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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
Phd in Transportation / Simulation of Land Use -Transportation Systems 2/45
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).
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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
Phd in Transportation / Simulation of Land Use -Transportation Systems 4/45
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
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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)
Phd in Transportation / Simulation of Land Use -Transportation Systems 6/45
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.
Phd in Transportation / Simulation of Land Use -Transportation Systems 7/45
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).
Phd in Transportation / Simulation of Land Use -Transportation Systems 8/45
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
Phd in Transportation / Simulation of Land Use -Transportation Systems 9/45
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
Phd in Transportation / Simulation of Land Use -Transportation Systems 10/45
Hagerstrand Space Time Prism
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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/
Phd in Transportation / Simulation of Land Use -Transportation Systems 12/45
Space time representation of travel and
activities
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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
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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).
Phd in Transportation / Simulation of Land Use -Transportation Systems 15/45
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
Phd in Transportation / Simulation of Land Use -Transportation Systems 16/45
• 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
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Comparison between 4 step and activity
based model structures
Source:Clarke, Philip; Davidson, Peter and Thomas, Adam (2008)
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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.
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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
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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)
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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.
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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
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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
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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
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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)
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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
Phd in Transportation / Simulation of Land Use -Transportation Systems 27/45
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…
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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
Phd in Transportation / Simulation of Land Use -Transportation Systems 29/45
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
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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
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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
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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)
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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.
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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
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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
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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.
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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)
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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
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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
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Main problems and future directions
There is still a lack of good understanding on the decision
mechanisms underlying activity patterns
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Moving from4 step to Activity based
models
Source:Clarke, Philip; Davidson, Peter and Thomas, Adam (2008)
Phd in Transportation / Simulation of Land Use -Transportation Systems 42/45
Moving from4 step to Activity based
models
Source:Clarke, Philip; Davidson, Peter and Thomas, Adam (2008)
Phd in Transportation / Simulation of Land Use -Transportation Systems 43/45
Moving from4 step to Activity based
models
Source:Clarke, Philip; Davidson, Peter and Thomas, Adam (2008)
Phd in Transportation / Simulation of Land Use -Transportation Systems 44/45
Applications of activity based models
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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