Detection of Anomalous Traffic Patterns and Insight Analysis from Bus Trajectory Data

© 2019, Springer Nature Switzerland AG. Detection of anomalous patterns from traffic data is closely related to analysis of traffic accidents, fault detection, flow management, and new infrastructure planning. Existing methods on traffic anomaly detection are modelled on taxi trajectory data and have shortcoming that the data may lose much information about actual road traffic situation, as taxi drivers can select optimal route for themselves to avoid traffic anomalies. We employ bus trajectory data as it reflects real traffic conditions on the road to detect city-wide anomalous traffic patterns and to provide broader range of insights into these anomalies. Taking these considerations, we first propose a feature visualization method by mapping extracted 3-dimensional hidden features to red-green-blue (RGB) color space with a deep sparse autoencoder (DSAE). A color trajectory (CT) is produced by encoding a trajectory with RGB colors. Then, a novel algorithm is devised to detect spatio-temporal outliers with spatial and temporal properties extracted from the CT. We also integrate the CT with the geographic information system (GIS) map to obtain insights for understanding the traffic anomaly locations, and more importantly the road influence affected by the corresponding anomalies. Our proposed method was tested on three real-world bus trajectory data sets to demonstrate the excellent performance of high detection rates and low false alarm rates

Real-Time Traffic Safety Evaluation in the Context of Connected Vehicles and Mobile Sensing

Recently, with the development of connected vehicles and mobile sensing technologies, vehicle-based data become much easier to obtain. However, only few studies have investigated the application of this kind of novel data to real-time traffic safety evaluation. This dissertation aims to conduct a series of real-time traffic safety studies by integrating all kinds of available vehicle-based data sources. First, this dissertation developed a deep learning model for identifying vehicle maneuvers using data from smartphone sensors (i.e., accelerometer and gyroscope). The proposed model was robust and suitable for real-time application as it required less processing of smartphone sensor data compared with the existing studies. Besides, a semi-supervised learning algorithm was proposed to make use of the massive unlabeled sensor data. The proposed algorithm could alleviate the cost of data preparation and improve model transferability. Second, trajectory data from 300 buses were used to develop a real-time crash likelihood prediction model for urban arterials. Results from extensive experiments illustrated the feasibility of using novel vehicle trajectory data to predict real-time crash likelihood. Moreover, to improve the model\u27s performance, data fusion techniques were proposed to integrated trajectory data from various vehicle types. The proposed data fusion techniques significantly improved the accuracy of crash likelihood prediction in terms of sensitivity and false alarm rate. Third, to improve pedestrian and bicycle safety, different vehicle-based surrogate safety measures, such as hard acceleration, hard deceleration, and long stop, were proposed for evaluating pedestrian and bicycle safety using vehicle trajectory data. In summary, the results from this dissertation can be further applied to real-time safety applications (e.g., real-time crash likelihood prediction and visualization system) in the context of proactive traffic management