Real-time vehicle trajectory data-based intersection flow estimation method

Abstract

The invention relates to a real-time vehicle trajectory data-based intersection flow estimation method. The method includes the following steps that: 1) a research period [0, T] is divided into a plurality of consecutive basic time intervals, and the basic time intervals are classified; 2) a vehicle departure time likelihood function is calculated according to vehicle departure time condition probabilities corresponding to different basic time interval types under a given arrival time condition on the basis of the classified basic time intervals, and a final likelihood function is calculated;and 3) the vehicle departure time likelihood function is solved, so that vehicle arrival rates in the basic time intervals can be obtained. Compared with the prior art, the method of the invention canadapt to low-sampling frequency and low-sampling rate data environments and does not need the fusion of historical data. The method has the advantages of high robustness, high real-time performance,high accuracy and the like.

Description

technical field [0001] The invention relates to the field of traffic control, in particular to an intersection flow estimation method based on real-time vehicle track data. Background technique [0002] As the main part of the urban road network, signalized intersections often cause traffic congestion due to the periodic alternation of traffic lights, which greatly restricts the overall operating efficiency of the urban road traffic system. As an important indicator for evaluating intersection operation, cycle flow can be used to indirectly estimate queue length, vehicle delay, parking times, and travel time on the one hand, and can be directly fed back for signal timing optimization on the other hand. [0003] At present, the research on flow estimation at home and abroad is mainly based on fixed-point detectors. The prediction methods include mathematical statistics methods such as filtering algorithms and model analysis methods such as basic graphs and cellular transmissi...

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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