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A Primer for Agent-Based Simulation and Modeling in Transportation Applications

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A Primer for Agent-Based Simulation and Modeling in Transportation Applications The Exploratory Advanced Research Program A Primer for Agent-Based Simulation and Modeling in Transportation Applications a Foreword Agent-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. 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 example. The 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, and artificial intelligence with applications in transportation. Monique R. Evans Debra S. Elston Director, Office of Safety Director, Office of Corporate Research, Research and Development Technology, and Innovation Management Notice This document is disseminated under the sponsorship of the U.S. Department of Transportation in the interest of information exchange. The U.S. Government assumes no liability for the use of the information contained in this document. The U.S. 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 document. Quality Assurance Statement The Federal Highway Administration (FHWA) provides high-quality information to serve Government, industry, and the public in a manner that promotes public understanding. 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. b Cover photo illustration: © www.istockphoto.com/Getty Images and www.photos.com/Viktoriya Sukhanova Technical Report Documentation Page i ii iii Table of Contents Introduction 1 Chapter 1. Agent-Based Modeling and Simulation 4 Basic Concepts 5 Backgrounds of ABMS 7 The Need for ABMS 9 Challenges in ABMS 9 ABMS Applications 11 An Example of ABMS in The Supply Chain 12 Chapter 2. Agent-Based Methods in Human Decisionmaking 14 Human Decision–Behavior Modeling Framework 15 Models of Learning 15 Models of Interactions 16 Chapter 3. Agent-Based Software Toolkits 18 NetLogo 20 MASON (MultiAgent Simulator of Neighborhoods) 21 Swarm 22 Repast (Recursive Porous Agent Simulation Toolkit) 23 Ascape 24 AnyLogic 24 Chapter 4. Agent-Based Transportation Modeling Platforms 26 Basic Structure of Agent-Based Transportation Platforms 27 TRANSIMS (TRansportation Analysis and SIMulation System) 29 MATSIM (Multi-Agent Transport Simulation) 31 OpenAMOS (Open Activity-Mobility Simulator) 32 SACSIM (Sacramento Activity-Based Travel Demand Simulation Model) 34 ILUTE (Integrated Land Use, Transportation, Environment) 34 SimAGENT 36 (Simulator of Activities, Greenhouse Emissions, Networks, and Travel) iv Chapter 5. Agent-Based: A System Paradigm Applied in the 3838 Transportation Field Multi-Agent System (MAS)— 3939 A Computational Method for the Distributed Systems mas Practiced in Transportation Problems 4141 mas Applied in Traffic Management 42 mas Applied in Dynamic Route Guidance 43 mas Applied in Signal Control 44 Summary 45 Chapter 6. Agent-Based Modeling for Route Choice Behaviors— 4646 An Illustrative Example Motivation 4747 An Example Applying ABMS Model to Route Choice Behavior 4949 Model Experiment Design 5151 Experimental Results 52 Concluding Remarks 56 Conclusions 58 Acknowledgements 60 References 6262 v List of Figures Figure 1. 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 10 social 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. 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 Tables Table 1. Agent-Based Modeling Applications. 11 Table 2. Comparison of ABMS Software Toolkits. 20 vi List of Symbols Greater than Less than or equal to Not equal to Proportional to Σ Summation symbol Π Product symbol Dirichlet distribution parameter set Beta function Vector of minimum travel time variables Binary variable, equal to 1 if the traveler perceives that the th route takes the minimum travel time ( ) on the th day, and 0 otherwise Natural exponential constant, equal to 2.71828 Expectation Route Choice Threshold Element of Function Proportational value of travelers who replan their trips in TRANSIMS Data likelihood function Route Day Gumbel distribution parameter Subjective probability that the route takes the minimum travel time on the th day Vector of subjective probabilities Number of iterations of the outer-loop in DaySim Travel time of jth route on nth day Minimum travel time of all routes on nth day Perception error term vii List of Acronyms and Abbreviations General Terms 2D two dimensional 3D three dimensional ABMS agent-based modeling and simulation ABS agent-based simulation ABSS agent-based social simulation ACT-R Adaptive Control of Thought—Rational aDAPTS Agent-based Dynamic Activity Planning and Travel Scheduling AI artificial intelligence API application programming interface BBN Bayesian belief network BDI belief–desire–intention BPR Bureau of Public Roads CARTESIUS Coordinated Adaptive Real-Time Expert System for Incident Management in Urban Systems CAS complex adaptive systems CEMDAP Comprehensive Econometric Microsimulator for Daily Activity Travel Patterns CEMSELTS Comprehensive Econometric Microsimulator for Socio- economics, Land Use, and Transportation System CTMRGS Cooperative Traffic Management and Route Guidance System DAI distributed artificial intelligence DaySim Person Day Activity and Travel Simulator DTA dynamic traffic assignment DYNASMART-P Dynamic Network Assignment-Simulation Model for Advanced Roadway Telematics (Planning version) DynusT Dynamic Urban Systems for Transportation EMME equilibre multimodal, multimodal equilibrium FIPA Foundation for Intelligent Physical Agents GA genetic algorithm viii GMU George Mason University HOV high-occupancy vehicle HUT Helsinki University of Technology ILUTE Integrated Land Use, Transportation, Environment ISP information service provider MABS multi-agent based simulation MALTA Multiresolution and Loading of Transportation Activities MAS multi-agent systems MASON Multiagent Simulator of Neighborhoods MATSim Multi-Agent Transport Simulation Toolkit MITSIM MIcroscopic Traffic SIMulator NASA National Aeronautics and Space Administration NP-hard Non-deterministic Polynomial-time hard O-D origin–destination OpenAMOS Open Activity-Mobility Simulator PCATS Prism Constrained Activity Travel Simulator PopGen population generator PopSyn population synthesizer Repast Recursive Porous Agent Simulation Toolkit RL reinforcement learning SAAS social aspects of agent systems SACSIM Sacramento Activity-Based Travel Demand Simulation Model SimAGENT Simulator of Activities, Greenhouse Emission, Networks, and Travel SOCSIM social simulation SOV single-occupant vehicle TASHA Travel Activity Scheduler for Household Agents TRACK-R TRaffic Agent City for Knowledge-Based Recommendation TRANSIMS Transportation Analysis and Simulation System TRYS Tráfico, Razonamiento y Simulación TRYSA2 Tráfico, Razonamiento y Simulación Autonomous Agents UE user equilibrium ix x Introduction gent-based modeling and simulation The goal of this primer is to review the (ABMS) has been widely applied historical aspects and the ongoing Aacross a spectrum of disciplines by developments of ABMS in the interdisciplinary both researchers and practitioners. Examples transportation areas, summarizing and of these disciplines include ecology, biology, clarifying the scope and key characteristics business, economic science, computer of past agent-based studies and to shed light simulation, social sciences, political science, on future potential research. Another policy, and military studies. Knowledge and scientific focus of this primer is to document applications of ABMS continue to expand a research effort that attempts to establish and accumulate through rapid and in-depth the relationship between the classical and research and development. ABMS-based route
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