Freeway Traffic Incident Detection Modeling And Applied Research

With the development of freeway,the logistics and the traveler are rising,the travel time is reducing and the work efficiency is improving,at the same time,some problems appear such as traffic congestion,serious accidents and traffic environment.these problems is caused by traffic accident.This paper is to leave under this background,the freeway traffic incident management system is showed to be the settle measures against traffic safety problem in this thesis.the study and apply of foreign and domestic the freeway traffic incident management system has been summarized.Traffic incident detection is the core of traffic incident management.This paper is studied for freeway traffic incident automatic detection algorithm.First,the development trend of traffic incident detection algorithm is mainly analyzed,and the application of neural network in traffic incident detection algorithm is described.On the summary domestic foundation based on neural network incident detection algorithm,the author have put forward a new freeway incident detection algorithm based on neural network and data fusion, The algorithm combines the traffic information of mainline with the information of tolling station to detect the incident.The LVQ neural network is used to classify the traffic incidents.LVQ has a simple network structure,but it is very effective and robust in traffic incidents detection. In order to improve the precision of the LVQ netural network for traffic incidents detection,Boosting algorithm is used to build an integration-neural network.Using the simulation data trains LVQ network,then detection effect is validated by actual data. as the simulation results shown,the new algorithm for incident detection has a lower false alarm rate and a higher detection rate,and it can improve the detection.The algorithm would have a bright prospect in china.