Vehicle queuing length estimation method based on Kalman filtering in Internet of Vehicles environment

Abstract

The invention discloses a vehicle queuing length estimation method based on Kalman filtering in an Internet of Vehicles environment. The method is applied to the technical field of traffic engineering, and aims to solve the problems that an existing Kalman filtering method cannot provide estimation of second-level queuing length in queue length estimation and does not fully utilize real-time Internet of Vehicles data. The method comprises the following steps: estimating traffic volume, saturated flow and permeability according to the Internet of Vehicles trajectory data; and then, consideringthe estimation error, proposing a Kalman filtering method to estimate the queuing length in real time based on the estimated parameters (namely traffic volume, saturated flow and permeability) and traffic signal timing data. According to the method provided by the invention, second-level queuing length estimation can be realized, and the method has very important significance for evaluating the traffic signal performance and optimizing the traffic signal timing.

Description

technical field [0001] The invention belongs to the technical field of traffic engineering, in particular to a vehicle queuing length estimation technology. Background technique [0002] Queue length is considered to be the key to calculating vehicle delays, parking, emissions and optimizing traffic signal timing schemes at signalized intersections. In addition, accurate queue length data can assist traffic engineers to better evaluate the effectiveness of traffic signal control systems and adjust traffic signal timing schemes. Therefore, it is very important to estimate the queue length accurately and in real time. [0003] At present, there have been extensive researches on queue length estimation methods. These studies generally fall into two broad categories: input-output models and shock wave models. However, existing queue length estimation methods are mainly based on data from stationary detectors and rovers. And most research has focused on fixed detectors. Ther...

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Problems solved by technology

[0005] In order to solve the problem that the existing Kalman filter method cannot provide an estimate of the second-level queue length in estimating the queue length, and does not make full use of the real-time Internet of Vehicles data, the present invention proposes a vehicle queue length based on Kalman filter in the Internet of Vehicles environment Estimation method, using real-time Internet of Vehicles data, can estimate the queue length second by second

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