Learning GPS Point Representations to Detect Anomalous Bus Trajectories

Abstract

Discovering anomalous bus trajectories can benefit transportation agencies to improve their services by helping them to deal with unexpected events such as detours or accidents. In this work, we propose a deep-learning strategy, which we name Spatial-Temporal Outlier Detector (STOD), that predicts the spatial/temporal anomaly degree of a bus trajectory by using learned representations of its GPS points. To calculate the score, STOD learns the regular behavior of bus trajectories by building a model that predicts the route id of buses. The degree of uncertainty on this prediction, measured by the entropy of the output class probability distribution, indicates the anomaly score of the trajectory. To perform the classification, STOD represents each point of a trajectory by the concatenation of two different representations. The first one (PAC embedding) is generated by the Point Activity Classifier (PAC) by leveraging temporal and spatial features on a stacked deep-learning model to predict the semantics of the point in terms of its bus activity (in route, bus stop, traffic signal, and other stops). The second representation (Geo embedding) captures the spatial relationship between a point and its geographical neighbors by applying a word embedding technique on the set of all trajectories. The experimental evaluation shows that our model is effective for filtering noisy trajectories since it outputs higher anomaly scores for both spatial and temporal anomalous trajectories than regular ones.

MobApp: A Data Visualization Tool for Trajectory Analysis

“…For this purpose, the application provides a drop-down component for users to select routes, visualize precision, and recall curves. Figure 3c shows the mentioned components and the four available algorithms used at this demo: GMVSAE [Liu et al 2020], STOD [Cruz and Barbosa 2020], iBOAT [Chen et al 2013],…”

Research on Real-Time Anomaly Detection Method of Bus Trajectory Based on Flink

“…Fu et al [3] validated the spatial auto-correlation of five taxi over-speed events with taxi GPS data. Cruz et al [4] used the spatio-temporal characteristics of GPS points to predict the degree of spatial or temporal anomaly in bus trajectories.…”

“…Although these methods can effectively satisfy the needs of trajectory anomaly detection in their own scenarios, they are limited to running in a single node in batch mode [1][2][3][4]. Therefore, their capacities are not enough to meet the requirements of large-scale detection.…”

“…For example, if a bus deviated from the designated route during a trip, the existing solution typically becomes aware about this anomaly within a few minutes. On the other hand, the current solutions [1][2][3][4] fail to handle a large-scale bus transportation system. Currently, many existing works have been conducted on abnormal detection for vehicle GPS trajectories [1][2][3][4].…”

“…On the other hand, the current solutions [1][2][3][4] fail to handle a large-scale bus transportation system. Currently, many existing works have been conducted on abnormal detection for vehicle GPS trajectories [1][2][3][4]. These methods conduct anomaly detection on historical GPS trajectories in the batch mode.…”