| As an effective mothds of both managing road traffic demand and raising additional revenue for road construction, congestion pricing has been studied extensively by the transportation researchers and the government. Indeed, congestion pricing has become one of the priorities on transport policy agendas throughout the world. It is increasingly believed that congestion pricing may offer an effective instrument to control travel demand, travel tools, travel time, and also can reduce the air pulltion with the reduction of the road congestion. Congestion pricing money also raises revenue that may, for instance, be used for transport improvements. An increasing number of congestion pricing schemes have been proposed, tested or implemented worldwide. In the networks, some of the users'travel behaviors are based on personal, and some of users'travel behaviors are based on group. So it is more meaningful to research on the mixed equilibrium behaviors. Based on congestion pricing under mixed equilibrium behaviors on urban traffic networks, the paper study as follows:Firstly, the congested road pricing problem is investigated under the mixed traffic equilibrium behaviors in urban congested transportation networks. In reality, both competition and cooperation may exist among network users, which can be described as the Cournot-Nash (CN) equilibrium behaviors. Some individual users always seek the shortest paths based on the current flow pattern, and thus follow Wardropian user equilibrium (UE). By affording to the government the congestion fees, some drivers may choose to give up the travel or choose some public travel tools, so the UE players' demand is elastic. A bi-level programming model is formulated to find the proper congestion pricing on the existing links under the mixed traffic equilibrium behaviors. The upper-level problem aims to maximize the social economic benefit and minimize the total system cost by charging to the UE players only, while the lower-level problem represents the mixed behaviors of route choice based on congestion pricing strategies. The particle swarm optimization (PSO) algorithm is used to solve the proposed model and a numerical example is provided. The computation results show that the congestion pricing strategy can improve network performance and reduces the travel demand.Secondly, some individual users always seek the shortest paths based on the current flow pattern, but the travelers perceive travel costs at different precision levels. Both competition and cooperation exist among network users while their perceptions of travel time are stochastic and subject to variations, which can be described as the Stochastic Cournot-Nash (SCN) equilibrium behaviors. If travelers choose their shortest routes with the least cost according to their perception, the principle of stochastic user equilibrium (SUE) is applied to model the resultant route choice pattern. A bi-level programming model is formulated to deal with the congestion pricing strategies on the existing links under the stochastic mixed behaviors. The upper-level problem aims to minimize the total system cost by charging to part of network users, while the lower-level problem represents the mixed behaviors of stochastic route choice based on congestion pricing strategies. The computation results show that the congestion pricing strategy can obviously improve network performance in terms of reducing system congestion.Finally, we compare the total travel time of all users with different congestion pricing strategies. It shows four different situations:no congestion pricing to the SUE and SCN users, only charging to the SUE users, charging the SUE and SCN users with the same congestion pricing, charging the SUE and SCN users with different congestion pricing. Through charging to the SUE players only can reduce the total charging fees and priority of the SCN players can be guaranteed. The result shows the feasibility and practicality of the strategy which charges to the SUE players only.
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