Introduction to Activity-Based Modeling Joshua Auld Transportation Research and Analysis Computing Center Argonne National Laboratory November 28, 2011

Travel Demand Modeling Current and Future Applications Roadway planning / new construction Decreasing funding for new roads Environmental/Air quality impacts More important after ISTEA, SAFETEA-LU Conformity analysis Travel Demand Management Large collection of strategies Increase efficiency of system (TSM) Change user behavior (TDM) Congestion pricing, ride-share program, etc. ITS / Operations

Activity based model theory Travel is derived from participation in activities Not accounted for in 4-step models Time and space between the activities generates travel Activity participation is modeled at household/individual level Microsimulation model Individual’s activity participation constrained by: Time availability Location Institutional characteristics (operating hours, etc.) Household considerations Activity patterns are generated by individuals which satisfy these constraints and meet some other criteria

Activity based model theory (cont.) Figure shows a daily activity pattern graph (for travel in 1-dimension) Vertical lines represent activities Diagonal lines are travel episodes Explicit representation of time of occurrence for all travel episodes, linked to associated activities ABM generates an activity pattern for modeled individuals Know when and where they are traveling at all times Home Shopping Work Lunch Hagerstrand (1972) – Time geography – tracing of human activity through time-space subject to the constraints placed on those activities (biological, institutional, spatio-temporal). Gave rise to the time-space prism (which is another form that some ABMs take – assumes fixed activities, and flexible activities are scheduled in the available prism). This representation above is a major advance over four step modeling. There is no gravity modeling/calibration procedure to estimate flows between zones – flow is known precisely from the results of the activity pattern. Home

Activity based model theory (cont.) It is the goal of an activity based model to develop an activity schedule that: Satisfies all of the given constraints, and Satisfies some schedule optimization criteria The models take as inputs: Individual/household attributes Environment attributes (land-use, activity locations, transportation networks, etc.) Attributes and actions of other individuals Then use these inputs to model a series of choices: What activities to schedule When to schedule them Where to schedule them Who to go with How to get there How long to stay How these choices are modeled depends on the type of model used Not all models directly estimate all of the choices, and all of the models estimate them in different ways and in different orders.

Approaches to Activity Based Modeling Two general approaches to modeling activity participation choices Econometric models Rule-based models In econometric type models: Models are usually tour based – select activity tours from predefined set Utility maximization to model pattern formation In rule-based models: Computational process models or other derived decision rules Bottom-up approach to schedule building

Example of a nested logit model for decision making Econometric models Decisions modeled using discrete choice Usually nested logit models Model sequence: Number and type of tours -> Stops in each tour -> mode choice for tour, etc. Example of a nested logit model for decision making This example logit decision structure first determines the number of maintenance stops generated, assigns them to household individuals, then allocates a vehicle for each stop (activity). This is the first stage of a two-stage model, where the second stage then uses a NL model to form tours for each individual. Source: Wen and Koppelman (2000)

Econometric models (cont.) Econometric models are generally tour-based Model the number and purpose of activity tours Chose from a set of pre-defined tours Examples: Bowman-Ben Akiva (1996) and derivatives (DAYSIM) PB Consult models (MORPC, NYMTC, etc.) Jakarta Model - Yagi and Mohammadian (2008) Wen and Koppelman (2000) Tours are pre-defined, since the choice set with no predefined tours would be too large – combinatorial problem Ex. 6 activity types – how many different patterns can be created? Source: Wen and Koppelman (2000)

Criticisms of econometric models Unrealistic behavioral assumptions Utility maximization in decision making by individuals Artificially restrict activity scheduling to predefined choices Can not represent full range of scheduling behavior Reduces number of choices to be modeled, i.e. combinatorial problem No consideration of dynamics (full day selected at one time) Limitations on time-scale of analysis Usually discretized to time-of-day periods See the Garling, Kwan paper for critique of utility maximization in scheduling. Basically, the choice set is too large, so in reality people develop heuristics. Do not actually maximize their utility. EXAMPLE: Mode Choice to work: My wife has the car and there’s no parking at work, so I’ll take the train. This is a choice heuristic, rather than calculating utility over every choice facet and arriving at optimal solution. These models do not work well to capture the process of scheduling – which may be important in their responsiveness to policy changes.

