As the electric vehicle industry continues to grow on a large scale, the challenges related to the locating and sizing of charging stations are becoming more apparent. This paper utilizes floating car data from Urumqi, considering factors such as battery SoC, usage time, and the diverse preferences of electric vehicle drivers when making charging decisions. Through this approach, this paper predicts the charging demands of electric vehicles and formulates a model for optimal locating and sizing based on these predicted needs. The research findings indicate that by simulating charging decisions made by electric vehicle drivers, a total of 10,766 charging demands were identified. In comparison, there were 14,759 parking events extracted from the floating car data. This approach reduced redundancy in charging demand predictions compared to directly using the floating car data for extracting parking events. When considering the locating of charging stations, the strategy of exclusively installing fast-charging stations showed better results than the approach involving a mix of fast and slow charging stations. When the decision of locating prioritized minimizing unmet charging demands, it successfully addressed a minimum of 130 additional charging demands. Similarly, when minimizing the idle time for electric taxi drivers was the primary concern, this approach led to a reduction of at least 109 hours in idle time; through a reallocating and sizing adjustment of charging piles within the charging station based on the locating plan, it was possible to meet an additional 83 charging demands. This modification also significantly reduced the average idle time for users, decreasing from an average of 4.26 minutes to 2.63 minutes. Moreover, the charging piles’ average usage time increased, going up from 10.31 hours to 10.42 hours, thus improving the overall efficiency of the charging piles. These findings provide valuable insights for the locating and sizing of charging stations tailored to urban electric taxis.

Comparing with the conventional random regret minimization (RRM) model, the generalized random regret minimization (G-RRM) model can accommodate the nonlinear variation and can be applied broadly. By considering the regret aversion characteristics and selecting quantitative and qualitative attributes, G-RRM and random utility maximization (RUM)-based models were constructed for commuting mode choice. Survey data was used to estimate the parameters of both models, and the G-RRM model was proved to outperform the RUM model. Finally, predicting results of commuters’ mode choice showed that speedy traffic modes with mass capacity, such as subway and BRT, were more preferred by commuters. The study provides a new idea and method for commuters’ mode choice researches.

To deeply explore the mechanism of consumers' electric vehicle (EV) purchase behavior and address research gaps related to insufficient consideration of psychological latent variables and neglect of consumer heterogeneity in existing studies, this study constructs a latent class model (LCM) that integrates personal attributes, vehicle attributes, and six psychological latent variables: perceived usefulness, perceived ease of use, perceived risk, environmental awareness, purchase attitude, and purchase intention. Based on 1,044 valid questionnaires collected from Urumqi, latent profile analysis (LPA) is used to classify consumers. The results indicate that EV consumers can be divided into five distinct latent profiles with significant differences in purchase preferences: risk-avoidance type, moderate-low intention wait-and-see type, utility-oriented and low environmental concern type, high utility cognition and low risk proactive type, and all-dimensional high-intention core type. Socioeconomic and vehicle-related factors exert heterogeneous impacts on the psychological variables and purchase decisions of each profile. This study clarifies the intrinsic psychological mechanism of EV purchase behavior, providing a theoretical basis and targeted strategy references for the government and enterprises to promote EV adoption and advance sustainable transportation development.