In 2017, Shenzhen replaced all its buses with battery e-buses (electric buses) and has become the first all-e-bus city in the world. Systematic planning of the supporting charging infrastructure for the electrified bus transportation system is required. Considering the number of city e-buses and the land scarcity, large-scale bus charging stations were preferred and adopted by the city. Compared with other EVs (electric vehicles), e-buses have operational tasks and different charging behavior. Since large-scale electricity-consuming stations will result in an intense burden on the power grid, it is necessary to consider both the transportation network and the power grid when planning the charging infrastructure. A cost-minimization model to jointly determine the deployment of bus charging stations and a grid connection scheme was put forward, which is essentially a three-fold assignment model. The problem was formulated as a mixed-integer second-order cone programming model, and a “No R” algorithm was proposed to improve the computational speed further. Computational studies, including a case study of Shenzhen, were implemented and the impacts of EV technology advancements on the cost and the infrastructure layout were also investigated.

China has been one of the most aggressive countries in electric vehicle (EV) promotion. However, private EV sales fail to achieve the government’s target. In particular, cutting purchase subsidies poses great uncertainty in relation to EV diffusion. In this research, a system dynamics model aims to investigate the influence of government policies, infrastructure development plans, the duration of policies, and the phase out strategy of policies. Parameters relating to consumers’ preferences are drawn from a questionnaire survey, which is conducted in Shenzhen, the pioneer city in China’s EV promotion. The result of a scenario analysis shows that purchase subsidies, purchase restrictions and driving restrictions are the most effective policies for EV promotion. Driving restrictions are more effective but less easy to enforce than purchase restrictions. The number and location of charging piles are much more important than large charging stations. Moreover, EV diffusion can be self-sufficient after current policies have been maintained for 11 years. We find that the gradual removal of subsidies will cause a four-year delay in EV sales entering rapid growth in Shenzhen. However, cutting subsidies in cities without purchase restrictions will cause the failure of EV promotion.

Multivariate time series forecasting has long been a research hotspot because of its wide range of application scenarios. However, the dynamics and multiple patterns of spatiotemporal dependencies make this problem challenging. Most existing methods suffer from two major shortcomings: (1) They ignore the local context semantics when modeling temporal dependencies. (2) They lack the ability to capture the spatial dependencies of multiple patterns. To tackle such issues, we propose a novel Transformer-based model for multivariate time series forecasting, called the spatial–temporal convolutional Transformer network (STCTN). STCTN mainly consists of two novel attention mechanisms to respectively model temporal and spatial dependencies. Local-range convolutional attention mechanism is proposed in STCTN to simultaneously focus on both global and local context temporal dependencies at the sequence level, which addresses the first shortcoming. Group-range convolutional attention mechanism is designed to model multiple spatial dependency patterns at graph level, as well as reduce the computation and memory complexity, which addresses the second shortcoming. Continuous positional encoding is proposed to link the historical observations and predicted future values in positional encoding, which also improves the forecasting performance. Extensive experiments on six real-world datasets show that the proposed STCTN outperforms the start-of-the-art methods and is more robust to nonsmooth time series data.

To prove the important role of battery electric buses (BEBs) in reducing carbon dioxide (CO${}_2$) emissions, we propose a framework to compare CO${}_2$ emissions between BEBs and conventional diesel buses (CDBs) based on low sampling frequency BEBs data at the city scale in Shenzhen. We applied the VT-Micro model to improve the estimation of CDBs’ CO${}_2$ emissions. A modal-activity-based method was implemented to reconstruct the second-by-second trajectories from the dataset as the input of the VT-Micro model. We updated the data of the Guangdong power generation mix to improve the estimation of BEBs’ CO${}_2$ emissions. The experiments showed that BEBs could reduce CO${}_2$ emissions by 18.0–23.9% in comparison with CDBs when the frequency of air-conditioning usage was low. In addition, BEBs tended to achieve more CO${}_2$ emission reduction benefits when the transit buses traveled at a low speed. Improving the traffic efficiency of road networks and promoting inter-provincial electricity trading are important to promote the adoption of BEBs.

Virtual testing plays a vital role in developing connected and automated vehicles. To ensure the safety of CAV, millions of miles of testing are required. The scenario library defines the parameter of each testing scenario, which reduces testing by merging similar scenarios and focusing on challenging scenarios. However, current scenario generation methods still bear some disadvantages, such as lack of data and low efficiency. In order to overcome the lack of data, this paper proposed a genetic algorithm-based framework for the cut-in event and car-following event. In this study, the genetic algorithm searches for one of the most challenging scenarios within the boundaries. The challenging scenario is acquired by the brute force search algorithm which searches from the start scenario. The result shows the ratio of accident scenarios to challenging scenarios in our scenario set is more than 40%. Compared with natural driving data scenarios, our method performs well without natural driving data. In addition, the genetic algorithm converges in ten iterations which indicates its efficiency and suitability in scenario generation. More specifically, whole challenging scenarios which probably occur in the real world are collected in the scenario set.

This paper studies how autonomous vehicles (AVs) influence future traffic and energy consumption when adopting various route guidance strategies, which can be divided into global information strategies and local information strategies according to the scope of information collection. A mixed traffic flow model is established. Then, an autonomous vehicle model is improved to investigate the impact of six route guidance strategies. The selected strategies are specifically evaluated in a double-route scenario, considering both single-exit and dual-exit configurations. Three indicators are chosen to evaluate traffic efficiency, including traffic flow, average speed and quantity of vehicles. Consumption per unit flux is the indicator of energy consumption level. Simulation results show that autonomous vehicles can improve traffic efficiency and reduce energy consumption.