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Phd in Transportation / Simulation of Land Use -Transportation Systems 1/45

Phd Program in Transportation

Simulation of Land Use-Transportation Systems

João de Abreu e Silva

Session 5 Activity Based Approach

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Criticisms to the 4 step model (I)

Lack of valid representation of the underlying travel behavior

(developed to evaluate the impact of capital-intensive infrastructure

investment projects)

Travel demand as a derived demand

"a common philosophical perspective, whereby the conventional

approach to the study of travel behavior ... is replaced by a richer,

more holistic, framework in which travel is analyzed as daily or multi-

day patterns of behavior, related to and derived from differences in

lifestyles and activity participation among the population“ (Jones et

al., 1990).




Criticisms to the 4 step model (II)

4 step model - travel demand and network performance tend toward

equilibrium . In most cases there is no integration only in the

assignement part demand is integrated with the supply

Trips are the fundamental unit of analysis

There is no temporal dimension – first applications considered only

the morning peak hour

The production and atraction of trips is made by independent models

(no feedback e.g. not doing the trip)

Ignore the spatial and temporal interconnectivity in household travel

beahvior

4 step model – travel is not a derived demand




Criticisms to the 4 step model (III)

PT, 2X

Work Squash By foot,

2X

At

home

At

home

Family

visit Car, 2X

Work

Trip-based model

• Modelling as independent and isolated

trips, no connections between the

different trips

• no time component

• no direction

• no sequential infomation

Work

At home

Play Squash

Family

visit

7.30h,PT

12h,

By foot 12.50h,

By foot

16.40h,PT

22h,

Car 19h,

Car

Reality




Criticisms to the 4 step model (IV)

According to Kitamura trying to infer underlying behavior from the

observation of only trips is somewhat akin to trying to understand the

behavior of an octopus by examining only the individual tentacles

(McNally and Craig, 2007)

There is a need to move from a more statiscally oriented trip based

approach to a more behaviorally oriented activity based (Bhat and

Koppelman, 2003)




Criticisms to the 4 step model (V)

A focus on individual trips, ignoring the spatial and temporal interrelationship

between all trips and activities comprising an individual’s activity pattern;

Misrepresentation of overall behavior as an outcome of a true choice process,

rather than as defined by a range of complex constraints which delimit (or even

define) choice;

Inadequate specification of the interrelationships between travel and activity

participation and scheduling, including activity linkages and interpersonal

constraints;

Misspecification of individual choice sets, resulting from the inability to establish

distinct choice alternatives available to the decision maker in a constrained

environment;

The construction of models based strictly on the concept of utility maximization,

neglecting substantial evidence relative to alternate decision strategies involving

household dynamics, information levels, choice complexity, discontinuous

specifications, and habit formation.




Criticisms to the 4 step model (VI)

When the focus shifts from infrastructure provision to mobility

management the 4 step model is inadequate

It doesn't reflect:

A. the linkages between trips and activities

B. the temporal constraints and dependencies of activity scheduling

C. the underlying activity behavior that generates the trips

D. The scheduling or organization of trips and activities

E. It doesn´t distinguish between non-home based trips made during

different periods of the day (ignores important aspects that influence

mode choice)

They are not policy-sensitivite (for policies other than

infrastructure provision).




From the 4 step model to the activity

based approach

Travel is basically a physical mechanism to access the place where a

desired activity is to be performed

Criticism on the 4SM didn´t fuelled at first the activity based approach

but instead led to the development of innovations in this earlier

approach (eg discrete choice models, and equilibrium assignment)

Travel decisions are driven by a collection of activities which form an

agenda – thus when one perceives how this agenda works is able to

perceive travel decisions




Activity based approach antecedents (I)

Mitchell and Rapkin (1954) Urban Traffic: A Function of Land Use

- Established the link between travel and activities

Hagerstrand (1970) – time geographic approach - constrains of

activities in time and space.

Emphasized the constraints imposed by the spatial distribution of

opportunities for activity participation and temporal considerations on

individual activity participation decision – Space time prism




Hagerstrand Space Time Prism




Constant travel time budget

Mean Trip duration (hours versus distance) – first proposed by

Zahavi

http://www.bts.gov/publications/journal_of_transportation_and_statistics/volume_03_number_

03/paper_01/




Space time representation of travel and

activities




Activity based approach antecedents (II)

Chapin (1974) identification of behavior patterns across time and

space . Developed a motivational framework in which societal

constraints and inherent individual motivations interact to shape

revealed activity participation patterns

Fried, Havens and Thall (1977) – Social structure and participation in

activities




Activity based approach antecedents (III)

Cullen and Godson (1975) argued that the spatial and temporal

constraints identified by Hagerstrand are fundamentally

characterized by varying degrees of rigidity (or flexibility).

