Spatial-Temporal-Cost Combination based Taxi Driving Fraud Detection for Collaborative Internet of Vehicles

Vehicle-to-vehicle (V2V) interaction and collaboration can provide us with a large number of mobile traffic trajectories that can be used to analyze driving behavior. In this paper, we propose a spatio-temporal cost combination based framework for taxi driving fraud detection (STC). First, the point of interest (POI) where taxis interact and collaborate with Collaborative Internet of Vehicles (C-IoVs) participants is identified, and a baseline trajectory model is built to determine the typical trajectory distribution. Second, a statistical model is used to calculate the trip distribution, travel time, and travel cost. At the same time, the taxi trajectory points are converted into evolving graphs to detect the abnormality of the local road segment. Then we can analyze the causes of outlier trajectories combined with the perception of abnormal road environments. Finally, the GPS trajectories of real taxis were used to evaluate outliers, which proves the effectiveness and efficiency of the method.