Method for estimating traffic state parameter based on confidence of speed of float car

Abstract

The invention provides a method for estimating a traffic state parameter (speed) based on confidence optimization by analyzing data characteristic of the float car, introducing a confidence factor and mainly considering float car data sample number, the speed and time continuity of a traffic state. The average speed of the float car is estimated by confiding most of vehicles and high-speed vehicles and combining speed data of a previous time interval and historical speed data. The influence of abnormal data on a calculation result is effectively reduced, so that the calculation result is closer to an actual road situation; meanwhile, the problem of traffic state parameter estimation under the condition of sample size deficiency is solved, so that the accuracy and the stability of the traffic state parameter estimation are greatly improved.

Description

technical field [0001] The present invention relates to the technical field of intelligent transportation, in particular to the technical field of floating car information collection, and more specifically, to a method for estimating traffic state parameters based on the confidence degree of the speed of the floating car. Background technique [0002] Float car, also known as "probe car", is one of the advanced technical means used in the international intelligent transportation system (ITS) to obtain road traffic information in recent years. Its basic principle is: according to the vehicle position, direction and speed information regularly recorded by the floating vehicle equipped with the vehicle-mounted global positioning system during its driving process, apply map matching, path estimation and other related calculation models and algorithms to process, so that the position of the floating vehicle The data is associated with urban roads in time and space, and finally tr...

AI Technical Summary

Problems solved by technology

[0010] Among the above-mentioned technical links, the estimation of traffic state parameters (mainly referring to vehicle speed) is the core technical link of traffic state discrimination. If the traffic state parameters cannot be estimated effectively and close to the real traffic state, the discrimination of the entire traffic state will be impossible.

[0011] Among the existing traffic state estimation methods, the more common ones are data exclusion methods, which artificially collect uncertain return information, such as data with large and small speeds, data with large return time intervals, and vehicle speeds less than It is simple to exclude 75% of the data in the previous return interval, but the obvious consequence is that the real and useful information in the actual road is often removed together. The final result is that the road operation has always remained stable, but Can not fully reflect the comprehensive real road traffic conditions

There is also an estimation method that simply sets parameters and fuses historical data, but there are various problems after all, and it is difficult to reflect the real traffic conditions

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