Trajectory data-based signal intersection periodic flow estimation method

Abstract

The invention relates to a trajectory data-based signal intersection periodic flow estimation method. The method comprises the following steps that: 1) the trajectory point data of sampled vehicles are acquired, and the key point information of the vehicles entering and leaving a queue is obtained; 2) a fitting method is adopted to estimate the queuing waves and evanescent waves of vehicle queuing, and the intersection point of the queuing waves and the evanescent waves is taken as the flow estimated value of queuing vehicles; 3) the density distribution function of full-cycle flow is obtainedaccording to the flow estimated value, and the proportion of non-stop vehicles in the full-cycle flow; and 4) a full-cycle flow estimation problem is transformed into a parameter estimation problem based on the Poisson distribution and M3 distribution of the non-queuing vehicles according to the density distribution function of the full-cycle flow, and a maximum likelihood estimation method is used to perform estimation, and the maximum-likelihood expectation-maximization method is adopted to perform solving, and the estimated value of the arrival flow of each cycle can be obtained. Comparedwith the prior art, the method of the present invention has the advantages of the fusion of model analysis and statistical analysis, the full use of trajectory information, wide applicability and thelike.

Description

technical field [0001] The invention relates to the field of intelligent traffic systems, in particular to a method for estimating periodical flow at signalized intersections based on trajectory data. Background technique [0002] At urban road signalized intersections, the traffic flow in each signal cycle plays an important role in traffic state estimation and signal control optimization. Traditional traffic detection mainly obtains flow data through fixed detectors represented by coils to estimate the actual arrival flow, but the deployment range of fixed detectors is limited, and there are also problems of detection failure. With the development and popularization of vehicle positioning and Internet of Vehicles technology, real-time trajectory data is gradually applied to urban traffic management. Taking Shanghai as an example, currently the city has established a floating car system based on 50,000 taxis and 20,000 buses, with a daily data transmission frequency of 10-...

AI Technical Summary

Problems solved by technology

The traffic estimation method based on fixed-point detectors mainly has the problems of high equipment layout and maintenance costs and low upload frequency, and the obtained detection indicators such as speed and traffic are based on the average value of the detection step, which cannot reflect the volatility and randomness of traffic flow. sex

Mathematical statistical methods are generally implemented by historical detection data, and most of the model parameters require empirical data calibration; methods based on basic graphs also need to fit the relationship between traffic flow parameters based on historical data, which is less general; and cellular transport models and other model analysis There are specific assumptions in the methods of traffic flow parameters, which abstract the relationship between traffic flow parameters, such as simulated arrival distribution, homogeneity assumptions, etc. Although the random characteristics of traffic flow are considered, the assumptions about the quantitative relationship of traffic flow parameters Varies from place to place, limited scope of application

The research on the use of trajectory for traffic estimation appeared late. Although the trajectory data has the advantages of high precision and strong real-time performance, in practical applications, due to the low capture rate, there are problems of data sparseness and large prediction errors, and the existing methods of using trajectory data The estimation accuracy of the flow estimation method will decrease under the condition of adaptive control

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