Main road coordination control optimization method based on sampling track data

Abstract

The invention relates to a main road coordination control optimization method based on sampling track data. The method comprises the following steps of: 1), obtaining sampled vehicle track data, preprocessing the data to obtain track feature vectors and a prior arrival rate matrix; 2), on the basis of the prior arrival rate matrix, judging arrival states of sampled tracks in a period according toany group of set signal timing data, reconstructing track waves, estimating mapping arrival time and practical arrival time of the tracks, and obtaining signal control delay of the tracks at each intersection; and 3), accumulating the signal delay of all tracks at all intersections, obtaining total delay of all sampled tracks of main roads and an analysis formula for signal timing parameters at each intersection, establishing an optimization model based on constraints among the timing parameters, and carrying out solution through utilization of a multi-bee colony optimization algorithm. Compared with the prior art, the method has the advantages that space-time association is taken into consideration, a coordination effect is relatively good, and applicability is wide.

Description

technical field [0001] The invention relates to the field of traffic control, in particular to an optimization method for arterial coordinated control based on sampling trajectory data. Background technique [0002] As the backbone of the traffic network, urban arterial roads undertake the functions of long-distance express traffic and commuter traffic, but congestion is the most frequent and serious. Therefore, urban arterial roads often adopt coordinated control strategies to ensure the operating efficiency of main line traffic. Although the current signal control field has developed to the stage of induction control and adaptive control, and the application of induction control systems such as SCOOT and SCATS has also been implemented, the multi-period timing control strategy is still relatively difficult in the current urban arterial road traffic control and management applications. Extensive and cost-effective way. [0003] At present, the main data input for timing ar...

AI Technical Summary

Problems solved by technology

[0003] At present, the main data input for timing arterial control is the hourly aggregate flow of each flow direction of the arterial road obtained through timing detectors or manual measurement. On the one hand, the operation and maintenance costs of the equipment are high, and on the other hand, the data accuracy will also be affected by equipment damage or detection failure. Impact

In addition, the relationship between the coordination target and the data input of the existing timing control model is mostly deduced based on a specific theory, and the effect of the flow input on the optimization of the signal timing parameters is not significant enough, and there is no feedback mechanism between the coordination result and the flow. Unable to determine changes in arterial operations following coordination

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