Integrating congestion pricing and transit investment planning |
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| Institution: | 1. Humboldt-Universität zu Berlin, Department of Agricultural Economics, Division of Resource Economics, Germany;2. Arizona State University, School of Human Evolution and Social Change, United States;1. LAAS-CNRS, Université de Toulouse, CNRS, Toulouse, France;2. Université du Québec à Montréal, Montréal, Canada;3. LAAS-CNRS, Université de Toulouse, CNRS, INSA, Toulouse, France;1. VTI Swedish Road and Transport Research Institute, Box 55685, SE-10215 Stockholm, Sweden;2. ITS, University of Leeds, Leeds, UK;3. RAND Europe, Cambridge, UK;4. CTS, Stockholm, Sweden;5. KTH Royal Institute of Technology, Stockholm,Sweden;1. Key Laboratory of Road and Traffic Engineering of the Ministry of Education, College of Transportation Engineering, Tongji University, Shanghai, PR China;2. Institute of Transport and Logistics Studies (ITLS), The Business School, The University of Sydney, New South Wales 2006, Australia;1. Maritime Institute, Department of Economics, Old Dominion University, Norfolk, VA 23529, United States;2. Department of Information Technology and Decision Sciences, Strome College of Business, Old Dominion University, Norfolk, VA 23529, United States |
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| Abstract: | This paper develops a mathematical model and solution procedure to identify an optimal zonal pricing scheme for automobile traffic to incentivize the expanded use of transit as a mechanism to stem congestion and the social costs that arise from that congestion. The optimization model assumes that there is a homogenous collection of users whose behavior can be described as utility maximizers and for which their utility function is driven by monetary costs. These monetary costs are assumed to be the tolls in place, the per mile cost to drive, and the value of their time. We assume that there is a system owner who sets the toll prices, collects the proceeds from the tolls, and invests those funds in transit system improvements in the form of headway reductions. This yields a bi-level optimization model which we solve using an iterative procedure that is an integration of a genetic algorithm and the Frank–Wolfe method. The method and solution procedure is applied to an illustrative example. |
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| Keywords: | Congestion pricing Bi-level optimization User equilibrium Transit headway reduction |
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