Depth learning-based road network traffic situation forecast method and system

A traffic situation, deep learning technology, applied in the direction of road vehicle traffic control system, traffic control system, traffic flow detection, etc., can solve problems such as poor prediction accuracy and portability

Active Publication Date: 2019-12-13
ZHEJIANG UNIV OF TECH +1
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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

Method used

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  • Depth learning-based road network traffic situation forecast method and system
  • Depth learning-based road network traffic situation forecast method and system
  • Depth learning-based road network traffic situation forecast method and system

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Embodiment Construction

[0139] The present invention will be further described below in conjunction with the accompanying drawings.

[0140] refer to Figure 1 to Figure 5 , a road network traffic situation prediction method based on deep learning, including the following steps:

[0141] S1, obtain multi-source traffic data and road network static configuration information, construct traffic flow parameter model; Described obtain multi-source traffic data, comprise Internet link speed data (Gaode map API, Baidu map API), detector flow data ( SCATS detector), traffic signal control program data; the static configuration information of the road network, including road network spatial geographic location information, intersection number, road section grade, road section length, road section number, lane number, lane function, etc.;

[0142] S2. Analyze the correlation of road network congestion and build basic forecasting groups;

[0143] S3. Construct a deep learning traffic situation prediction mode...

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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, in particular to a method and system for predicting road network traffic situation based on deep learning. Background technique [0002] The main goal of Intelligent Transportation System (ITS) is to realize the intelligentization of traffic control and traffic guidance, and reliable traffic situation prediction is the decisive factor for the effective realization of both. That is to use the current traffic data and historical traffic rules 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 the traffic management department 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. [...

Claims

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Application Information

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IPC IPC(8): G08G1/01G08G1/065
CPCG08G1/0129G08G1/0133G08G1/065
Inventor 梁荣华谢竞成吴越丁楚吟徐甲邹开荣李瑶杨宪赞周浩敏温晓岳
Owner ZHEJIANG UNIV OF TECH
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