This paper proposes an improved intelligent driver model (IDM) by considering the information of multiple front and rear vehicles to describe the car-following behaviour of CAVs (Connected and autonomous vehicles). The model involves the velocity and acceleration of multiple front and rear vehicles as well as the velocity difference and headway between the host vehicle and its surrounding vehicles. By introducing location-related parameters, the model quantitatively expresses the change in influence degree of a surrounding vehicle with its location to the host vehicle. To maximize traffic stability, we obtain the optimal value of the parameters in the model and the effect of specific time delays on the stability of traffic flow with numerical simulation. The results indicate that for a single vehicle control, the proposed model provides a much quicker and smoother acceleration and deceleration process to the desired speed than the IDM and multi-front IDM. And for platoon control, the proposed multi-front and rear IDM is superior to the other two models in decreasing the starting and braking time and increasing the stability of speed and acceleration. With effective car-following behaviour control, it is helpful to improve the operation efficiency of CAVs and enhance the stability of traffic flow. In addition to the car-following behaviour control, the model can be utilized for platoon control in the case of CAVs' homogeneous flow. This model can also serve as an effective tool to simulate car-following behaviour, which is beneficial for road traffic management and infrastructure layout in connected environments.

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With the rapid development of the construction and operation of mass transit hubs, passenger data collection, modeling, and prediction for optimal control have become very important. In this paper, pedestrian facilities are abstracted into connected nodes, and the passenger flow network is formed according to the facility connection relationship determined by the traffic organization; therefore, the state variables of the hub, such as saturation, and the traveling time can be estimated by pedestrian flow information collected by camera monitors and a free Wi-Fi network, including the fast analysis of data features and traffic flow prediction. The method is applied to a real case. The features of pedestrian flows are classified as chaotic and nonchaotic. We use a regression model to predict the nonchaotic situation, and the wavelet support vector machine model is proposed for the chaotic. The results can be used for the control of exits and ramps in the hub.

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