They undertook extensive empirical analysis to indicate that

temporal constraints are more rigid than spatial constraints and

that the rigidity of temporal constraints is closely related to activity

type of participation (with more temporal rigidity associated with

work-related activities compared to leisure activities).




Jones et al (1983) in the Transport Studies Unit at the University of Oxford were the first

ones to define and empirically testing the activity based approach




• Trips that start and end from home

or from the same work-location are

modelled independent

• Direction + (spatial) limitations

• No temporal dimension

• Independent tours, model is not

capable of making the integration

• Uses Nested logit techniques

Tour-based model Play Squash

By foot By foot

Work

At

home

PT PT

Car Car

At home

Family

visit

Work

At home

Play Squash

Family

visit

7.30h,PT

12h,

By foot 12.50h,

By foot

16.40h,PT

22h,

Car 19h,

Car

Reality

Tour based Approach




Comparison between 4 step and activity

based model structures

Source:Clarke, Philip; Davidson, Peter and Thomas, Adam (2008)




Population Synthetizer

Needed to construct the microdata set that represents the characteristics of

individuals and households (decision agents of interest).

The method more used was developed by Beckham et al (1996) (Beckman, R.J., Baggerly,

K.A., and McKay, M.D., 1996. Creating synthetic baseline populations. Transportation Research Part A, 30(6), 415-

429.)

It integrates aggregate data from one source (e.g. Census data) with disaggregate

data from another source (typically using survey results)

The disaggregate data usually represents households with the information

characterizing each household member

The aggregate data represents joint aggregate distributions of relevant

socioeconomic and demographic variables

The method devised by Beckman and his colleagues uses the disaggregate data

as “seeds” in order to create individual population records that are collectively

consistent with the cross tabulations provided by the aggregate data.




Activity Generator (based on Transims)

It is used to generate the household activities activity priorities, activity locations,

activity times, and mode and travel preferences.

Main input is an activity survey.

The activity assignment process consists in matching synthetic households with

corresponding survey households.

Activities will be produced for each household. The activities are associated with a

set of parameters:

activity importance,

the activity duration,

and a time interval during which the activity must be performed,

if it is performed at all (mandatory versus non-mandatory activities)

Sample size, and more generally data quality, could be a limitation




Activity Scheduler

The activity scheduler aims to model the scheduling of activities by households (e.g. as a

response to transport policies)

Could be based on the theory of consumer choice (how much time people allocate to certain

activities and how they schedule them)

The individual maximizes utility subjected to constraints (e.g. income)

Other frameworks :

Enumerating feasible alternatives – generating the possible alternatives, subjected to

spatiotemporal constraints, and then choosing among them (CARLA)

Two stage process – pre-travel (scheduling a set of activities). Travel this schedule is

monitored and evaluated, which then influences further decisions (STARCHILD)

Based on the cognitive model of planning – Activities are defined as means in which the

environment allows individuals to attain their objectives: It considers individual

preferences based on their beliefs about the relevance and importance of activities to

achieve the desired goals. The choice of participating in activities is determined by the

individual preferences together with prior commitements (SCHEDULER)




Mantra of activity based approach

Travel is derived from the demand for activity participation

Sequences or patterns of behavior, and not individual trips, are the

relevant unit of analysis

Household and other social structures influence travel and activity

behavior

Spatial, temporal, transportation, and interpersonal

interdependencies constrain both activity and travel behavior; and

Activity-based approaches reflect the scheduling of activities in time

and space.




