Eco-system optimal time-dependent flow assignment in a congested network |
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| Institution: | 1. Department of Transportation and Logistics Management, National Chiao Tung University, Hsinchu, 300, Taiwan;2. School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ, 85287, USA;3. State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, 100044, China;4. School of Civil Engineering, Purdue University, West Lafayette, IN, 47907, USA;5. Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC, 27695-7908, USA;1. Institute for Transport Studies, The University of Leeds, Leeds, United Kingdom;2. Intelligent Transport Systems Lab, The Swinburne University of Technology, Melbourne, Australia;1. Department of Civil and Coastal Engineering, University of Florida, 365 Weil Hall, Gainesville, FL 32611-6580, United States;2. Department of Industrial Engineering, Tsinghua University, N502 Shunde Building, Beijing 100084, China;3. School of Transportation Engineering, Tongji University, Shanghai 201804, China |
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| Abstract: | This research addresses the eco-system optimal dynamic traffic assignment (ESODTA) problem which aims to find system optimal eco-routing or green routing flows that minimize total vehicular emission in a congested network. We propose a generic agent-based ESODTA model and a simplified queueing model (SQM) that is able to clearly distinguish vehicles’ speed in free-flow and congested conditions for multi-scale emission analysis, and facilitates analyzing the relationship between link emission and delay. Based on the SQM, an expanded space-time network is constructed to formulate the ESODTA with constant bottleneck discharge capacities. The resulting integer linear model of the ESODTA is solved by a Lagrangian relaxation-based algorithm. For the simulation-based ESODTA, we present the column-generation-based heuristic, which requires link and path marginal emissions in the embedded time-dependent least-cost path algorithm and the gradient-projection-based descent direction method. We derive a formula of marginal emission which encompasses the marginal travel time as a special case, and develop an algorithm for evaluating path marginal emissions in a congested network. Numerical experiments are conducted to demonstrate that the proposed algorithm is able to effectively obtain coordinated route flows that minimize the system-wide vehicular emission for large-scale networks. |
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