Intelligent Traffic Anomaly Diagnosis Through the Integration of Diverse Information Sources

While most transportation centers have the capability to collect and access various types of information such as planned and unplanned traffic incidents, and real-time and archived sensor data, many have not integrated this information in a coherent fashion. Information about anomalous highway situations can be collected from a variety of (currently) standalone sources and if integrated, would allow presentation of this data to transportation professionals and the traveling public, thereby enabling real-time and long term planning. The goal of this project was to simulate incoming data as it would be collected by a transportation agency from various sources such as inductive loop detectors, global positioning system devices, human reporters monitoring the system, emergency management personnel, and planned incident reports. In this project, a software prototype was developed that integrates incoming information about the roadway and uses this information in a centralized and intelligent computer algorithm that can provide transportation experts with accurate and timely diagnoses of traffic anomalies.