A Primer for Agent-Based Simulation and Modeling …

1 AA Primer for Agent-Based Simulation and Modeling in Transportation ApplicationsThe Exploratory Advanced Research Programb ForewordAgent-based Modeling and Simulation (ABMS) methods have been applied in a spectrum of research domains. This Primer focuses on ABMS in the transportation interdisciplinary domain, describes the basic concepts of ABMS and the recent progress of ABMS in transportation areas, and elaborates on the scope and key characteristics of past Agent-Based transportation models, based on research results that have been reported in the literature.

2 Specifically, the objectives of this Primer are to explain the basic concept of ABMS and various ABMS methodologies scoped in the literature, review ABMS applications emerging in transportation studies in the last few decades, describe the general ABMS Modeling frameworks and commonly shared procedures exhibited in a variety of transportation applications, outline the strength and limitation of ABMS in various transportation applications, and demonstrate that ABMS exhibits certain comparable Modeling outcomes compared to classical approaches through a traveler s route choice decisionmaking process target audiences of this Primer are researchers and practitioners in the interdisciplinary fields of transportation, who are specialized or interested in social science models, behavioral models, activity-based travel demand models, lane use models, route choice models, human factors.

3 And artificial intelligence with applications in R. EvansDirector, Office of Safety Research and DevelopmentDebra S. ElstonDirector, Office of Corporate Research, Technology, and Innovation ManagementNoticeThis document is disseminated under the sponsorship of the Department of Transportation in the interest of information exchange. The Government assumes no liability for the use of the information contained in this Government does not endorse products or manufacturers. Trademarks or manufacturers names appear in this report only because they are considered essential to the objective of the Assurance StatementThe Federal Highway Administration (FHWA) provides high-quality information to serve Government, industry, and the public in a manner that promotes public understanding.

4 Standards and policies are used to ensure and maximize the quality, objectivity, utility, and integrity of its information. FHWA periodically reviews quality issues and adjusts its programs and processes to ensure continuous quality improvement. Cover photo illustration: Images and SukhanovaiTechnical Report Documentation PageiiiiiivTable of ContentsIntroduction Chapter 1. Agent-Based Modeling and Simulation Basic Concepts Backgrounds of ABMS The Need for ABMS Challenges in ABMS ABMS Applications An Example of ABMS in The Supply ChainChapter 2.

5 Agent-Based Methods in Human Decisionmaking Human Decision Behavior Modeling Framework Models of Learning Models of Interactions Chapter 3. Agent-Based Software Toolkits NetLogo MASON (MultiAgent Simulator of Neighborhoods) Swarm Repast (Recursive Porous Agent Simulation Toolkit) Ascape AnyLogic Chapter 4. Agent-Based Transportation Modeling Platforms Basic Structure of Agent-Based Transportation Platforms TRANSIMS (TRansportation Analysis and Simulation System) MATSIM (Multi-Agent Transport Simulation ) OpenAMOS (Open Activity-Mobility Simulator) SACSIM (Sacramento Activity-Based Travel Demand Simulation Model) ILUTE (Integrated Land Use, Transportation, Environment) SimAGENT (Simulator of Activities, Greenhouse Emissions, Networks, and Travel) 1457991112141515161820212223242426272931 32343436vChapter 5.

6 Agent-Based : A System Paradigm Applied in the 38 Transportation Field Multi-Agent System (MAS) 39 A Computational Method for the Distributed Systems MAS Practiced in Transportation Problems 41 MAS Applied in Traffic Management 42 MAS Applied in Dynamic Route Guidance 43 MAS Applied in Signal Control 44 Summary 45 Chapter 6.

7 Agent-Based Modeling for Route Choice Behaviors 46An Illustrative Example Motivation 47 An Example Applying ABMS Model to Route Choice Behavior 49 Model Experiment Design 51 Experimental Results 52 Concluding Remarks 56 Conclusions 58 Acknowledgements 60 References 6238394142434445464749515256586062viList of FiguresFigure 1.

8 An agent. 6 Figure 2. Example of Game of Life. 7 Figure 3. A bottom-up approach to ABMS. 8 Figure 4. Simple local rules result in complex system behaviors. 8 Figure 5. Autonomous agents interact over a self-organizing 10social network in a Repast Simulation of social influence. Figure 6. A typical supply chain network and its agents. 12 Figure 7. Model structure of an Agent-Based Simulation approach. 28 Figure 8. Model diagram in TRANSIMS.

9 30 Figure 9. Simple network with three links. 52 Figure 10. Time plots of flow and travel time (Experiment I). 52 Figure 11. Flows on each route (Experiment II). 54 Figure 12. Travel times on each route (Experiment II). 54 Figure 13. Flows on each route (Experiment III). 55 Figure 14. Travel times on each route (Experiment III). 55 List of TablesTable 1. Agent-Based Modeling Applications.

10 11 Table 2. Comparison of ABMS Software Toolkits. 20viiList of Symbols Greater than Less than or equal to Not equal to Proportional to Summation symbol Product symbol Dirichlet distribution parameter set Beta function Vector of minimum travel time variables