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Research on Intersection Signal Fuzzy Control Based on Traffic Flow Prediction with SVM

Author: HaoLei
Tutor: GuanKe
School: Chang'an University
Course: Traffic Information Engineering \u0026 Control
Keywords: Traffic forecasts Fuzzy Control Intersection Support Vector Machine
CLC: U491.51
Type: Master's thesis
Year: 2011
Downloads: 46
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Abstract


A high degree of complexity and randomness , this paper proposes a intersection optimize fuzzy control method based on the intersection of the main road traffic flow forecasting support vector machine for intersection traffic flow in the city . Traditional intersection fuzzy control algorithm is in the process of directing traffic , the traffic police in a - phase green light is about to the end to determine whether the need to delay the green light of the current phase , in which the delay time depends on the current phase of the traffic flow and the next phase of traffic flow comprehensive comparison of the queue length , after the traffic police are determined based on experience signal timing . This article first to predict the traffic flow , the key is how to build a suitable prediction model . Article SVM (Support Vector Machine, SVM) and support vector machine is a solution to the nonlinear regression network model has strong predictive ability , high prediction accuracy and fast convergence rate , minimum squares support vector machine (Least Squares Support Vector Machine, LS-SVM) to build a traffic flow forecasting model , and compare the results of the prediction model . Due to the high accuracy of the LS-SVM forecasting methods , this paper the prediction method of traffic flow data as input to the fuzzy controller . Secondly, as the research object to the main road intersects the intersection using fuzzy control toolbox to establish the principle of intersection of four - phase fuzzy controller and fuzzy control . The next two phases of the LS-SVM predicted queue length as the fuzzy controller input , according to the fuzzy inference drawn next phase green , release this phase by the forecast after a green phase duration , but not yet all queuing of the vehicle. Finally , with traditional intersection timing control compared to the simulation and analysis of the model in Matlab Simulink environment , the simulation results show that the fuzzy control based on data predicted average vehicle delay when asked than timing control to improve the intersection vehicle the capacity to achieve the purpose of maintaining intersection traffic unobstructed .

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CLC: > Transportation > Road transport > Technical management of traffic engineering and road transport > Traffic engineering and traffic management > Line of traffic safety facilities > Traffic signals
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