Individual activity programs

Individual activity programs emerge from a decision process which

allocates different responsabilities to different household members

Subjected to the following constraints

Environmental

Transportation

Ineherent to the household

The result are individual activity programs




Complexity

Complexity problems

Eg one household with only one member and 3 non-home activities

leads to 107 potencial solutions

Introducing inter personal interactions and other constraints like in-

home activities will reduce the number of potential solutions, but if we

introduce more members in the household the number of possible

solutions will increase very fast




Solutions to complexity

To cope with complexitiy

Fried et al (1977) introduced ideas related with stable points of

reference – social and role structures (and they influence activity

behavior)

Household life cycle – In different stages in life the role structures

change (later used by Jones in 1983)

Activity pattern also exhibits temporal stability




Short term adaptations

Structuration (Lundberg, 1988)– both

top down – constrains and shapes the individual behavior (eg

accessibility of resources to perform a determined activity) and

bottom up – transformed by individual actions (the desire or need to

perform a specific activity)

Environment – the transport system and the location of activities in

space

Each possible activity exerts “arousal” (measured using the

constraints and the desires or needs) on the individual

Heuristics are used to attemp finding the best person environment fit

(if it doesn’t work the individual will make more drastic changes –

changing job or horme)




Modeling tools used in Activity based

approaches

The choice of activities can be viewed as a solution to an allocation

problem (with limited resources and constraints) to achieve a higher

quality of life (utility???)

Simulation based applications

Computational Process Models

ALBATROSS




ALBATROSS

Albatross: A learning based transportation oriented simulation system

= activity-based model of activity-travel behavior, derived from theories of choice heuristics

Developped in the Netherlands (Arentze, Timmermans ;2000)

The model predicts which activities are conducted when, where, for how long, with whom and also transport mode

Decision tree is proposed as a formalism to model the heuristic choice

Crucial component of the model. The better the learning algorithm, the better the prediction…




Constraints that have been taken into

account in Albatross

Situational constraints: can’t be in two places at the same time

Institutional constraints: such as opening hours

Household constraints: such as bringing children to school

Spatial constraints: e.g. particular activities cannot be performed at particular locations

Time constraints: activities require some minimum duration

Spatial-temporal: constraints an individual cannot be at a particular location at the right time to conduct a particular activity




Modelling Choice behavior

Models used to rely on utility-maximization

Albatross assumes that choice behavior is based on rules that are formed and continuously adapted through learning while the individual is interacting with the environment (reinforcement learning) or communicating with others (social learning).

As said, rules are currently derived from decision trees

Other rule-based learning algorithms can also be used




ALBATROSS The scheduling model

Components:

1. a model of the sequential decision making

process

2. models to compute dynamic constraints on

choice options

3. a set of decision trees representing choice

behavior of individuals

related to each step in the process model

]]

a-priori

defined

derived from

observed choice

behavior

Skeleton refers to the fixed and given part of the schedule

Flexible activities: optional activities added on the skeleton




Econometric based applications (I)

Econometric based applications

TRANSIMS

The model generates a daily activity pattern through application of a

(heavily) nested logit model that reflects primary and secondary tours

and associated characteristics. The proposed model structure was

significantly reduced in scale due to estimation problems, primarily

defined by combinatorics.

It is linked with a population synthesizer and integrated with a

microsimulation of modeled travel behavior




Econometric based applications (II)

Econometric Modeling methods

Discrete choice models

Structural equation models

Hazard duration models – modeling duration data – modeling the end

of duration occurrence given the duration until that moment

Discrete/continuous models to estimate activity type choice and

activity duration

Discrete/Ordinal Models – eg discrete/grouped system of

employment and income or work mode choice and non work activity

stops during commuting (eg usefull to modeling ridesharing)




Mathematical Programming Approaches (I)

Household Activity Pattern Problem (Recker 1995) – variation of the

pick up and delivrey problem with time windows.

As applied, households "pick-up" activities at various locations within

a region, accessing these locations using household transportation

resources and reflecting interpersonal and temporal constraints, and

"deliver" these activities by completing a tour and returning home.

Constructed as a mixed integer mathematical program, HAPP both

provides a theoretical basic and explicitly reflects a full range of travel

and activity constraints.




Mathematical Programming Approaches (II)

Prism- Constrained Activity-Travel Simulator PCATS (Kitamura and

Fujii, 1988)

Divides the day between two types of periods

Unit of analysis - individual activities

Open periods – when one has the option of engaging in flexible

activities – It attemps to fill this open periods based on space time

prism of activities.

It uses sequential structure for generation of the activity episodes

and associated attributes (activity type, activity duration, activity

location, and mode choice) within the "open" period




Mathematical Programming Approaches (III)

Comprehensive Activity-Travel Generation for Workers (CATGW)

(Bhat and Singh, 1999)

Based on the fixity of two temporal points in a worker's continuous

daily time domain. Arrival time at work and the departure time from

work. (before morning commute pattern, work commute pattern,

midday pattern, and post home-arrival pattern).

