristics

Urban Transportation model system

InputsFour step model
Traffic flow onnetwork
Quantity (Volume)
Quality (Speed)❹TrafficAssignment(By what route?)
6Four step model
❶

Oi

Dj

ZONE IZONE J

Trip generation❷Tij
ZONE J

ZONE I❸
Modal split

Trip distribution

TijM1
ZONE IZONE J
TijM2

Route taken by flow TijM1

ZONE IZONE J❹
Assignment
7

Example

ZONE 2
Pi: 66;
Aj: 90

PiProduction
147

266

3110

Aj
Attraction

ZONE 3
Pi: 110;
Aj: 88145

239088(Income)

Study

2Work
Other

61High3

Medium59Low

9To Zones

From Zones

ZONE 1
Pi: 47;
Aj: 45TijmrsTijmrsp

1712311018

1947

Trip Purpose

230

32

466

Education

3Work

123

540

65110

Other

245

9088

223

TijmrpTijm
Mode I
Mode II1725
1540

Tijmr
Route A

5Route B

17Route C

38❶Trip Generation• Aims at predicting the total number of trips generated by (Oi) andattracted to (Dj) each zone of the study area
• Trip or Journey: This is a one-way movement from a point of origin toa point of destination• Home-based (HB) Trip This is one where the home of the trip makeris either the origin or the destination of the journey• Non-home-based (NHB) Trip This, conversely, is one where neither
end of the trip is the home of the traveler

9Classification of Trips• Travel to work
• Travel to school or college (education trips)• Shopping trips• Social and recreational journeys• Escort trips (to accompany or collect somebody else)• Other journeys

10❷Trip Distribution• The purpose of the trip distribution is to estimate ‘zone to zone’
movements, i.e., trip interchanges
Gravity ModelProbability that a trip of a particular purpose k produced at zone i will
be attracted to zone j, is proportional to the attractiveness or ‘pull’ ofzone j, which depends on two factors.
One factor is the magnitude of activities related to the trip purpose k inzone j, and the other is the spatial separation of the zones i and j.

11❷Trip Distribution: Gravity Model• The gravity model assumes that the trips
produced at an origin and attracted to a
destination are directly proportional to
the total trip productions at the origin
and the total attractions at thedestination.
• The calibrating term or "friction factor"(F) represents the reluctance orimpedance of persons to make trips ofvarious duration or distances.
• The general friction factor indicates thatas travel times increase, travelers areincreasingly less likely to make trips ofsuch lengths.

Standard form of gravity model

12❸Mode Choice• Relates the probability of transit
usage to explanatory variables inmathematical form

Factors Affecting Mode Choice
Factors that may explain a trip maker’s
choosing a specific mode oftransportation for a trip are groupedcommonly as follows:• Trip Makers Characteristics:• Income• Car-Ownership• Car Availability• Age• Trip Characteristics:• Trip Purpose - work, shop, recreation, etc.
• Destination Orientation - CBD vs. non-CBD• Trip Length• Transportation Systems Characteristics• Waiting time
• Speed• Cost• Comfort and Convenience• Access to terminal or transfer location
13❸Mode ChoiceBus - 40 minutes

I choseCAR

Car - 35 minutes

Actual Behavior – Revealed Preference (RP) Data

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❸Mode ChoiceBus - 40 minutes

A new service introduced
Metro - 15 minutes

Car - 35 minutes

Hypothetical Behavior – Stated Preference (SP) Data

If new service isintroduced, I willchose Metro15

❸Mode Choiceexp(𝑣𝑀𝑒𝑡𝑟𝑜 )• 𝑃𝑀𝑒𝑡𝑟𝑜 =exp 𝑣𝑀𝑒𝑡𝑟𝑜 +exp(𝑣𝐸𝑀 )16

❹Traffic Assignment• Allocates the trips between each zone pair to the links comprising the most likely
travel routes.
• The trips on each link are accumulated and the total trips on each link arereported at the end of the assignment process All or Nothing Assignment User Equilibrium17

❹Traffic Assignment
All or nothing

User Equilibrium (UE)• Trips from any origin to any
destination is loaded into a single,
minimum cost path between them

• The user equilibrium assignment is based on
Wardrop’s first principle, which states that nodriver can unilaterally reduce his/her travel costsby shifting to another route.

