REAL-TIME DATA FUSION FOR ARTERIAL STREET INCIDENT DETECTION USING NEURAL NETWORKS

This research contributes to the development of an automatic incident detection system for detecting traffic-delaying events on arterial street networks for an Advanced Traveler Information System demonstration called ADVANCE. Data describing current traffic conditions will be gathered in real time from two distinct sources: inductive loop detectors and specially equipped vehicles that measure and report their travel times on roadway links. Two approaches are considered for data fusion, the combination of information from these sources to produce a single decision about the presence or absence of incidents on each link. In the integrated fusion approach, observed traffic data are combined directly using a neural network. In the algorithm output fusion approach, separate incident-detection algorithms individually preprocess data from each source, reporting outputs that are combined using a neural network. Data for calibrating these system components were generated using computer simulation. The algorithm output fusion network performed better than the other approach, detecting over 80% of the incidents with almost no false alarms. Fusing algorithm outputs using neural networks was thus found to improve the capability provided by separate source incident detection algorithms operating alone. The importance of validating these results through calibration and testing with field data, as well as improving performance through introduction of an additional data source is discussed.