A road network traffic situation prediction method and system based on deep learning

Abstract

A depth learning-based road network traffic situation forecast method comprises the following steps of S1, acquiring multi-source traffic data and road network static configuration information, and building a traffic flow parameter model, wherein the multi-source traffic data comprises internet road segment speed data, detector flow data and signal machine control scheme data, and the road networkstatic configuration information comprises road network space geographical position information, intersection number, road segment class, road segment length, road segment number, lane number and lane function; S2, analyzing road network congestion relevancy, and building a basic forecast group; S3, building a dual-stage attention mechanism-based depth learning traffic situation forecast model; and S4, building a traffic situation forecast system. The forecast accuracy and the transportability are relatively good.

Description

Technical field [0001] The invention relates to the field of intelligent traffic engineering, and in particular to a road network traffic situation prediction method and system based on deep learning. Background technique [0002] The main goal of Intelligent Transportation System (ITS) is to realize the intelligence of traffic control and traffic guidance, and reliable traffic situation prediction is the decisive factor for the effective realization of both. That is, using current traffic data and historical traffic laws to construct a reliable prediction model to predict the traffic flow parameters (speed, flow, etc.) of the road network at the next moment. The predicted results can be used as the basis for traffic management departments to adjust the traffic control plan to ensure the stable and efficient operation of the traffic system; at the same time, it can remind vehicles to adjust their driving routes in time to reduce travel delays for citizens. [0003] The complex roa...

AI Technical Summary

Problems solved by technology

[0005] In order to overcome the shortcomings of the poor prediction accuracy and portability of the existing road network traffic prediction methods, the present invention provides a road network traffic situation prediction method based on deep learning, which adopts the method of multi-source traffic data fusion, combined with trajectory data, Fixed detector data and signal machine control schemes, etc., to build a multivariate traffic flow parameter model

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