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A dynamic road network traffic demand forecasting method and system

A technology of traffic demand and dynamic road network, applied in the direction of road vehicle traffic control system, traffic control system, traffic flow detection, etc. The effect of network optimization, alleviation of traffic congestion, convenient use and maintenance

Active Publication Date: 2019-11-08
DUOLUN TECH CO LTD
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  • Abstract
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AI Technical Summary

Problems solved by technology

[0003] In modern traffic control systems, the accuracy of traffic demand prediction directly affects the analysis of congestion hotspots and the parameters of the signal optimization model, which indirectly affects the deployment of police force and the generation of signal optimization schemes, which will lead to untimely police dispatch when congestion occurs. May also cause traffic accidents

Method used

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  • A dynamic road network traffic demand forecasting method and system
  • A dynamic road network traffic demand forecasting method and system
  • A dynamic road network traffic demand forecasting method and system

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Embodiment Construction

[0042] Attached below figure 1 And embodiment further illustrate the present invention.

[0043] A method for forecasting traffic demand on a dynamic road network, comprising the following steps:

[0044] Step 1: S1. Data collection

[0045] Collect traffic flow data from various traffic flow detection equipment and write it into the original database of traffic flow; traffic flow detection data comes from traffic detection equipment on the road surface, mainly including: video detector, geomagnetic detector, microwave detector, bayonet , signal machine, intersection signal equipment, etc., the equipment collects traffic flow data and transmits it to the collection center through the communication link.

[0046] Collect geographic information data and write it into the geographic information database. The following geographic information data are mainly collected: roads, intersections, and road sections. Roads need to include road grades, and road sections need to include a...

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Abstract

The invention discloses a traffic demand forecasting method and system for a dynamic road network. According to the invention, multi-source traffic data is collected and integrated all the time; dynamic grid data is formed through overlapping traffic flow data, geographic information data and traffic management information on the road network; and a data forecasting model is established on the basis of dynamic road network data. The method has characteristics of good real time performance, high accuracy and the like, can improve jamming study and judge precision effectively, so as to provide reasonable traffic demand data (queue, flow, speed, occupancy rate and the like) for signal optimization. The system can improve an optimization model and relieves traffic jams.

Description

technical field [0001] The invention relates to a method and a system for predicting traffic demand on a dynamic road network, which are used for traffic analysis and belong to the field of traffic control. Background technique [0002] In the field of traffic control, most of the traffic analysis mainly focuses on the collected traffic flow data, analyzes the data quality, and uses the road network data to analyze the congestion status and short-term prediction of the data, and does not combine traffic incidents, construction roads, traffic Comprehensive analysis of traffic management data such as traffic control, the accuracy of analysis and prediction is relatively not very high. [0003] In modern traffic control systems, the accuracy of traffic demand prediction directly affects the analysis of congestion hotspots and the parameters of the signal optimization model, which indirectly affects the deployment of police force and the generation of signal optimization schemes...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G08G1/01G06Q10/04G06F16/29G06F16/23
CPCG06F16/29G06Q10/04G08G1/0125
Inventor 张秀梅杨志奎林宝华叶剑
Owner DUOLUN TECH CO LTD
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