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Item type:Item, Fuzzy logic-based traffic incident detection system with discrete wavelet transform(2014-01-01) ;La-Inchua, Jaraspat ;Chivapreecha, SorawatThajchayapong, SuttipongThis paper presents a fuzzy logic-based traffic incident detection system to detect a lane-blocking traffic incident that usually causes of traffic congestion. The proposed system uses fuzzy logic to identify traffic status as normal and abnormal. Macroscopic and microscopic traffic variables, namely, mean speed and standard deviation of inter-arrival time are used as inputs to the fuzzy inference system (FIS). As traffic variables have many fluctuations which are considered as noisy signals, discrete wavelet transform (DWT) as used for de-noising and also extracting features from noisy signals. It is found that the proposed system that uses DWT can give higher detection rate when compared with the system without DWT. Furthermore, the majority voting is also applied to the outputs of FIS in order to increase detection rate. Finally, based on simulation results, the performance of the proposed detection system for lane-blocking traffic incidents will be shown. © 2014 IEEE. - Some of the metrics are blocked by yourconsent settings
Item type:Item, A new system for traffic incident detection using fuzzy logic and majority voting(2013-09-02) ;La-Inchua, Jaraspat ;Chivapreecha, SorawatThajchayapong, SuttipongThis paper presents a system to detect lane-blocking traffic incidents which are amongst major causes of traffic jam. The proposed system uses fuzzy logic to identify traffic status as normal and abnormal. Mean speed and standard deviation of inter-arrival time are used as inputs to the fuzzy inference system (FIS), and then, the majority voting is applied to the outputs of FIS to improve detection rate and mean time to detection. Furthermore, based on simulation results, we show that the proposed lane-blocking detection system is very suitable for real-time implementation. © 2013 IEEE.
