Method for Automatic Detection of Traffic Incidents Using Neural Networks and Traffic Data

Incidents on the highway disrupt traffic flow and the cost of delay caused by incidents is significant. To reduce the impact of an incident, a traffic management center needs to quickly detect and remove it from the highway. Quick and efficient automatic incident detection has been a main goal of the transportation research for many years. Also many algorithms based on loop detector data have been developed and tested for the Automatic Incident Detection (AID). However, many of them have a limited success in their overall performance in terms of detection rate. We think that the changes in average traffic speed in case of traffic incidents have certain patterns that are different from the normal case. Our research tries to automatically detect traffic incidents using neural networks on traffic data. In the field tests, the suggested model performed better than existing algorithms in the literature.