1. Urban Planning Group Implementation of a Model of Dynamic Activity- Travel Rescheduling Decisions: An Agent-Based Micro-Simulation Framework Theo Arentze, Claudia Pelizaro, Harry Timmermans

2. Urban Planning Group Outline of the presentation Background and objectives The activity scheduling model The micro-simulation system A (limited) illustration Conclusions Future research

3. Urban Planning Group The activity-based modelling approach Activity-based models of travel demand consider travel as a derivative of activities individuals conduct in space and time The models aim to predict for an individual on a day: Which activities are conducted Where When For how long Transport mode In the context of a household Taking space-time constraints into account

4. Urban Planning Group Dynamic aspects of activity-travel choice Activity choice is state dependent Activities Day 1  Needs Day 2  Activities Day 2  …. Activity choice is adaptive in the short term Activities  Congestion  Activities  …. Individuals make choices under limited information and learn Activities  Experience  Activities  ….

5. Urban Planning Group Aim of this study Developing an agent-based micro-simulation system of dynamic activity-travel choice where agents Represent individuals Have limited knowledge of their environment Generate an activity schedule for each day Implement the schedule in space and time Experience congestion and adapt their schedules Update their needs and knowledge

6. Urban Planning Group The dynamics of the system Start of day Schedule Activities in time and space Environment Needs / Knowledge All agents Re-scheduling Interactions Learning

7. Urban Planning Group The (re-)scheduling model: requirements Activities and travel are scheduled on a continuous time scale The schedule meets a full set of scheduling constraints Needs for activities grow over time and are satisfied by activities depending on duration Scheduling decisions are based on heuristics rather than on exhaustive search

8. Urban Planning Group The scheduling model: Aurora Knowledge - Land-use system - Transport system Utility functions Dynamic constraints Scheduling heuristic Adapted Schedule Existing/empty Schedule Activity list and needs

9. Urban Planning Group Utility functions Utility of a schedule Need for an activity of type a Utility of an activity of type a

10. Urban Planning Group The scheduling heuristic Input is a consistent schedule and output is a consistent schedule with higher or equal utility Iteratively implements operations on an existing schedule until no further improvement is possible Operations are evaluated under optimal duration and timing choices

11. Urban Planning Group Operations considered Insert an activity Substitute an activity Reposition an activity Change location of an activity Include/remove a return-home trip between activities Change transport mode of a trip (chain) Example

12. Urban Planning Group Schedule implementation User can set the time-step size For each time time step 1.Update travel time on each link of the transport network 2.Simulate unforeseen events 3.Update the state of each individual

13. Urban Planning Group Updating the state of each agent Next time step An episode ends within the step Mismatch Solve conflict Re-schedule No Yes No Yes

14. Urban Planning Group Updating knowledge Travel time for each origin-destination traveled Location choice sets Knowledge decay Re-inforcement Exploration Default settings for activities Duration Location Transport mode Origin, Return home

15. Urban Planning Group Activity list Defined for each household Externally given and constant during the simulation Activity type is defined in terms of parameters Need/urgency function Utility function Default settings of each activity History of each activity (day when last performed)

16. Urban Planning Group Illustration of an application Model is component of GRAS: a DSS for green-space planning Study area is Eindhoven, approximately 200,000 inhabitants Limited activity list: Work activity Green activity Other activity (remaining time) Parameters were manually calibrated on activity diary data Population was synthesized using IPF

17. Urban Planning Group Green space infra structure data from Eindhoven

18. Urban Planning Group Spatial interaction between cells is established via road network linked to cell’s centroid

19. Urban Planning Group Parameter settings utility functions

20. Urban Planning Group Assumptions and scenario Location choice was based on an MNL model Transport mode options include Fast and Slow modes To simulate an increase in general time pressure on schedules of individuals the alpha of Other was increased from 600 to 700 minutes What is the impact on duration, frequency and utility of green space activities?

21. Urban Planning Group Some results Baseline Time-pressure scenario

22. Urban Planning Group Conclusions The system shows how an activity-based model can be used for micro-simulation of spatial behaviour The system takes into account: Urgency of activities as a function of time Time budgets and competition between activities Space-time constraints Ability to re-schedule activities Ability to learn from interaction with the environment The system allows users to analyse impacts of temporal as well as spatial variables on utilities and traffic flows

23. Urban Planning Group Future research Extension of the scheduling model Task allocation between members of the household Weekly-based activity scheduling Long-term household decisions Empirical estimation and calibration of the model system Develop test applications

24. Urban Planning Group Step 1 Back

25. Urban Planning Group Step 2 Back

26. Urban Planning Group Step 3 Back

27. Urban Planning Group Step 4 Back

28. Urban Planning Group Step 5 Back

29. Urban Planning Group Step 6 Back

30. Urban Planning Group Step 7 Back