In order to improve the operational efficiency of a shared electric vehicle (SEV) system, this paper conducts integrated planning research from the aspects of locating and sizing SEV outlets, locating and sizing charging facilities, and operation scheduling by considering the carbon emission reduction and its benefits. Firstly, a multi-objective locating model was constructed with the optimization objectives of minimum outlet construction cost and user travel time cost and minimum carbon emissions from outlet construction and vehicle routing. Secondly, a charging demand simulation predictive model framework was designed. Finally, an SEV scheduling model with maximal operating income and minimal scheduling costs as optimization objectives was constructed considering the benefits of carbon emission reduction. By applying these models to the Chicago Sketch network and using a genetic algorithm to solve the models, it is concluded that the optimal outlet location solution considering carbon emission reduction will increase the outlet construction cost and user travel time cost. When the user loss rate is 40%, the initial number of vehicles in each outlet can make the operator’s daily income reach the maximum. The number of charging piles in each outlet is optimally configured according to 55% of the highest charging demand. Scheduling can make the distribution of charging demand more uniform and reduces the maximum charging demand by about 36.8% and the total number of charging piles by about 24.0%, at the same time, increases the average time utilization rate of charging piles at all outlets by about 15.6% and the user satisfaction rate by about 8.6%. Although scheduling increases the scheduling cost by about 3.8%, it can increase the total daily revenue of operators by 6.5%. Carbon reduction benefits can increase the total daily revenue of operators by 0.7%.

A Scobit-based model, which considers individual heterogeneity, is proposed to analyze the residents' willingness to participate in car-sharing programs. In the Scobit model, skewness parameters are used to reflect the heterogeneity of individual attributes of different travelers. During the parameter calibration and elasticity analysis progress, the model is mutually validated with the results of Binary Logit model. It shows that the Binary Logit model and Scobit model are consistent with the calibration results and Scobit model performs better than Binary Logit model. Based on the results, the factors of age, occupation, household income, car hailing and car rental experience, vehicle cost, vehicle mileage, parking service, vehicle type and vehicle return method significantly affect people's choices. Besides, elastic value analysis results show that vehicle costs, vehicle's mileage, parking services have greater impact on the choice of residents than others. Finally, some advice is given to encourage the car-sharing participation willingness.

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 explore the mechanism of consumers’ battery electric vehicle (BEV) purchase behavior in depth 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 1044 valid questionnaires collected from Urumqi, latent profile analysis (LPA) is used to classify consumers. The results indicate that BEV consumers can be divided into five distinct latent profiles with significant differences in purchase preferences: the risk-avoidance type, the moderate–low intention wait-and-see type, the utility-oriented and low environmental concern type, the high utility cognition and low-risk proactive type, and the 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 BEV purchase behavior, providing a theoretical basis and targeted strategy references for the government and enterprises to promote BEV adoption and advance sustainable transportation development.

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.