Electric vehicles (EVs) are one of the most promising applications that are reshaping the future urban mobility market and benefitting the urban environment. Analyzing the adoption of EVs helps both vehicle sales market management and urban transportation-related environmental cost estimation. Previous studies have shown that EV adoption is mostly affected by the economic environment and users’ psychological factors; however, both factors vary among specific urban transportation networks. This paper thus proposes network-related vehicle operating cost functions and a logit-based choice model, which considers both the economic environment and users’ psychological factors at a network level. The model can thus estimate the vehicle adoption for specific networks. Numerical experiments and sensitivity analyses were conducted to illustrate the proposed method and provide practical insights in estimating EV adoption, respectively. The results suggest that EV adoption greatly varies among different cities.
Pedestrian crossing at signal-controlled intersections is a hot and challenging problem in slow traffic research. On the one hand, the signal-controlled intersection is a typical mixed traffic environment where pedestrians crossing the street as well as vehicles, will constantly conflict and threaten road traffic safety. On the other hand, compared with other parts, the tail of pedestrian flow occupies a large amount of intersections’ space and time resources, which greatly hinders the passage of right-turning vehicles. Therefore, it is significant to study the characteristics of pedestrian crossing, especially the characteristics of the tail of pedestrian flow crossing at the intersections. It can not only enrich relevant research and provide reference for optimizing the timing of intersection signals, but also improve the pedestrian crossing environment and alleviating traffic congestion. In this paper, the video data of the intersection of Yanta North Road and Jianxi Street, the intersection of Xiaozhai West Road and Hanguang Road, as well as the intersection of Keji Second Road and Taibai South Road were collected by UAV. The track data of pedestrians and vehicles were extracted by OpenCV, YoloV3 and DeepSort. The pedestrian quantity-time curve was plotted for single one way and multiple two-way street crossings. It is found that there is a good fitting effect between the curve and one-dimensional Gaussian function. Based on this, the actual speed and average waiting time of right-turning vehicles passing the crosswalk are compared respectively under the long tail distribution of different significance degrees. The result shows the long tail distribution of pedestrian crossing can obviously reduce the speed of right-turning vehicles and prolong the average waiting time.
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