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On Traffic Data Fusion and Travel Time Prediction Based on Gradient Calibration Method

Author: LangZuo
Tutor: DongHaiRong
School: Beijing Jiaotong University
Course: Control Theory and Control Engineering
Keywords: BP algorithm Traffic data fusion Travel Time Prediction Gradient correction method The hard shoulders monitor system
CLC: TP391.41
Type: Master's thesis
Year: 2008
Downloads: 224
Quote: 4
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Data fusion and prediction process as a new data processing technology , has broad prospects for the development and application in the transport sector . Through integration of traffic data , forecasts , can improve the accuracy and credibility of the traffic data , the control center as the data can make more accurate judgments on traffic conditions , and thus more effective traffic control . The research work of this paper is as follows: First, the background and significance of the study of the subject , described the principle of the traffic data collection technologies , and introduce a traffic data fusion technology and its principles of forecasting techniques ; followed by analysis of the artificial neural network data fusion model , and focuses on the principle of BP algorithm . Slow rate of convergence in order to solve the BP neural network training algorithm can easily fall into local minima and other shortcomings , without increasing the complexity of the algorithm , an improved BP algorithm - gradient correction method , and the Beijing Chaoyang Bridge consecutive cross section points through the data collected by the microwave detector simulation studies . Compared with the previous method , indicating that the gradient correction method to reduce errors and improve data accuracy has a significant effect; travel time prediction and improved gradient correction method , the basic BP algorithm fusion error compared , indicating that the gradient of the correction method is able to use different sections of the data for more accurate prediction of the sections of the travel time . Finally , to ease traffic congestion , the Fourth Ring Road in Beijing , for example exploratory research . Build emergency lane monitoring system platform , using microwave detector and camera to obtain traffic data , integration of the data obtained in the Fourth Ring Road . Current traffic conditions as the basis for analysis , and controls the emergency lane is turned on, in order to achieve the purpose of alleviate traffic congestion .

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