An interpretable analytic framework of the relationship between carsharing station development patterns and built environment for sustainable urban transportation

The in-depth understanding of the relationship between development patterns of carsharing stations and built environment are important to the comprehensive station evaluation, layout optimization and urban spatial resources planning. However, the previous researches mainly study the operation of carsharing by aggregate methods with cross-sectional data and rarely discovered patterns within carsharing operation time series. Therefore, an interpretable analytic framework is proposed for predicting development patterns of carsharing stations, which is composed of a development pattern construction method based on Time Series Clustering and an interpretable prediction method based on CatBoost and SHAP models. The temporal variations of time series data are sufficiently utilized by time series clustering to identify patterns and CatBoost-SHAP has better classification performance and interpretability than general machine learning methods. The proposed framework is applied to explore the relationship between the development pattern of one-way carsharing stations and the built environment influencing factors. The result shows that the carsharing stations of Nanjing EVCARD are divided into two types: increasing pattern and decreasing pattern. The built environment factors that have the greatest impact on model output and the impact of pairwise factors are visually analysed. Moreover, this is also effective for a specific individual station to analyze the causes of its status quo. Therefore, this study provides data-driven intuitive decision references for carsharing operators, which helps the operators effectively manage carsharing stations.