Discriminating method based on section detector for urban traffic state

Abstract

The invention discloses a discriminating method based on a section detector for urban traffic state. The existing traffic state discriminating method has low accuracy and less reliability. Based on the three traffic flow parameters of traffic flow, speed and time occupancy rate, the invention constructs a comprehensive congestion evaluation index, namely a traffic congestion index which is used for discriminating the road traffic state. The traffic state discriminating method provided by the invention comprises the steps of acquiring section traffic flow data at information publishing intervals, smoothing traffic flow parameters, calculating a speed congestion index and a occupancy congestion index, calculating a critical speed congestion index, calculating the traffic congestion index and discriminating the road traffic state. Based on the traffic information of a certain detecting section of a road and the overall consideration of various traffic flow parameters, the discriminating method based on the section detector for urban traffic state can automatically discriminate the traffic state of the road, and meanwhile utilize discriminating threshold values as few as possible and make full use of available resources, thereby facilitating the realization of a project.

Description

technical field [0001] The invention relates to an urban road traffic state discrimination method based on a section detector, which is used for urban traffic control and management and belongs to the field of intelligent traffic research. Background technique [0002] Scientific and reasonable estimation of road traffic status can provide dynamic decision-making basis for traffic managers and traffic participants, and induce the healthy development of urban traffic. [0003] At present, the identification of urban road traffic status is mainly based on floating car data, supplemented by video surveillance and manual observation. As the only taxi that can support the large-scale application of floating car data collection source, it is also a kind of operating vehicle, and its passenger load rate and departure rate vary greatly at different time periods, and it is often concentrated in areas where public activities are concentrated and important areas. On the passenger corr...

AI Technical Summary

Problems solved by technology

As the only taxi that can support the large-scale application of floating car data collection source, it is also a kind of operating vehicle, and its passenger load rate and departure rate vary greatly at different time periods, and it is often concentrated in areas where public activities are concentrated and important areas. On the passenger corridor, this driving characteristic will affect the sample size and calculation accuracy of the floating car used to calculate the travel speed of the road section; due to the extremely limited video surveillance resources, human factors have a greater impact on the results of manual observation and video surveillance, so now The accuracy and reliability of the traffic state discrimination method are low

At the same time, most of the existing traffic state discrimination methods use speed as the only basis for discrimination, and have high requirements for the stability and reliability of speed data, and the time occupancy rate is also an important factor to measure traffic state.

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