The 19thBehavior Modeling Summer Course 2020
Transportation Modelling: An Introduction
Arnab Jana
September 14, 2020

The University of Tokyo
http://bin.t.u-tokyo.ac.jp/model20/index_e.html

Why are we interested in Travel DemandModelling?
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• Demand for Travel is 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 – activity
systemcharacteristics❶Trip Generation(How many trips)❷Trip Distribution(Where do theygo?)

Outputs

Transportation
systemCharacteristics

Urban Transportation model
systemInputsFour step model
Traffic flow onnetwork
Quantity (Volume)
Quality (Speed)❸Mode Choice(By what mode?)❹TrafficAssignment(By what route?)
6Four step model❶
DjOi

Trip generation

ZONE J

ZONE I❷

Tij

ZONE J

ZONE I❸

TijM1
ZONE I

Trip distribution

ZONE J

Modal splitTijM2❹
ZONE IZONE J

Assignment

Route taken by flow TijM1
7Tijmrsp

ExampleZONE 1
Pi: 47;
Aj: 45

2Work
Other

61High3

Medium59Low

917Production
147

266
110

Aj
Attraction

ZONE 3
Pi: 110;
Aj: 88

Edud.

Pi

3
ZONE 2
Pi: 66;
Aj: 90Tijmrs(Income)

145

2
3

F
rom
Zones

To Zones
123

1
1030
545

2
18
32
40
90

Tijm

3
19
465
88

4766
110
223

Mode I
Mode II

Tijmr
25
1540

Route A

5Route B

17

Route C

3Tijmrp
Trip Purpose
Education

390Work1288Other2817

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 in mathematical form
• Factors Affecting Mode Choice
Factors that may explain a trip maker’s choosing a specific mode of transportation for a trip are grouped commonly as follows:• Trip Makers Characteristics:••••

IncomeCar-Ownership
Car AvailabilityAge• Trip Characteristics:•••

Trip Purpose - work, shop, recreation, etc.
Destination Orientation - CBD vs. non-CBDTrip 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 – Reveled Preference (RP) Data

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❸Mode ChoiceBus - 40 minutes

A new service introduced
Metro - 15 minutes

If new service
is introduced, I
will chose
Metro

Car - 35 minutes
Hypothetical Behavior – Stated Preference (SP) Data

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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 no driver can unilaterally
reduce his/her travel costs by shifting to another route.

Limitations:• Unrealistic as only one path is utilized• No consideration for capacity or congestion – travel
time is a fixed input• UE conditions can be written for a given O-D pair as18

Choice models

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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 for individuali

ji = random partZji = column vector of characteristics of the individual iand attributes of the alternative j = column vector of parameters

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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

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

23

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)24

Activity Based Modelling25

Necessity of Activity Based Travel DemandModelling• 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 necessary

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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 demand for activities rather thanmerely 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

29Modelling Trips


Trip-based model
Tour-based model
ABM

Hypothetical Travel Day

Trip 7

Trip 6

Trip 5Trip 4

Trip 2
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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Time

Activities in Time and SpaceW

HL
S

Activities:
H … Home W … Work

L … Leisure S … Shopping31

Activities in Time and Space
H
S

L

Activity time- MaintenanceTravel- Maintenance

L

F
Activity time- Leisure

TimeW

F

Travel- Leisure

HHomeW

Work- Mandatory

Activity time- MandatoryW
Travel- Mandatory

HF

Social connection- Leisure

LCinema- Leisure

SShopping- 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 efficient33

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 choices34

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

35

A Person’s Daily Travel Pattern (conventionalmodel)

ShopHomeDiner

TRIPS:-2 HBW-1 HBS-1 HBO-1 NHBWork

36

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

7:00 PMShop

5:30 PMHome

7:30 PM

9:30 PM
5:15 PMDinner

7:30 AM

9:00 PM-2 Home based tours(chains)-Timing of all trips-Duration of activity ateach location

4:30 PMWork

8:15 PM37

All Household Members’ Travel Pattern(activity based model)
7:00 PMShop

5:30 PMShop to Diner by car

9:30
9:30PMPM
5:15 PM
5:15
PMHomeDiner

7:30 AM

Home to Work by Bus
Home to School by Car
Work to Home by Bus
3:30 PM

School to Home by Car

7:30 PM

9:00 PM

4:30 PMWork to School by Car

2:30 PMSchool

School to Work by CarWork

8:00 AM

8:15 PMWork

9:00 AM38

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 )39

Survey Instrument• Household Information• Person Information• Activity Information

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

40Modelling approaches• Econometric modelling• Rule based modelling• Markov models• Microsimulation modelling41

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.

42Best wishes!!

For details:

Arnab Jana, PhD
Assistant Professor | Centre for Urban Science and Engineering
Associated Faculty | Centre for Policy Studies
Indian Institute of Technology Bombay+91 22 2576 9331
arnabjana[@]gmail.com; arnab.jana[@]iitb.ac.in
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