Modeling driving styles of online ride-hailing drivers with model identifiability and interpretability

The online ride-hailing industry has grown rapidly and has greatly improved travel efficiency worldwide. The driving styles of the drivers of ride-hailing vehicles largely determine the safety and comfort of passengers. Therefore, identifying and analyzing such driving styles are important tasks in order to maintain travel safety and efficiency. However, few studies have focused specifically on online ride-hailing drivers' driving styles, and those few usually suffer from a lack of representativeness of influential factors as well as from failure to balance the identification accuracy and interpretability of the model applied. To address these issues, we propose an interpretable machine learning method to model the driving styles of online ride-hailing drivers. The proposed method considers both model identifiability and interpretability. First, we conducted naturalistic driving tests in Nanjing, China to collect driver data, vehicle kinematic data, and video data. Then, we extracted driver features, environmental features, and online ride-hailing driving features as the influential factors. Next, we used the CatBoost algorithm to identify three types of driving styles: aggressive, normal, and cautious. Finally, we used the Shapley additive explanation (SHAP) algorithm (1) to explore the effects of the influential factors on the driving styles from four perspectives (feature importance, total effect, main effect, and interaction effect) and (2) to analyze the differences in driving styles between online ride-hailing drivers and ordinary drivers. In addition, to verify the effectiveness of CatBoost, we compared it to three typical machine learning algorithms: extreme gradient boosting (XGBoost), artificial neural network, and support vector machine. The results show that CatBoost significantly outperformed the other three algorithms with macro-average precision, recall, and F1score values of 0.818, 0.881, and 0.842, respectively. Also, SHAP was able to explain the complex nonlinear relationship between the driving styles and the influential factors. Overall, the distance, driving event, driver age, driving task, and duration features are of relatively high importance. This study provides an effective and innovative method to identify and analyze the driving styles of online ride-hailing drivers. Importantly, this method can help the online ride-hailing industry to monitor, analyze, and improve drivers' driving behavior and thus improve travel safety and efficiency.