Tours start and end at home, circuits start and end at work




Advantages of rule based models

Rule-based mechanism to restrict the number of activity-related

choices available to an individual as well as for choice selection from

the restricted choice set.

Vause emphasizes the need to avoid the use of a single choice

strategy in modeling and advances the use of the rule based

mechanism as a method to simulate different choice strategies (such

as satisfaction, dominance, lexicographic and utility) within the same

operational framework.




Benefits from Activity based approach

Better specification of travel demand models – Activity based

approaches tend to increase the quality of trip based planning

methods

Example:

Travel activity behavior (number of stops and number of trips)

increase the fit of mode choice models

Use of concepts like life cycle, and the recognition of intra household

interactions, time constraints (using travel time as an independent

variable in traditional non-work trip generation models)




Issues in Activity based approach

Inter – individual interactions

This is a subject that has received so far limited attention

Interactions among individuals might take the form of:

Joint participation in certain activities

Escort activities

Car allocation inside the household

They are important due to the constraints that they might impose




Issues in Activity based approach

In-Home and Out-of Home Activity Substituition

Thus, the in-home/out-of-home participation decision has an impact

on the generation of trips.

Telecommuting

Teleshopping

Leisure at home

Understanding how this substitution is made has important impacts

on travel demand

Collection of in-home activities has impaired the modeling of it




Main problems and future directions

There is still a lack of good understanding on the decision

mechanisms underlying activity patterns




Moving from4 step to Activity based

models






models





Applications of activity based models




References and further readings

Chapin, F.S. (1974) Human activity patterns in the city. New York: Wiley

Hägerstrand, T. (1970) “What about people in regional science?” Papers of the Regional Science Association, 24:7-21

Jones, P. M., M. C. Dix, M. I. Clarke, and I. G. Heggie (1983) Understanding Travel Behavior. Aldershot: Gower.

Mitchell, R. and C. Rapkin (1954) Urban Traffic: A Function of Land Use, New York: Columbia University Press.

Recker, W. W. (1995) “The Household Activity Pattern Problem: General Formulation and Solution”, Transportation Research B,

29:61-77.

Fried, M., J. Havens, and M. Thall (1977) “Travel Behavior -- A Synthesized Theory, NCHRP, Transportation Research Board,

Washington, Final Report.

Lundberg, C. G. (1988). “On the Structuration of Multiactivity Task-environments”, Environmental and Planning A, 20:1603-1621.

Bhat, C.R. and S.K. Singh (2000) A comprehensive daily activity-travel generation model system for workers, Transportation

Research Part A, 34, pp 1-22.

Kitamura, R. and S. Fujii (1998) Two computational process models of activity-travel behavior, In T. Garling, T. Laitila and K.

Westin (eds.) Theoretical Foundations of Travel Choice Modeling, Oxford: Elsevier Science, pp. 251-279.

Cullen, I. and V. Godson (1975) Urban networks: the structure of activity patterns, Progress in Planning, 4, 1-96.

McNally, Michael and Rindt, Craig (2007), The Activity – Based Approach, Institute of Transportation Studies, UCI,

http://www.its.uci.edu/its/publications/papers/CASA/UCI-ITS-AS-WP-07-1.pdf

Bhat, Chandra and Koppelman, Frank (2003) Activity-Based Modeling of Travel Demand,

http://www.ce.utexas.edu/prof/bhat/ABSTRACTS/TSHANDBK.pdf

Axhausen, K.W. and T. Gärling (1992) Activity based approaches to travel analysis: Conceptual frameworks, models and

research problems, Transport Reviews, 12 (4) 323-341.

Source:Clarke, Philip; Davidson, Peter and Thomas, Adam (2008) Migrating Four-Step Models to na Activity Based Modelling

Framework in Practice, Presented at the ETC, http://www.etcproceedings.org/paper/migrating-4-step-models-to-an-activity-

based-modelling-framework-in-practice














http://www.ce.utexas.edu/prof/bhat/ABSTRACTS/TSHANDBK.pdf

http://www.etcproceedings.org/paper/migrating-4-step-models-to-an-activity-based-modelling-framework-in-practice























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