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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]