A multiclass cell transmission model for shared human and autonomous vehicle roads |
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| Institution: | 1. Department of Civil and Environmental Engineering, Institute of Transportation Studies, 4000 Anteater Instruction and Research Building, University of California, Irvine, CA 92697-3600, United States;2. Universite Paris-Est, IFSTTAR/COSYS/GRETTIA, F-77447 Champs-sur Marne Cedex, France;1. Institute for Transport Studies, University of Leeds, Leeds, England, United Kingdom;2. Ulster Business School, University of Ulster, Newtownabbey, Northern Ireland, United Kingdom;3. Dept. of Industrial Engineering and Management, Ben-Gurion University of the Negev, Israel;1. Department of Industrial Engineering, Tsinghua University, Beijing 100084, China;2. Gulf Coast Center for Evacuation and Transportation Resiliency, Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, LA 70803, United States;3. School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW 2052, Australia |
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| Abstract: | Autonomous vehicles have the potential to improve link and intersection traffic behavior. Computer reaction times may admit reduced following headways and increase capacity and backwards wave speed. The degree of these improvements will depend on the proportion of autonomous vehicles in the network. To model arbitrary shared road scenarios, we develop a multiclass cell transmission model that admits variations in capacity and backwards wave speed in response to class proportions within each cell. The multiclass cell transmission model is shown to be consistent with the hydrodynamic theory. This paper then develops a car following model incorporating driver reaction time to predict capacity and backwards wave speed for multiclass scenarios. For intersection modeling, we adapt the legacy early method for intelligent traffic management (Bento et al., 2013) to general simulation-based dynamic traffic assignment models. Empirical results on a city network show that intersection controls are a major bottleneck in the model, and that the legacy early method improves over traffic signals when the autonomous vehicle proportion is sufficiently high. |
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| Keywords: | Autonomous vehicles Dynamic traffic assignment Cell transmission model Multiclass Shared road |
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