SVM and Kalman filter-based road segment travel time prediction method and device

Abstract

The invention relates to the field of traffic management and control, in particular to a road segment travel time prediction method and a device based on SVM and Kalman filter. The training set data and the test set data composed of the set travel time information and the corresponding travel time are obtained, and the support vector regression machine is trained by the support vector machine regression algorithm. Obtaining an initial predicted travel time matrix according to the support vector regression machine and the test set data, constructing a state equation according to the intersection time delay and the road condition in the set travel section information, and constructing an observation equation according to the predicted travel time, the weather condition and the state equationcorresponding to the continuous sampling time before any time; according to the Kalman filter algorithm to solve the observation equation corresponding to the actual prediction time at any time, by selecting a continuous set of sampling time before any time corresponding to the prediction travel time, real-time change of the coefficient matrix of the Kalman filter, so that the entire prediction model is more reasonable and more accurate.

Description

Technical field [0001] The invention relates to the field of traffic management and control, in particular to a road section travel time prediction method and device based on SVM and Kalman filtering. Background technique [0002] The travel time of the road section is the main basis for the urban traffic management and control department to provide effective information to travelers, conduct reasonable traffic guidance, and improve traffic utilization. Therefore, Travel Time Prediction (TTP) has therefore become experts and scholars at home and abroad. Research hotspots. In recent years, with the vigorous development of intelligent transportation systems, research in this field has also made significant progress. At present, there have been many studies on travel time prediction, especially based on historical travel time prediction, at home and abroad. For example, improved K-means clustering method, improved moving average method and Bayesian classifier and rule-based classif...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a road section travel time prediction method and device based on SVM and Kalman filter, to solve the problem that the existing SVM and Kalman filter combination model cannot change the coefficient matrix of Kalman filter in real time according to the previous travel time of the vehicle. The problem of inaccurate travel time prediction results

[0005] In order to realize the prediction of road travel time, solve the problem that the existing SVM and Kalman filter combination model cannot change the coefficient matrix of Kalman filter in real time according to the vehicle's previous travel time, resulting in inaccurate prediction results of the current travel time

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