Agent-based en-route diversion: Dynamic behavioral responses and network performance represented by Macroscopic Fundamental Diagrams |
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| Institution: | 1. Department of Civil and Environmental Engineering, University of Maryland, College Park, MD 20742, United States;2. College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, PR China;3. International Aviation Division, Institute of Air Transport, China Academy of Civil Aviation Science and Technology, PR China;4. Department of Civil Engineering, Tsinghua University, Beijing 100084, PR China;1. School of Transportation, Southeast University, Nanjing, Jiangsu 210096, PR China;2. Department of Civil and Environmental Engineering, National University of Singapore, Singapore 117576, Singapore;3. School of Traffic and Transportation Engineering, Changsha University of Science & Technology, Changsha, Hunan 410004, PR China;1. Erasmus School of Economics, Erasmus University, The Netherlands;2. Rotterdam School of Management, Erasmus University, The Netherlands;3. Netherlands Railways, Utrecht, The Netherlands;4. VU University, Amsterdam, The Netherlands;1. Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong Special Administrative Region;2. School of Intelligent Systems Engineering, Sun Yat-Sen University, Guangzhou, China;1. Department of Civil and Environmental Engineering, University of Maryland, College Park, MD 20742, USA;2. Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI 48109, USA;3. School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85281, USA;1. College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China;2. Zhejiang University/the University of Illinois Urbana-Champaign Institute (ZJU-UIUC), Zhejiang University, Haining 314400, China;3. Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies, Hangzhou 310007, China |
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| Abstract: | This paper focuses on modeling agents’ en-route diversion behavior under information provision. The behavior model is estimated based on naïve Bayes rules and re-calibrated using a Bayesian approach. Stated-preference driving simulator data is employed for model estimation. Bluetooth-based field data is employed for re-calibration. Then the behavior model is integrated with a simulation-based dynamic traffic assignment model. A traffic incident scenario along with variable message signs (VMS) is designed and analyzed under the context of a real-world large-scale transportation network to demonstrate the integrated model and the impact of drivers’ dynamic en-route diversion behavior on network performance. Macroscopic Fundamental Diagram (MFD) is employed as a measurement to represent traffic dynamics. This research has quantitatively evaluated the impact of information provision and en-route diversion in a VMS case study. It proposes and demonstrates an original, complete, behaviorally sound, and cost-effective modeling framework for potential analyses and evaluations related to Advanced Traffic Information System (ATIS) and real-time operational applications. |
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| Keywords: | En-route diversion Agent-based simulation Dynamic traffic assignment Macroscopic Fundamental Diagram |
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