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Day-to-day traffic dynamics considering social interaction: From individual route choice behavior to a network flow model
Institution:1. Institute of Systems Engineering, College of Management and Economics, Tianjin University, No. 92 Weijin Road, Nankai District, Tianjin 300072, China;2. MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China;1. School of Management, Huazhong University of Science and Technology, Luoyu Road 1037, Wuhan, PR China;2. Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China;3. College of Computer Science, Inner Mongolia University, Hohhot 010021, PR China;1. School of Intelligent Systems Engineering, Sun Yat-Sen University, Guangzhou, China;2. 4Paradigm Inc, Beijing, China;3. School of Automation, Nanjing University of Science and Technology, Nanjing, China;4. Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region;5. School of Integrated Innovation, Chulalongkorn University, Bangkok, Thailand;6. Research Institute of Smart City, Shenzhen University, Shenzhen, China
Abstract:Social interaction is increasingly recognized as an important factor that influences travelers’ behaviors. It remains challenging to incorporate its effect into travel choice behaviors, although there has been some research into this area. Considering random interaction among travelers, we model travelers’ day-to-day route choice under the uncertain traffic condition. We further explore the evolution of network flow based on the individual-level route choice model, though that travelers are heterogeneous in decision-making under the random-interaction scheme. We analyze and prove the existence of equilibrium and the stability of equilibrium. We also analyzed and described the specific properties of the network flow evolution and travelers’ behaviors. Two interesting phenomena are found in this study. First, the number of travelers that an individual interacts with can affect his route choice strategy. However, the interaction count exerts no influence on the evolution of network flow at the aggregate-level. Second, when the network flow reaches equilibrium, the route choice strategy at the individual-level is not necessarily invariable. Finally, two networks are used as numerical examples to show model properties and to demonstrate the two study phenomena. This study improves the understanding of travelers’ route choice dynamics and informs how the network flow evolves under the influence of social interaction.
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