As environmental and energy issues have attracted more and more attention from the public, research on electric vehicles has become extensive and in-depth. As driving range limit is one of the key factors restricting the development of electric vehicles, the energy supply of electric vehicles mainly relies on the building of charging stations, battery swapping stations, and wireless charging lanes. Actually, the latter two kinds of infrastructure are seldom employed due to their immature technology, relatively large construction costs, and difficulty in standardization. Currently, charging stations are widely used since, in the real world, there are different types of charging station with various levels which could be suitable for the needs of network users. In the past, the study of the location charging stations for battery electric vehicles did not take the different sizes and different types into consideration. In fact, it is of great significance to set charging stations with multiple sizes and multiple types to meet the needs of network users. In the paper, we define the model as a location problem in a capacitated network with an agent technique using multiple sizes and multiple types and formulate the model as a 0–1 mixed integer linear program (MILP) to minimize the total trip travel time of all agents. Finally, we demonstrate the model through numerical examples on two networks and make sensitivity analyses on total budget, initial quantity, and the anxious range of agents accordingly. The results show that as the initial charge increases or the budget increases, travel time for all agents can be reduced; a reduction in range anxiety can increase travel time for all agents.

Objective: The primary objective of this study is to understand electric bicycle riders’ responses to pedestrian countdown signal devices (PCSDs), in particular, red light violations (RLVs) and early start behaviors. Methods: Descriptive analyses were performed to compare the frequency of electric bicycle riders’ risk-taking behaviors, including RLVs and early start maneuvers at signalized intersections with PCSDs and intersections without PCSDs. Binary logit models were deployed to evaluate the influence of the associated factors, including gender, presence of a pillion passenger, type of electric bicycle, and number of turning vehicles, on the RLV behaviors and early start maneuvers of electric bicycle riders at signalized intersections with PCSDs. Results: Descriptive analyses indicate that PCSDs are effective in reducing the number of red light running violations. Significant variables for RLV behaviors include being female, no pillion passengers, type of electric bicycle, and number of turning vehicles. In addition, a similar correlation between early start maneuvers and influential factors was observed. Conclusions: PCSDs are effective in preventing RLVs but increase the probability of early starts. Based on the findings in this article, practical countermeasures such as redesigning PCSDs could help improve safety at intersections for electric bicycle riders and should be further investigated.

Compared with traditional fuel vehicles, battery electric vehicles (BEVs) as a sustainable transportation form can reduce carbon dioxide emissions and save energy, so its market share has great potential. However, there are some problems, such as: Their limited range, long recharging time, and scarce charging facilities, hindering improvement in the market potential of BEVs. Therefore, perfect and efficient charging facility deployment for BEVs is very important. For this reason, the optimal locations for charging stations for BEVs are investigated in this paper. Instead of flow-based formulation, this paper is based on agents under strictly imposed link capacity constraints, where all agents can select their routes and decide on the battery recharging plan without running out of charge. In our study, not only the locations of charging stations, but also the size of charging stations with the different number of chargers, would be taken into consideration. Then, this problem is formulated as a location problem for BEV charging stations of multiple sizes based on agents under link capacity constraints. This problem is referred to as the agent-refueling, multiple-size location problem with capacitated network (ARMSLP-CN). We formulate the ARMSLP-CN as a 0–1 mixed-integer linear program (MILP) with the aim to minimize the total trip time for all agents, including four parts, namely, the travel time, queue time, fixed time for recharging, and variable recharging time depending on the type of charger and the amount of power recharged, in which commercial solvers can solve the linearized model directly. To demonstrate this model, two different numerical instances are designed, and sensitivity analyses are also presented.