Traffic state weak signal perception studying and judging method based on multi-source data fusion

Abstract

The invention discloses a traffic state weak signal perception studying and judging method based on multi-source data fusion. The method comprises the steps that gridding division is conducted on roads according to road attributes and traffic data attributes; acquiring, preprocessing and fusing traffic data; according to the DSRC section data, estimating the number of vehicles on a certain sectionof highway section by utilizing mobile phone base station data, and calibrating through positioning data and videos; identifying the running state of the regional vehicle; carrying out real-time dynamic analysis on the traffic state change parameters; and comprehensively reflecting the traffic transition state change process through the traffic operation state retention time and the traffic statechange speed trend. According to the invention, the multi-source data obtained from multiple aspects can be fully utilized, the traffic state weak signal perception research and judgment method is provided, the traffic state change trend can be timely and accurately perceived in the traffic state transition stage, active dispersion and control are further realized, traffic congestion is avoided to the greatest extent, and the traffic operation efficiency and safety are ensured.

Description

technical field [0001] The invention belongs to the technical field of intelligent transportation. Background technique [0002] The perception and judgment of traffic status has an important impact on the safety and efficiency of road traffic. How to use existing technical resources to monitor and predict traffic status in a timely and accurate manner has always been a hot research issue. [0003] At present, research on traffic state perception mostly relies on fixed vehicle detector data, but due to cost constraints, the number of vehicle detectors is very limited, and the obtained vehicle detector data is very limited. The few methods that use multi-source data for traffic state perception also have problems such as single data source and type, and low data utilization to a certain extent, which seriously affects the accuracy and spatial coverage of traffic state monitoring and prediction. [0004] In addition, the existing technology focuses on evaluating and analyzin...

AI Technical Summary

Problems solved by technology

[0003] At present, research on traffic state perception mostly relies on fixed vehicle detector data, but due to cost constraints, the number of vehicle detectors is very limited, and the obtained vehicle detector data is very limited

The few methods that use multi-source data for traffic state awareness also have problems such as single data source and type, and low data utilization to a certain extent, which seriously affects the accuracy and spatial coverage of traffic state monitoring and prediction.

[0004] In addition, the existing technology focuses on evaluating and analyzing the traffic state at a certain moment in the present or in the past and predicting the traffic state in a certain period of time in the future, and cannot perceive the traffic in a short period of time from real-time subtle traffic state changes Operational trend, that is, weak signal perception of traffic status, which makes it difficult to actively manage traffic conditions in the future and relieve traffic operation pressure