Anomalous ride-hailing driver detection with deep transfer inverse reinforcement learning

Abstract

The rapid expansion in group size of online ride-hailing drivers has made anomalous driver detection become a critical issue, which substantially affects the safety and operation aspects of ride-hailing services Existing studies mainly focus on the identification of abnormal trajectories, while none of them investigate anomalous driver detection. The former evaluates a specific trajectory, while the latter comprehensively evaluates a driver. The driving preferences of anomalous ride-hailing drivers differ from those of most ride-hailing drivers. Inverse reinforcement learning (IRL) mines the hidden preference of an agent from its observed behavior Although existing IRL models can mine the driver’s route preference from trajectories, they depend on massive trajectories. To deal with insufficient trajectories, we propose deep transfer inverse reinforcement learning, which reuses the partial pre-trained common reward network and transfers it to be a part of the personalized reward network. Then, we use an autoencoder network to reduce the dimension of personalized route preference and combine it with K-means clustering to distinguish all the drivers into two categories (i.e., normal and abnormal). Numerical experiments using ride-hailing trajectories in Chengdu, China are conducted to verify the effectiveness of our proposed approach. The results demonstrate that precision and recall of our proposed approach are 91.7% and 97.1%, respectively.