Limitations:• UE conditions can be written for a given O-D pairas• Unrealistic as only one path isutilized• No consideration for capacity orcongestion – travel time is a fixed
input

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Choice models

19

Choice Models• Choice modelling is based primarily on the utility theory.
• Characteristics of the alternatives defines its attractiveness for aparticular user
• Utility is a subjective concept but it can be useful for comparison
between given alternatives.

20

Utility Theory• Each alternative has attractiveness or utility associated with it• Decision maker is assumed to chose that alternative which yields the highest utility• Utilities are expressed as sum of measured attractiveness and a random term
• Measured attractiveness is a function of the attributes of the alternative as well as
the decision maker’s characteristics
Where,U ji  V ji   ji
V ji   / Z ji

Z ji  ( X ji , Si )Uji =utility of alternative j for individual i
Vji = measured attractiveness of alternative j forindividual iji = random partZji = column vector of characteristics of the individual iand attributes of the alternative j = column vector of parameters

21Utility Theory• The alternative j is chosen by i whenU ji  U li for all l  j• The probability Pji for the jth alternative to be chosen isPji  Pr[V ji   ji  Vli   li ] for all l  j Pr[( li   ji )  (V ji  Vli )]22

Utility Theory
VCar= - 0.023*TIME -0.021*COST +0.003*INCOME -0.001
VBus= -0.023*TIME -0.021*COST-0.001*INCOME
VTrain= -0.023*TIME -0.021*COST+0.003
TIME and COST are generic variables

INCOME is alternative specific variable23

Variables …
• Generic Variable - Variable that appears in the utility functions of allalternatives in a generic sense and has same coefficient estimate forall the alternatives• Alternative Specific Variable - Variable that appears only in the utility
function of those alternatives to which it is specific and has different
coefficient estimate for each of the alternatives• Alternative Specific Constant - Takes care of unexplained effects

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Some Limitations of 4-step TDM• Traditional travel demand models ignore travel as a demand derived
from activity participation decisions• Does not incorporate the reason for traveling – the activity at the endof the trip
• Trips treated as independent and ignores their spatial, temporal, andsocial interactions• Heavy emphasis on commuting trips and Home-based trips• Limited policy sensitivity (TAZs are hard to use in policy analysis)25

Activity Based Modelling26

Necessity of Activity Based Travel Demand Modelling• Development of ABM due to poor forecasting results achieved in thetrip based aggregate demand models• Introduce - road pricing• New technologies - (Internet and mobile phones)• For solving urbanization problems, understanding behaviouralchanges of people in developing countries is necessary27

Activity Based Modelling – Historical• ABM belongs to the 3rd generation of travel demand models• Trip based 4-step models• Disaggregate trip based models (1980’s & 1990’s)• Activity based models• In ABM the basic unit of analysis is the activities of individuals/households• Activity Based Models (ABM) predict travel behavior as a derivative of activities(i.e., derived demand)• Travel decisions are part of a broader process based on modeling the demandfor activities rather than merely modeling trips• ABM are based on the theories of Hägerstrand (1970) and Chapin (1974)• Hägerstrand focused on personal and social constraints• Chapin focused on opportunities and choices• Theory is that activity demand is motivated by basic human desires for:survival, ego gratification, and social encounters
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ABM Approach• Travel demand is derived from activities that individuals need/wish toperform• Sequence/patterns of behavior, not individual trips, are the unit of analysis• Household and other social structures influence travel and activity
behavior• Spatial, temporal, transportation, and interpersonal interdependencies
constrain activity/travel behavior• Activity based approaches aim at predicting which activities are conducted
where, when, for how long, with whom, by mode, and ideally also theimplied route decision

