Analysis of travel activity patterns and charging choices of BEV users in Japan

Engineering notes

Key topics: autonomous driving, bev, planning. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Battery electric vehicle (BEV) users must integrate charging into their daily travel routines in ways unlike conventional refueling, requiring a deliberate coordination of trip purposes, time constraints, and charging accessibility. Understanding these behavioral charging decisions is critical for supporting the long-term sustainability and usability of EV systems. Yet most prior studies focus on infrastructure placement or isolated aspects of charging decisions, without systematically integrating activity-based travel routines into behavioral models. This study addresses that gap by investigating how BEV users in Japan choose between home charging and fast charging, focusing on trip purposes, acceptable waiting time, and socio-demographic characteristics. Using survey data from 441 BEV users across the Chubu and Kanto regions of Japan, three structural equation models (SEMs) are estimated to examine the behavioral pathways underlying charging decisions. Model 1 captures the baseline effects of socio-demographics and time sensitivity. Model 2 introduces total trip frequency as a mediator, and Model 3 disaggregates trip purposes—commuting, shopping, and leisure—to examine their effects on charging behavior. The results indicate that trip activity patterns are significant determinants of charging behavior. Commuting trips are most consistently associated with home charging, reflecting predictable travel schedules that facilitate planned overnight residential charging. Shopping trips show the strongest association with fast charging, suggesting that commercial destinations serve as key locations for opportunistic charging. Leisure trips exhibit a flexible, context-dependent pattern depending on trip distance and time availability. Acceptable waiting time emerges as a critical behavioral constraint, with lower acceptance directly increasing fast charging use and indirectly shaping charging behavior through travel frequency. Socio-demographic heterogeneity further shapes charging behavior: higher-income households show greater reliance on home charging, while middle-income households depend more on public fast charging. BEV model type also influences charging strategy, with Nissan Leaf owners relying predominantly on residential charging, whereas imported BEV owners adopt more diverse charging approaches. Regional differences between Chubu and Kanto highlight the role of residential density and private parking availability in shaping charging behavior. These findings suggest that EV infrastructure planning should integrate activity-based travel patterns.

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