Trajectory anomaly detection, aiming to estimate the anomaly risk of trajectories given the Source-Destination (SD) pairs, has become a critical problem for many real-world applications. Existing solutions directly train a generative model for observed trajectories and calculate the conditional generative probability$P(T \vert C)$as the anomaly risk, where$T$and$C$represent the trajectory and SD pair respectively. However, we argue that the observed trajectories are confounded by road network preference which is a common cause of both SD distribution and trajectories. Existing methods ignore this issue limiting their generalization ability on out-of-distribution trajectories. In this paper, we define the debiased trajectory anomaly detection problem and propose a causal implicit generative model, namely CausalTAD, to solve it. CausalTAD adopts do-calculus to eliminate the confounding bias of road network preference and estimates$P(T\vert do(C))$as the anomaly criterion. Extensive experiments show that CausalTadcan not only achieve superior performance on trained trajectories but also generally improve the performance of out-of-distribution data, with improvements of 2.1% ~ 5.7% and 10.6% ~ 32.7% respectively.

Nowadays safety has become one of the most critical factors for ride-hailing service. Ride-hailing platforms have conducted meticulous background checks for drivers to minimize the risk of abnormal trips, e.g. violence and sexual assault. However, current methods are labor-consuming and highly rely on the personal information of drivers, which may harm the fairness of the order dispatching system. In this paper, we utilize the trip trajectories as inputs and propose a dual variational auto-encoder(VAE) framework, namely TripSafe, to estimate the probability of abnormal safety incidents. Specifically, TripSafe models the moving behavior and route information, as two independent components and employs VAEs to pre-train generative models for normal trips. Then, a fusion network is adopted to fine-tune the whole model with a few labeled samples. In practice, TripSafe monitors the data update and calculate the anomaly score of partial-observed trips in real-time. Experiments on real ridehailing data show that TripSafe is superior to the state-of-the-art baselines with about 14.2%~28.9% improvements on F1 score.