An Application of Fuzzy Logic in Urban Traffic Incident Detection
Traffic congestion in urban areas is an increasing problem around the world. Traffic incidents (such as accidents) are considered as the major source of traffic congestion. Traffic incidents have negative impacts on traffic flow, air pollution and fuel consumption. As a result, increasing interest in finding new techniques to deal with this issue has been shown. Traffic incident-management systems can decrease the effect of such events and keep roads capacity as close as possible to normal levels. Incident detection system is an important part of any incident management system.
This thesis proposes a new approach to incident detection in urban traffic networks using fuzzy logic theory with the objective of reducing traffic delays and increasing road safety. The proposed detection system can be then integrated with a traffic incident management system to reduce traffic congestion related to non-recurrent incident situations. A methodology has been established based on fuzzy logic for detecting incident status in urban areas using detector accumulative count differences. Three fuzzy models were developed and evaluated using simulated data (generated using the commercial software: PTV VISSIM by PTV Group). The fuzzy model can detect incident status on a regular basis (every minute). Performance measures were introduced to capture the capabilities of the suggested models in detecting incidents. The dissertation concludes with a summary of the major findings, recommendations and future research