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 Practice Paper 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: Agents are:
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 Behavioral rules Decision making Memory
Agent-based modeling is a new approach to modeling systems comprised of autonomous agents. Each agent has a set of characteristics and rules governing its decisions-making capabilities. An agent is goal-oriented, having goals to achieve with respect to its behaviors. However, an agent is flexible, and has the ability to learn and adapt its behavior over times based on experience. This requires some form of knowledge.

23. Agent-based modeling Environment…
Moreover, an agent is situated, living in an environment in which...
Environment

24. Agent-based modeling … … …
...it interacts with other agents. Agents have some kind of protocols for interaction with other agents, and the capability to respond to the environment. Agents have the ability to recognize and distinguish the traits of other agents. …

25. Agent-based modeling
...it interacts with other agents. Agents have some kind of protocols for interaction with other agents, and the capability to respond to the environment. Agents have the ability to recognize and distinguish the traits of other agents.

26. Agent-based modeling
Finally, there might be various types of agents in an agent-based modelling systems, which are interacting among each other.

27. Agent-based modeling

28. Agent-based modeling
The actors of the (real) system modeled are represented at indivudual level and implement simple rules. The behavior of the system is not explictly modeled but emerges from the simulation

29. Agent-based modeling
The actors of the (real) system that is modeled are represented at indivudual level and implement simple rules. The behavior of the system is not explictly modeled but emerges from the simulation Simple rules implemented at the micro-level (individual) allows modeling complex behavior at the macro-level (system)

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

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 Trip distribution Mode choice
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 Generation35

36. Four Step Process – Trip Distribution36

37. Four Step Process – Mode Choice37

38. Four Step Process – Route Assignment38

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 required such 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 of the art 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
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. Introduction to 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
MATSim provides a toolbox to implement large-scale agent-based transport simulations. The toolbox consists of severel modules which can be combined or used stand-alone. Modules can be replaced by own implementations to test single aspects of your own work. Currently, MATSim offers a toolbox for demand-modeling, agent-based mobility-simulation (traffic flow simulation), re-planning, a controler to iteratively run simulations as well as methods to analyze the output generated by the modules.
Multiagent traffic simulation toolkit, fast microscopic transport model. Each traveller of the real system is modelled as an individual agent in the simulation. Supply side is modelled as fixed constraints of the system. The agents are able to take decisions, according to given information and coherently with a predetermined goal. Agents are able to learn.
Transport is a derived necessity in relation with the primary need of individuals to perform certain activities during the day.

51. Working with MATSim… Users Super-users Developers Black-box use
Add new features
Developers
Add new fundamental features

52. Working with MATSim… Users Super-users Developers Black-box use
Add new features
Developers
Add new fundamental features

53. MATSim Optimization Loop
Optimization is based on a co-evoluationary algorithm
Period-to-period replanning (typically day-to-day)
Each agent has total information and acts like homo economicus
Approach is valid for typical day situations

54. MATSim – Scenario Creation
A MATSim scenario contains some mandatory as well as some supplementary data structures
Mandatory
Network
Population
Supplementary
Facilities
Transit (Schedule, Vehicles)
Counts

55. 55
Road network
High resolution navigation network, including turning rules

56. 56
Day-plan
7:30
7:40
7:50
7:56
17:03
17:09
17:13
17:25
17:45
17:55
19:24
19:31

57. Speed vs Resolution physical (VISSIM) Resolution CA (TRANSIMS)
parallel
Q event
(MATSIM) Q
(Cetin)
Q event
(MATSIM)
meso
(METROPOLIS)
macro
(VISUM)
Speed

58. Facilities „Facilities“: Building location Activity options58
Facilities
„Facilities“:
Building location
Activity options
Capacity, Opening time
Source:
Enterprise register, Building register

59. Performance - Scenario59
Performance - Scenario
Transportation system in Switzerland
24 h of an average Work-day
5.99 Mio Agents
1.6 Mio Facilities for 1.7 Mio Activities (5 Types)
Navigation network with 1.0 Mio Links
4 Modes (others optional  i.e. shared modes)
22.2 Mio Trips
Routes-, Time-, (Subtour-)Mode- und „Location“-Choice
 One Iteration in ca. 4.5 hours
5.99 Mio Agenten = 88% der Gesamtbevölkerung
Verkehrsmittel: MIV, ÖV, Fahrrad, zuFuss, Mitfahren
22.2 Mio Wege = 3.7 Wege/Agent
Aktivitätentypen: zu Hause, Arbeit-Sektor 2, Arbeit-Sektor 3, Einkaufen, Freizeit, Schule-Kindergarten, Schule-Pflicht, Schule-Erweitert, Schule-Universität und Schule, und Übrige

60. Current research themes (I)
Simulation of public transport
Improved routing, multimodal simulation
Replanning improvement
Reduce the number of iterations, add other choice dimensions
Simulation of traffic lights and lanes
Focus on adaptive signal-control
Queue simulation
Parallelization
Modeling of vehicle fleet
Calculation of emissions
Electric vehicles
Simulation of the use of electric vehicles

61. Current research themes (II)
Agents coordination
Simulation of joint plans
Parking
Improvement of parking choice and search
Introduction of land-use
Integration with UrbanSim
Location choice of retailers
Addition of supply-side agents
Car-sharing
Car-sharing as an additional modal option
Weather impacts
Modeling of weather and climate change effects

62. Current scenarios Zurich and Switzerland Switzerland 7,6 Mio Agents
Navigation road network with 1 Mio Links
Berlin, Germany
Singapore
Gauteng, South-Africa
Sioux Falls, USA
Munich, Germany
Germany/Europe – Main road network
Padang, Indonesia
Tel-Aviv, Israel
Kyoto, Japan
Toronto, Canada
Caracas, Venezuela
Tel Aviv, Israel
Switzerland
Berlin and Munich, Germany
Toronto, Canada
Gauteng, South Africa

63. MATSim Singapore 60FPS NEW TITLES.mkv
(author: Pieter Fourie)

64. Possible Case Study Themes
Carsharing
Electric Vehicles
Weather

65. Questions
Laptop?
Windows
Mac

66. Additional Literature
Bhat, C. R., J. Y. Guo, S. Srinivasan and A. Sivakumar (2004) A comprehensive econometric microsimulator for daily activity-travel patterns, Transportation Research Record, 1894,
Kitamura, R. (1988) An evaluation of activity-based travel analysis, Transportation, 15 (1) 9–34.
Macal, C. M. and M. J. North (2005) Tutorial on agent-based modeling and simulation, Proceedings of the 37th Conference on Winter simulation, Orlando, December 2005.
Mahmassani, H. S., T. Hu and R. Jayakrishnan (1992) Dynamic traffic assignment and simulation for advanced network informatics, in N. H. Gartner and G. Improta (eds.) Compendium of the Second International Seminar on Urban Traffic Networks.
Ortuzar, J. D. D. and L. G. Willumsen (2006) Modelling Transport, John Wiley & Sons, Chichester.