RFID spatio-temporal data traffic flow characteristic parameter prediction method

Abstract

The invention discloses an RFID spatio-temporal data traffic flow characteristic parameter prediction method which comprises the following steps: S1, acquiring traffic data of an RFID acquisition target road section, and performing spatio-temporal correlation analysis on the traffic data; S2, obtaining correlation between traffic flow characteristic parameters influencing the traffic state of thetarget road section and traffic flow characteristic parameters capable of reflecting the traffic state of the target road section; S3, predicting traffic flow characteristic parameters of the target road section in a traffic flow stable state and a traffic flow unstable state; and S4, carrying out weighted combination on the traffic flow characteristic parameters in the two states. The method solves the problems of large calculation amount, poor real-time performance and anti-interference capability, low prediction precision, low prediction efficiency and the like of an existing prediction method, and has the advantages that accurate, comprehensive and reliable traffic flow characteristic parameter prediction can be realized, and a new thought is provided for improving the traffic jam problem.

Description

technical field [0001] The invention relates to the traffic field, in particular to a method for predicting traffic flow characteristic parameters based on RFID spatio-temporal data. Background technique [0002] Accurate, comprehensive and reliable grasp of traffic flow characteristic parameter prediction information is the premise, foundation and key to realize traffic control, traffic guidance and provide real-time traffic information. At present, most of the predictions of traffic parameters at home and abroad are analyzed and studied through a large amount of data obtained by camera sensors, loop coils, taxi GPS, floating cars, etc. However, on the one hand, some sensors cannot recognize vehicle information well in harsh environments; on the other hand, most of the data collected by some sensors are sampling data. The invention provides a new RFID electronic license plate collection technology, which is not affected by weather, and the acquired data is full sample data...

AI Technical Summary

Problems solved by technology

[0003] After reviewing relevant papers and patents, most of the existing traffic flow characteristic parameter prediction methods are computationally intensive, poor real-time performance and anti-interference ability, low prediction accuracy, and low prediction efficiency.

Images