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Study for Traffic Incident Detection and Simulation in Freeway

Author: ZhangKai
Tutor: JuYongFeng
School: Chang'an University
Course: Traffic Information Engineering \u0026 Control
Keywords: Highway Queuing theory Traffic Incident Detection Generalized regression neural network Traffic state parameters
CLC: U491.116
Type: Master's thesis
Year: 2009
Downloads: 277
Quote: 4
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Abstract


With China's highway construction and development, highway safety gradually attracted people's attention, and highway traffic incident detection technologies improve and mature for Highway Safety plays a key role. When the freeway event of an emergency, traffic incident detection techniques can discover so as to avoid causing heavy casualties and economic losses, reducing the probability of occurrence of the second event. Based on the data highway operations research to establish queuing theory simulation platform. In order to more truly reflect the operation of certain sections of the vehicle, the vehicle through the simulation carried out constantly tracked and simulated traffic incidents, generate the required data. In this paper, the existing traffic incident detection algorithm research, and comparative analysis of their effectiveness, detailed analysis of the traffic incident detection algorithm to evaluate the effectiveness of methods. By analyzing the BP neural network, RBF network, GRNN (generalized regression neural network) of the basic principles of full advantage of neural network theory and its application to traffic incident detection algorithm is proposed based on BP network, RBF network, GRNN (generalized regression neural network) traffic incident detection algorithms. Data simulation platform via the motorway traffic state parameter data obtained, the training and testing based on neural network traffic incidents algorithm. Through traffic incident detection based on BP network algorithm based on RBF network traffic incident detection algorithm based on GRNN (generalized regression neural network) traffic incident detection algorithm three algorithms comparison test and analysis results based on GRNN (generalized regression neural network) traffic incident detection algorithms approximation capability classification ability and learning speed, with BP neural network and RBF network can not exceed advantages. GRNN finally converges to the optimal sample size large agglomeration return side, when the sample data is lacking, the predicted effect is better, and can handle volatile data. Traffic incident detection algorithm based on GRNN obtain higher traffic incident detection rate and low false alarm rate, and the coordination of evaluation also better than the other two kinds of algorithms. Meanwhile, the paper also states the existing traffic vehicle classification parameters to increase traffic. Compared two groups of test traffic state parameters and analysis shows that the increase in traffic state vehicle classification parameters traffic, traffic events can be improved algorithm of detection rate and false alarm rate reduction.

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CLC: > Transportation > Road transport > Technical management of traffic engineering and road transport > Traffic engineering and traffic management > Traffic Survey and Planning > Traffic survey > Traffic monitoring and detector
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