Predicting and identifying traffic hot spots applying neuro-fuzzy systems in intercity roads

Providing safety in roads for the purpose of protecting human assets and preventing social and economic losses resulted from road accidents is a significant issue. Identifying the traffic hot spots of the roads provides the possibility of promoting the road safety which is also related to investigate frequency and intensity of occurred accidents. Accidents are multidimensional and complicated events. Identifying the accident factors is based on applying a comprehensive and integrated system for making decisions. Therefore, applying common mathematical and statistical methods in this field can be resulted in some problems. Hence, the new research methods with abilities to infer meaning from complicated and ambiguous data seem useful. Therefore, along with identifying the traffic hot spots, adaptive Neuro-Fuzzy inference system is used to predict traffic hot spots on rural roads. In this process, a fuzzy inference system from Sugeno type is trained applying hybrid optimization routine (back propagation algorithm in combination with a least square type of method) and accident data of Karaj-Chalus road in Tehran Province. Then the system was tested by a complete set of data. Finally, the stated system could predict 96.85 % of accident frequencies in the studied blocks. Furthermore, the amount of effective false negative in all cases included only 0.82 % of predictions, which indicated a good approximation of predictions and model credibility.

Keywords
Accident, safety, optimization, least square, sugeno, hybrid, back propagation