Lecture: Agent Based Modeling in Transportation

1. Lecture:Agent Based Modeling in Transportation Lecturers: Dr. Francesco Ciari Dr. Rashid Waraich Assistant: Patrick Bösch Autumn Semester 2014

2. Lecture I September 16th 2014

3. Lecture Structure • Theory • Modeling Transport • Agent Based Modeling • Multi Agent Transport Simulation (MATSim) • Practice • Case studies (individual or in small groups) • Paper • The expected output is a case study report in the form of a proper scientific paper

4. Modeling transport(ation)

5. Modeling transportation Transportation: ??? Model: ???

6. Modeling transportation Transportation: is the movement of people, animals and goods from one location to another (Wikipedia) Model: ???

7. Modeling transportation Transportation: is the movement of people, animals and goods from one location to another (Wikipedia) Model: A simplified representation of a part of the real world which concentrates on certain elements considered important for its analysis from a particular point of view (Ortuzar and Wilumsen, 2006)

8. What for? • Planning (i.e. infrastructure, systems) • Policy making Type of model depends on: • Decision making context • Accuracy required • Data • Resources

9. Activity based paradigm

10. Transportation Transportation: is the movement of people, animals and goods from one location to another

11. Transportation Transportation: is the movement of people, animals and goods from one location to another

12. Transportation Transportation: is the movement of people, animals and goods from one location to another What are the reasons of this movement?

18. Activity approaches Activity approaches means «The consideration of revealed travel patterns in the context of a structure of activities, of the individual or household, with a framework emphasizing the importance of time and space constraints. (Goodwin, 1983)

19. Activity approaches Allow looking at important aspects of travel like: • Activity Generation • In home/out of home activities (patterns, substitution) • Constraints • Scheduling • Social Networks (Kitamura, 1988)

20. Modeling with agents

21. What is an agent? • An agent: • Has a set of attributes/characteristics • Follows given behavioral rules • Has decision making capability • Is goal oriented • Acts in an environment and interacts with other agents • Is autonomous • Can learn • Agents are: • Heterogeneous • Attributes can change dynamically (Source: Macal and North, 2005)

22. Agent Attributes Behavioralrules Decisionmaking Memory

23. Agent-basedmodeling … … … Environment

24. Agent-based modeling … … … … … … … … … … … …

25. Agent-based modeling

26. Agent-basedmodeling

27. Agent-basedmodeling

28. Agent-basedmodeling The actorsofthe (real) systemmodeledarerepresentedatindivuduallevelandimplement simple rules. The behaviorofthesystemis not explictlymodeled but emergesfromthesimulation

29. Agent-basedmodeling The actorsofthe (real) systemthatis modeled arerepresentedatindivuduallevelandimplement simple rules. The behaviorofthesystemis not explictlymodeled but emergesfromthesimulation Simple rulesimplementedatthemicro-level (individual) allowsmodelingcomplexbehavioratthemacro-level (system)

30. Pros and cons Cons: • Data hungry • Skilled users Pros: • Models Individuals • Agents heterogeneity • Emergent behavior • Can deal with complexity

31. Why Agent-Based Modeling is becoming popular? • Increasingly complex world • Availability of high resolution level data • Computer power

32. What about transportation?

33. Traditional Modeling Approach • Four steps model

34. Four Step Process • Trip generation • Define number of trips from and to each zone. • Trip distribution • Define for each zone where its trips are coming from and going to. • Mode choice • Define transport mode for each trip. • Route assignment • Assign a path to each route. 34

35. Four Step Process – Trip Generation 35

36. Four Step Process – Trip Distribution 36

37. Four Step Process – Mode Choice 37

38. Four Step Process – Route Assignment 38

39. Four Step Process – Facts • Traditional approach in transport planning • Simple, well known and understood • Sequential execution • Feedback not required, but desirable • Aggregated Model • No individual preferences of single travelers • Only single trips, no trip chains • Static, average flows for the selected hour, e.g. peak hour 39

40. Iterative Four Step Process • Improvement of the traditional approach • Iterations allow feedback to previous process steps • Still an aggregated model 40

41. Modern Modeling Approaches • Activity-based demand generation • Dynamic traffic assignment

42. Activity-based demand generation • Models the traffic demand on an individual level. • Based on a synthetic population representing the original population. • For each individual a detailed daily schedule is created, including descriptions of performed… • …activities (location, start and end time, type) • …trips (mode, departure and arrival time) • Activity chains instead of unconnected activities and trips. • Represents the first three steps of the 4 step process.

43. Activity-based demand generation • Spatial resolution can be increased from zone to building/coordinate. • High resolution input data is requiredsuch as… • …the coordinates of all locations where an activity from type X can be performed. • …the capacity of each of this locations. • Examples of activity-based models • ALBATROSS (A Learning-Based Transportation Oriented Simulation System) • TASHA (Travel Activity Scheduler for Household agents)

44. Dynamic Traffic Assignment • Supports detailed description of the demand (persons/households). • Based on trip chains instead of single trips. • Time dependent link volumes replace static traffic flows. • Spatial and temporal dynamics are supported. • Represents the fourth step of the 4 step process.

45. Dynamic Traffic Assignment • Typical implementations are simulation based. • Iterative simulation and optimization of traffic flows in a network on an individual level. • Examples of DTA implementations • DYNAMIT (Ben-Akiva et.al.) • DYNASMART (Mahmassani et.al.) • VISSIM (PTV; only small scenarios) • TRANSIMS

46. State oftheart Fully agent-based approach • Combination of activity-based demand generation and dynamic traffic assignment

47. Fully Agent-based Approach • Combines the benefits of activity-based demand generation and dynamic traffic assignment. • Replaces all steps of the four step process. • During the whole process, people from the synthetic population are maintained as individuals. Individual behavior can be modeled!

48. Macro-Simulation vs. Micro-Simulation • Macro-Simulation • Based on aggregated data • Flows instead of individual movement • Often planning networks • Micro-Simulation • Population is modeled as a set of individuals • Traffic flows are based on the movement of single vehicles (or agents) and their interactions • Various traffic flow models, e.g. cellular automata model, queue model or car following model • Often high resolution networks (e.g. in navigation quality) 48

49. Introductionto MATSim

50. MATSim at a glance • Implementation of a fully agent-based approach as part of a transport modeling tool • Disaggregated • Activity-based • Dynamic • Agent-based • Open source framework written in java (GNU License) • Started ~10 years ago, community is still growing • Developed by Teams at ETH Zurich, TU Berlin and senozon AG • www.matsim.org