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Large-scale use of electric vehicles will greatly increase the traffic pressure on urban road network. Therefore, planning of charging stations for electric vehicles considering charging demand and transportation network is particularly important for the coordinated development of electric vehicles and intelligent transportation. Under the condition of bounded rationality, this paper considers such factors as the travel utility perception difference between the users of fuel vehicles and electric vehicles, the time-varying of traffic flow, the location and service level of charging stations. On this basis, combining the cumulative prospect theory, dynamic traffic flow allocation and charging demands, a two-level programming model is established to solve the problem of charging station site selection. The upper layer is a system optimal model, the goal is to minimize the travel time of the network. The lower model describes the time-variability of departure time and the randomness of charging and travel behaviors, establishes the dynamic user equilibrium model and designs the heuristic algorithm. The validity of the model and algorithm is verified by a numerical example. Through the simulation experiment, the optimal location scheme of charging station under different electric vehicle proportion is obtained, and the driving characteristics of two types of vehicles are analyzed. Compared with the traditional model, it is found that the charging station planning considering bounded rationality can achieve higher road network traffic efficiency with fewer charging piles.

Based on the difference between the online bus stop and the offline bus stop, two macro models are developed to describe the two types of bus stops. The numerical results show that the two models can qualitatively reproduce some complex phenomena resulted by the two types of bus stops and that the offline bus stop is more effective than the online bus stop when the initial density is relatively low.

The variable message signs (VMS) have been widely used in guiding and managing the dynamic traffic with development of intelligent transportation technologies. It is known that cell transmission model (CTM) can well reproduce such traffic dynamics as shock waves and jams. This paper is novel in simulating the traffic propagations based on CTM and VMS route recommendations. The impacts of VMS on congestion are numerically investigated. Simulation results show that the real-time traffic conditions can well be reproduced. The VMS can positively influence travelers’ decision making so as to alleviate the grave degree of traffic congestion. And further, the higher traffic demands are, the better effect of VMS route recommendation is.

Based on the properties of the car-following model,we present in this paper a formula of the kinetic energy change and use the formula to study the effect of the lane changing on the kinetic energy of the system. The numerical results show that the lane changing probability and the relative velocity have great effect on the kinetic energy,but the lane changing probability and the relative speed will respectively lessen the frequency and amplitude of the change of the kinetic energy. In addition,the enhanced average kinetic energy of the system will decrease with the increase of its maximum lane changing probability.

We use the model with the consideration of the traffic interruption probability (Physica A 387(2008)6845) to study the relationship between the traffic risk coefficient and the traffic interruption probability. The analytical and numerical results show that the traffic interruption probability will reduce the traffic risk coefficient and that the reduction is related to the density, which shows that this model can improve traffic security.