This paper analyzes the use of Bluetooth-based travel times, for Automatic Incident Detection (AID) purposes. Automatic incident messages were derived for both actual and simulated data through the use of an AID algorithm. This algorithm was developed by Technical University of Munich (TUM) and takes into account the travel times between two detector stations. Actual travel times were determined based on Bluetooth data of detectors installed along a 15 kilometer section of the A9 motorway in the context of the so called iRoute project and analyzed towards AID purposes. Due to the limited variety of incidents during the above mentioned period, a Vissim model of the section was set up for further analysis. Different scenarios of traffic situation, incidents and detector layout were introduced in the Vissim model and travel times were generated, processed and then run through the TUM algorithm. The performance measures Detection Rate (DR), False Alarm Rate (FAR) and Mean Time To Detect (MTTD) were used for the analysis of the incident messages' quality of both actual and simulated data. The performance measures were compared for every incident and conclusions were reached about the importance of detectors' distance, as well as the incident type and location, to incident detection.     «

This paper analyzes the use of Bluetooth-based travel times, for Automatic Incident Detection (AID) purposes. Automatic incident messages were derived for both actual and simulated data through the use of an AID algorithm. This algorithm was developed by Technical University of Munich (TUM) and takes into account the travel times between two detector stations. Actual travel times were determined based on Bluetooth data of detectors installed along a 15 kilometer section of the A9 motorway in the...     »