Search

Electric vehicles (EVs) have been widely introduced into the bus fleet while the short driving range and long charging time for battery electric buses (BEBs) are the two main barriers. Thus, bus operators are considering alternatives. Hydrogen buses (HBs) could be a promising option because of their longer driving range and shorter refueling time compared to BEBs. However, introducing HBs would be costly and thus it remains unclear which fuel option (hydrogen or electricity) is more feasible for an electrified public transit system. In response, this study proposed a data-driven micro-simulation approach to compare the system cost and level of service (i.e., the delay time of deviating from the timetable caused by charging events) with different fuel options (electricity or hydrogen) for electrifying public transit, using real-world bus operation information extracted from a GPS bus trajectory dataset in Shenzhen, China. The results suggested that the charging demands of BEBs tended to be concentrated in the central and northwest areas of the city while the refueling demands of HBs were more evenly distributed in not only the center but also the southwest and northeast areas. These resulted in different layouts of charging/hydrogen stations accordingly. Furthermore, given almost the same level of service to maintain, the system cost of the HB scenario could be 48.2% higher than that of the BEB scenario. Therefore, BEBs tended to be more economically feasible in Shenzhen. • A data-driven approach to comparing fuel options for electrifying public transit. • A bus trajectory dataset was used to represent real-world operation of a bus system. • Battery electric buses tended to be more economically feasible than hydrogen buses. • The energy and vehicle costs contributed significantly to the system cost. [ABSTRACT FROM AUTHOR]

Inequitable distribution of public electric vehicle (EV) charging facilities may result in disparities in charging accessibility, potentially impeding the uptake of EVs. In response, this paper proposes an analytical framework to assess the accessibility-based equity of public EV charging infrastructure systematically, using Hong Kong (HK) as the study area. The results demonstrated severe spatial inequity of charging facilities in central, north, and southwest HK. Meanwhile, the Gini index of all public EV charger types for the total population was 0.751, and the indices for low-income, low-education level, and government-funded housing subgroups were 0.791, 0.809, and 0.893, respectively. These indicated considerable horizontal and vertical inequity from a statistical perspective. Furthermore, age, education level, family structure, and housing type were identified as significant socio-demographic characteristics correlated to the accessibility-based equity of public EV charging infrastructure in HK. These findings are expected to be useful for future policymaking and infrastructure planning. [ABSTRACT FROM AUTHOR]

Electric vehicle (EV) charging demand is an essential input of charging facility location models. However, charging demand may vary across seasons. In response, this paper first provided insights into the seasonal variance in charging demand using a unique GPS trajectory dataset which contained travel, parking, and charging information of 2,658 private EVs in Beijing. The dataset was collected in January, April, July, and October 2018, which were representative months in winter, spring, summer, and autumn, respectively. Through statistical and spatiotemporal analyses, we found that in winter, EVs got recharged when their state of charge (SOC) was lower: the average SOCs on working days were 51.96%, 48.39%, 50.86%, and 43.50%, in spring, summer, autumn, and winter, respectively. Furthermore, the central urban areas tended to have a higher charging demand in winter. To further explore how the seasonal variance in charging demand may influence infrastructure deployment, we used the classical p-median model to deploy charging facilities with the charging demands in the four seasons, considering the modifiable areal unit problem (MAUP). The results suggested that the seasonal variance did influence the layout of charging facilities under different spatial analysis units (SAUs). The deployment of charging facilities in the central urban areas and outer suburbs tended to be more sensitive to seasonal variance in charging demand. The findings are expected to be useful for charging infrastructure planning in both the transport and power sectors. • Explore seasonal variance in Electric Vehicle (EV) charging demand with big data. • EVs got recharged when their state of charge (SOC) was lower in winter. • The central urban areas tended to have a higher charging demand in winter. • Significant seasonal effects on infrastructure deployment in central and outer areas. • More noticeable seasonal effects with fewer charging infrastructure to be deployed. [ABSTRACT FROM AUTHOR]

• Comparing BEV and HFCV from operational, economic, and environmental perspectives. • Proposing a data-driven and simulation-based multi-objective optimization method. • Using LCA to estimate GHG emissions and energy consumption in C2G life cycle. • Using a real-world trajectory dataset to quantify energy demand at the micro-scale. • Different combinations of fuel and facility have benefits and downsides. In order to evaluate suitability of battery electric vehicles (BEVs) and hydrogen fuel cell vehicles (HFCVs) in freight transport systems, this paper proposes a data-driven and simulation-based multi-objective optimization method to deploy charging/refueling facilities for BEVs/HFCVs. The model considers three objectives, namely minimizing total system cost, maximizing service reliability, and minimizing greenhouse gas (GHG) emissions. In particular, a data-driven micro-simulation approach is developed to simulate the operation of freight transport systems with different vehicle and facility types based on the analysis of a one-week Global Positioning System (GPS) trajectory dataset containing 63,000 freight vehicles in Beijing. With the model, we compare the suitability of BEVs and HFCVs within three typical scenarios, i.e., BEVs coupled with Charging Stations (BEV-CS), BEVs coupled with Battery Swap Stations (BEV-BSS), and HFCVs coupled with Hydrogen Refueling Stations (HFCV-HRS). The results suggest that BEV-CS has the lowest total system cost: its system cost is 62.5% and 90.3% of the costs in BEV-BSS and HFCV-HRS, respectively. BEV-BSS has the lowest delay time: its delay time is 62.1% and 86.0% of the delay times in BEV-CS and HFCV-HRS, respectively. HFCV-HRS has the lowest GHG emissions: its emissions are 37.3% and 46.9% of the emissions in BEV-CS and BEV-BSS, respectively. The results are expected to be helpful for policy making and infrastructure planning in promoting the development of alternative fuel vehicles. [ABSTRACT FROM AUTHOR]

This study aims to investigate the public attention and attitude towards shared bikes (SBs) in China. More than 721,000 posts collected from Sina Weibo (one of the most popular social media platforms) from January 2017 to August 2023 have been analyzed utilizing an analytical framework comprising statistical and temporal analysis, sentiment analysis, and topic analysis methods. The results reveal a consistent upward trend in public interest since 2018, with high engagement from the media, education, and institutional sectors, and greater interest from women and younger Weibo users. Economically developed regions with supportive policies and flat terrain, such as Beijing, Guangdong, and Shanghai, exhibit a higher level of public attention. Sentiment analysis indicates an overall positive public attitude, primarily driven by discounted monthly passes, but there are negative sentiments arising from improper parking management practices. The results are expected to be helpful for improving the SB system and informing relevant policy-making. [ABSTRACT FROM AUTHOR]