Monitoring of Traffic Anomalies using Microscopic Traffic Variables in Vehicular Transportation Networks

This thesis proposes methodologies to monitor traffic anomalies using microscopic traffic variables measured by equipped vehicles sharing information with one another and/or localized road-side infrastructure. The proposed methodologies can identify not only traffic anomalies that lead to traffic incidents, but also small transient deviations that are usually difficult to detect. Firstly, the thesis addresses the issue of anomaly detection where novel supervised and unsupervised algorithms are proposed. The unsupervised algorithm uses the change in variability of microscopic traffic variables to detect traffic anomalies, which is also shown to outperform previous algorithms monitoring ideally placed loop detectors. The supervised algorithm can identify anomalies under different traffic regimes with 100% detection rate and low false alarm rate when applied to real-world data, which presents a signi cant improvement over the unsupervised algorithm. It is also shown that the proposed algorithms can detect anomalies even when the microscopic traffic variables are aggregated and missing. Secondly, three classification algorithms are proposed, which can be integrated with the previously proposed detection algorithms. The first algorithm identifies a lane-blocking, which is a well-known type of anomaly that often leads to traffic incidents, and is shown to outperform existing algorithms. The second algorithm identifies real-world cases of transient anomalies as well as incident precursors by assessing spatial-temporal changes of microscopic traffic variables. The third algorithm addresses the problem of misclassifications under different traffic regimes by employing a certainty-based decision function, and it is shown to successfully classify all anomaly cases in the real-world data set. Finally, the study is extended to the inference of traffic anomalies at a location where traffic variables could not be measured directly. The key contributions of the proposed algorithm are the ability to infer both normal and anomalous traffic conditions at a target location by assessing only microscopic traffic variables from adjacent locations, and the ability to estimate lane-level traffic flow, time occupancy and inter-arrival time. Based on real-world data, it is shown that the proposed algorithm outperforms a Kalman filter-based approach