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ABM Paradigms• ABM rely on the following 5 paradigms:• Travel derived demand from activity participation• Focus is on the sequence of activities• Activities are planned within the context of the household• Activities are spread over a 24-hour• Travel choices are limited in time, space, and by personal constraints

30Modelling Trips

Trip 6

Trip 7

Trip 4

Trip 2

Trip 5Trip-based model Tour-based model ABM

Hypothetical Travel Day

Trip 3Trip-based model would model all 7 trips independent of the othertrips
Tour-based model would model Tour 1 and Tour 2 independent ofeach other, while the Work Tour would be modeled as two
independent trips
ABM would model the 4 activities and associated trips (work,meeting, shopping, and movie) as part of the same decision process
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TimeActivities in Time and Space

Activities:
H … Home W … Work

L … Leisure S … Shopping

WHL
S32

Activities in Time and SpaceHS

L

Activity time- MaintenanceTravel- Maintenance

LFTimeWF

Travel- Leisure

Activity timeLeisure

Activity timeMandatory

W
Travel- Mandatory

HHHomeW

Work- Mandatory

F

Social connection- Leisure

LCinema- LeisureS

Shopping- MaintenanceSpaceSource: Varun Varghese

Criticism of Trip and Tour Based Models
Modelled as independent andisolated trips

Modelled as independent andisolated tours• No-connection between thedifferent trips• No-temporal dimension• No-time component• No-sequential information• Independent tours, model is notcapable of making theintegration• No-behavioural foundation• No-data efficient34

Advantages of ABM• Theoretically based on human behavior• Better understanding and prediction of traveler behavior• Based on decision-making choices present in the “real-world”• Use of disaggregate data
• Inclusion of time-of-day travel choices

35

Activity Patterns (Schedule)
A sequence of activities, or a schedule, defines a path in space and time
What defines a person’s activity pattern?
• Total amount of time outside home
• Number of trips per day and their type

• Allocation of trips to tours• Allocation of tours to particular HH members• Departure time from home

• Activity duration• Activity location• Mode of transportation• Travel party• Arrival time at home in the evening

36

A Person’s Daily Travel Pattern (conventional model)
TRIPS:-2 HBW-1 HBS-1 HBO-1 NHB

Shop

Home
Home Based Other by carDinner

Work
37

A Person’s Daily Travel Pattern(activity based model)
TRIPS:-2 HBW-1 HBS-1 HBO-1 NHB

Shop

Home

5:30 PM
9:30 PM
5:15 PM

7:00 PM

7:30 PM9:00 PMHome Based Other by car

7:30 AMDinner-2 Home based tours (chains)-Timing of all trips-Duration of activity at eachlocation

4:30 PMWork
8:15 PM38

All Household Members’ Travel Pattern(activity based model)
Shop
5:30 PM
9:30 PM

Home

Home toSchool by Car

4:00 PM

5:15 PM

7:00 PM

7:30 PM
9:00 PM

Home Based Other by car

Dinner

7:30 AM

4:30 PM

8:00 AM

SchoolSchool to Work by Car 8:15 PM
3:30 PMWork

3:15 PM39

Some Key Aspects of Activity Based Models• Trips are linked for each person in a day• Timing and durations are included• Entire daily travel patterns are linked• Car use is associated to needs (take child to school, drive together toshop & dine and back )40

Survey Instrument• Household Information• Person Information• Activity Information

Activity DiaryActivities classified:• Work related activities• Maintenance activities• Leisure activities

41Modelling approaches• Econometric modelling• Rule based modelling• Markov models• Microsimulation modelling42

CONCLUSION• Conventional four stage-planning models for travel demand
forecasting includes the lack of behavioral foundation, overdependence on trips, and insensitivity to policy changes.
• There is a need to develop the models which will take into accountabove criteria's to improve the travel demand.
• The new modeling approach i.e. activity based travel demandmodeling has good scope in developing countries due to its more
focus on behavioral aspect of people.

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Best wishes!!

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1st International Conference on Urban Scienceand Engineering• Date: 28-29 February 2020
• Venue: VMCC, IITB

• All are welcome to submit paper and participate• See you in India!

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