Rule based model overview Create activity schedules based on heuristics Computational Process Models or other derived rules Attempt to model the underlying process of activity scheduling Examples: SCHEDULER (Garling 1989) AMOS (Pendyala 1995) ALBATROSS (Arentze, Timmermans 2000) TASHSA (Roorda, Doherty, Miller 2005) ADAPTS (Auld and Mohammadian 2009)

Computational process models Production system (Newell, Simon 1972) for modeling decision making behavior Rules as condition-action pairs Describe how information relating to a choice is searched Choice made depends on current information acting on set of rules Allows incorporation of learning Representation of process of decision making Production systems often modeled as Decision trees (ALBATROSS) Heurstic rules (SCHEDULER, TASHA) In operational models, none of the models are true CPMs, since learning is not included in peoples behavior. This was left as future refinements of the operational models. These are simplifications of true CPMs, due to limited modeling data.

Data requirements Rule-based activity scheduling models have extensive data needs: Synthesized population Socio-demographic characteristics of individuals Synthesized city/environment Activity locations/operating hours Roadway and transit networks Land use variables Activity diary/scheduling data Activity participation/generation rates Conflict resolution rules Planning process data Executed schedules for model validation It may be possible that activity diary data will not be needed depending on the results of transferability analysis.

ALBATROSS model overview Arentze and Timmermans (2000). Rule-based model for predicting: Activity participation, location, timing, duration, party and travel mode choice Includes household, institutional and spatio-temporal constraints Decision rules modeled using CHAID decision trees at each step Derived from activity survey data Model attempts to simulate daily activity schedule creation for individuals Long-term decisions considered fixed Household interactions modeled Starts with an assumed schedule skeleton Represents routine, fixed activities Considered the highest priority Sequentially attempts to add new activities in order of assumed priority Created to model travel in the Netherlands.

ALBATROSS model Scheduling process (continued) Ovals represents decision steps In ALBATROSS: A skeleton schedule first constructed containing routine activities Activity agenda is created Finally, activities added to the schedule from the activity agenda Source: Arentze, Timmermans (2000).

TASHA model overview Roorda, Doherty, Miller (2005) Activity scheduling model for Toronto area Based on a 1996 household travel survey Activities generated based on travel survey Attributes assigned using choice models Entirely rule-based Activities fit to Project Agendas, then Schedule Activities added to schedule based on assumed rules Heuristics represent schedule adjustment, conflict resolution Decision rules not well represented in the model I.E. mode choice and activity location do not figure into the scheduling process

TASHA model Scheduling process Generate activity episodes Activities generated based on trip diary data Frequency, start time and duration chosen from feasible portion of probability distributions Fill each project agenda Activities added to respective project agendas to form provisional schedules within each project Add activities to persons schedule Activities added to schedule in order of priority Priority assumptions: Work/School > Other > Shopping and Joint activities > Individual activities Adjust activities until conflicts removed Generate activity Fits in agenda No Yes Add to project agendas Next activity Next activity chosen is the highest priority activity remaining in project agendas. Activity episodes are generated using probability distributions for various groups classified by age, employment status, occupation, gender, etc. based on the type of activity Number of activities generated of each type is chosen from the activity frequency distribution Frequency-start time joint PDF and start time-duration PDF. Fits in schedule No Yes Add to schedule

SIMTRAVEL Integration of OpenAMOS ABM with MALTA DTA Prism-constrained activity-travel scheduling (PCATS) – Kitamura et al. (1997) Fill in prism gaps sequentially until no time-remaining between fixed activities Source: SimTRAVEL website - http://urbanmodel.asu.edu/intmod/presentations.html

Research Gaps in ABM Simplification of scheduling process Priority rules, fixed order of choices Short-term, diary data used Most rule-based models implemented using 1-2 day diary data Limited integration with traffic simulation Static assignment for fixed time-periods Ad hoc interoperability between model systems Activity Generation/Planning/Scheduling Dynamics???

Scheduling Order Example A) Impulsive Shop - Preplan Eat Out B) Preplan Shop - Impulsive Eat out Before Change Before Change After Change After Change This shows an example where the original activity pattern is the same in both situations (A and B) – home->shop->lunch with friends->home The only difference is that in A the shopping trip is impulsive on the way to lunch with friends. In B the lunch is impulsively decided on during the shopping trip – calls friends while out shopping. So patterns are the same only planning is different. Then there is a change in policy (or something else) which causes the store to be unavailable – observe different responses In A, the shopping trip is just skipped and person goes to lunch as planned. (7 trips reduced to 6) In B, the shopping trip is diverted to a different available store, not near restaurant so no trip with friends is planned (7 trips reduced to 2). Point is that policies, system changes, etc. may have different impacts depending on planning context

