|M.Sc Student||Livertovsky Vladislav|
|Subject||Incident Detection Algorithms on a Freeway|
|Department||Department of Civil and Environmental Engineering||Supervisor||Professor Shalom Hakkert|
The high cost of congestion caused by incidents such as accidents, disabled vehicles, construction work and other events that result in a capacity reduction of the facility, has prompted a growing worldwide interest in developing efficient and effective automated incident detection methods. The benefits to be derived from early incident detection and prompt response in terms of providing real- time traveler information and timely dispatch of emergency services can drastically reduce traffic delays, air pollution and improve road safety and real-time traffic control.
The detection of freeway incidents is an essential element of an area’s traffic management system. Incidents need to be detected and handled as promptly as possible in order to minimize traffic delay. Various algorithms and detection technologies are reviewed and examined for combinations, which offer optimized detection performance.
This study represents an effort to compile, compare and rank some of the available incident detection strategies. Based on an extensive literature review process the California algorithm No.8, DELOS, UCB-Berkeley and ARRB VicRoads were selected for testing. The performance of these algorithms was assessed using detailed incident and traffic data obtained from the Ayalon Highway.
An analysis of each one of the four algorithms is introduced in Chapter no.5. Specific software was developed to optimize the incident detection algorithms mentioned above. Each algorithm was calibrated with a technique in which the thresholds were generated from a uniform distribution (between bounds obtained from the literature). During the calibration, algorithm parameters were optimized and the calibration data was analyzed.