Predicting and Identifying Hot Spots by Applying Neuro- Fuzzy Systems

Abstract:-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 hot spots of the roads provides the possibility of promoting safety quality within optimization and safety for the roads. Identifying these weak points 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 has some problems. While the new research methods with abilities to infer meaning from complicated and ambiguous data seems useful. Therefore, in line with identifying the hot spots, Adaptive Neuro- Fuzzy Inference System (ANFIS) is used to predict hot spots on rural roads. In this process, a fuzzy inference system from Sugeno type is trained by 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 accidents frequency 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