Managing congestion and emissions in transportation networks with dynamic carbon credit charge scheme

Traffic congestion contributes to the air pollution problem due to the increased idling, braking and acceleration behaviors. This research puts forward an optimal dynamic credit charge scheme that can redistribute the traffic flows to attain mobility and emission goals. First, cell transmission model is used as dynamic network loading model to capture the flow propagation and dynamic user equilibrium (DUE) is formulated to investigate the flow redistribution in terms of simultaneous path and departure time choices under carbon credit charge scheme. Based on that, a bi-level formulation is proposed to describe the Stackelberg game between road manager and road users. For the higher level, road manager seeks to minimize system travel time and emissions by implementing credit charge schemes. For the lower level, heterogeneous users find dynamic user equilibrium by simultaneous path and departure time choices under given credit charge schemes. Given the non-convex property of the bi-level model, this paper proposes the pattern search algorithm embedded with projection method. Computational results on X-shape, Ziliaskopolous' and Nyguen-Dupuis network are given to show the applicability of the proposed algorithm on reasonable-size networks. Results show that minimum travel time design does not always generate effective path and departure time switches across all user groups, especially for users with high value of travel time, while minimum emissions credit design yields desirable behavioral adjustments. Besides, minimum travel time credit design does not always generate minimum carbon emissions in the network, especially in networks with complex O-D pairs and paths. This research casts light on how to schedule a time-varying credit charge scheme to attain mobility and emission goals. Policy implications of credit charge scheme are provided.