ADAPTS - Introduction ADAPTS activity-based model: Simulation of how activities are planned and scheduled Extends concept of “planning horizon” to activity attributes Time-of-day, location, mode, party composition Fits within overall framework of integrated land-use transportation model Constraints from long-term simulation (land-use model) Combined with route choice and traffic simulation Core concept: Universal set of activity planning / scheduling processes represented by heuristics and/or models Outcomes constrained by local context

ADAPTS Simulation Framework Information Flow Simulation Flow Initialize Simulation Initialize World Synthesize Population Generate routines For each timestep Household Schedule Household Planning Household Memory Land Use Institutional Constraints This slide shows an overview of the simulation process, so after the simulation is initialized (note that we are currently using 15min timesteps for 4 weeks of activity scheduling) At each timestep: Household activities are generated/planned/scheduled/executed (as shown in the next slide) Individual activities are generated/planned/scheduled/executed (again in next slide) Any activities which are executed are output to the traffic assignment Results of traffic used to update network knowledge for next timestep Go to next timestep Individual Schedules Network LOS Individual Memory Individual Planning Social Network Write Trip Vector Traffic Assignment

ADAPTS Planner/Scheduler At timestep t Handles at each timestep: Generation Planning Scheduling Each step can occur at different times for same activity Core of the framework is the Attribute Plan Order Model Activity Generation Generate new activity Yes Attribute Planning Order model No No Update existing activity(s) Set Plan Flags: (Ttime,Tloc, etc.) Yes Activity Planning t = Ttime t = Twith t = Tloc t = Tmod Time-of-Day Party Destination choice Mode Choice This slide is the most important in the presentation Stress the use of three core steps in the simulation – Generation, Planning and Scheduling, as opposed to just Generation and Scheduling as usually done (where the attributes are determined at generation time) Allows for more behaviorally realistic responses Planned Activity Schedule Activity Scheduling Resolve Conflicts Yes Conflict Resolution Model Decision Logical test Model Simulated events No Execute activity Executed Schedule

Activity Plan Order Model Decision Example: Activity Generation Activity Plan Order Model Plan Work Location T1 Plan new activity Ttime Tloc Twho-with Tmode Ttime Plan time-of-day Tloc Plan location Twho = Tmode Plan mode and who-with Texec Execute Activity Ttime Tloc Tmode/who Texecute Simulation Time Time: Shop Time: ? Loc: ? Mode: ? At Home Time: 12:00 AM – 8:00 AM Loc: Home Mode: None Work Time: 8:00 AM – 4:00 PM Loc: HOME Mode: None Work Time: 8:00 AM – 4:00 PM Loc: ? Mode: ? Shop Time: 4:00 – 5:00 Loc: Mall Mode: Auto ? Schedule ?

References Arentze, T. and H. Timmemans (2000). ALBATROSS – A Learning Based Transportation Oriented Simulation System. European Institute of Retailing and Services Studies (EIRASS), Technical University of Eindhoven. Auld, J. A., and A. Mohammadian (2009). Framework for the development of the Agent-based Dynamic Activity Planning and Travel Scheduling (ADAPTS) model. Transportation Letters: The International Journal of Transportation Research, 1 (3), 243-253. Bowman, J.L. and M.E. Ben-Akiva (2001). Activity-Based Disaggregate Travel Demand Model System with Activity Schedules. Transportation Research Part A. 35, 1-28. Kitamura, R., C. Chen, C., and Pendyala, R.M. (1997) Generation of synthetic daily activity-travel patterns. Transportation Research Record, 1607, 154-162. Pendyala, R.M.; R. Kitamura, A. Kikuchi, T. Yamamoto, S. Fujii (2005). Florida Activity Mobility Simulator: Overview and Preliminary Validation Results. Transportation Research Record: Journal of the Transportation Research Board, No. 1921, 123-130. Roorda, M.J., S.T. Doherty and E.J. Miller (2005). Operationalising Household Activity Scheduling Models: Addressing Assumptions and the Use of New Sources of Behavioral Data. Integrated Land-use and Transportation Models: Behavioural Foundations, M. Lee-Gosselin and S.T. Doherty (eds), Oxford: Elsevier, pp. 61-85. Yagi, S. and A. Mohammadian. (2008). Modeling Daily Activity-Travel Tour Patterns Incorporating Activity Scheduling Decision Rules, Transportation Research Record: Journal of the Transportation Research Board, No. 2076, TRB, National Research Council, Washington D.C., pp. 123-131.

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