For the environmental friendliness of the technology on battery electric vehicles, there is growing attention on it. However, the market share of battery electric vehicles remains low due to the range anxiety. As a remedy, the mobile charging services could offer charging service at any time or locations requested. For profitability of the services, the operator should route the charging vehicles in a more efficient manner. For this consideration, we formulate the mobile charging vehicle routing problem as a mixed integer linear program based on the classical vehicle routing problem with time windows. To demonstrate the model, test instances are designed and computational results are presented. In order to examine the change of the number of mobile charging vehicles and travel distance, sensitivity analyses, such as battery capacity and recharging rate, are performed. The results show that larger battery capacity, quicker charging rate, or higher service efficiency could decrease the number of mobile charging vehicles and total traveled distances, respectively.

Increasing attention is being paid to the technology of battery electric vehicles (BEVs) because of their environmental friendliness. However, their short range, extended recharging times, and insufficient charging facilities hinder the improvement in the market share of BEVs. As a remedy, this paper presents a novel approach to providing a service for the battery charge replenishment of BEVs. Instead of using traditional alternative methods by only providing a charging service in a fixed location, such as battery-swapping and charging lanes, the novel charge replenishment is provided by mobile charging vehicles (MCVs), which could offer a charging service at any time and at location requested. To consider the limited running range and the opportunity to recharge from MCVs, as well as to determine the location strategy of multiple types of plug-in charging facility locations and the routing plan of the MCVs simultaneously, the location routing problem (LRP) that can integrate two decision levels, with a strategic level (location) and tactical level (routing), is applied. Then, we present the multiple types of plug-in charging facilities’ location-routing problem with time windows for mobile charging vehicles (MTPCF-LRPwTW-MCVs), and formulate the MTPCF-LRPwTW-MCVs as a mixed integer linear program for the convenience of solving. To demonstrate the model, test instances are designed and computational results are presented. Furthermore, sensitivity analyses on battery capacity, recharging rate, and so on, are also examined. The results show that with the increase of the battery capacity or the improvement of the charging rate of the charging facilities, the service efficiency of the MCVs can reasonably be improved. Therefore, the proposed method could be used in real world problems.

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.

There are currently rising applications of battery electric buses in public transportation system, given their energy efficiency and societal benefits. However, limited range and time-consuming charging processes significantly reduce the operational efficiency of battery electric buses system. Thanks to fast charging technology, battery electric buses can run continuously through rational planning of fast charging system. Therefore, to achieve an effective and economic electric transit route network, electric transit route network and fast charging stations should be simultaneously and systematically deployed. This study develops a bi-level programming framework to formulate the electric transit route network design problem. Where, the upper-level model is to determine the route structure and charging station location to optimize the total costs of both user and operator, the lower-level passenger assignment model is to calculate user cost for upper-level model. Then, the proposed model is solved by a modified genetic algorithm (GA), which can search all possible route structures through specifically designed genetic operators. A numerical example from Swiss road network is employed to demonstrate the applicability of bi-level model and performance of modified GA.

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.

This work proposed a model for transit schedule design by integrating the spatial-equity considerations into the slack-time setting problem. The proposed model can help to design a transit service for more equitable reliability of all stops distributed along the route. The transit operators’ risk taking attitudes are also taken into consideration by introducing the travel-time budget (TTB) and mean-excess travel time (METT) as two risk measures. The proposed model can be formulated as a stochastic optimization problem and solved by a subsequently designed Monte Carlo (MC) simulation based genetic algorithm (GA) procedure. Scenario-based numerical examples from a real bus route in Beijing city of China are presented to demonstrate the characteristics of the model. The sensitivity of the parameters setting are also taken into consideration as well, which reflects the impact on the schedule setting of various factors including actual travel-time variability, risk-taking attitudes of transit operators...