An iterative method for traffic signal optimization based on message matrix gridding

Abstract

The invention relates to a traffic signal optimization iteration method based on message matrix gridding. The method comprises the following steps of initializing a traffic network model related parameter matrix; initializing a traffic flow state matrix according to road traffic information obtained by a microwave vehicle detector and establishing a network node message function basis matrix; calculating a message function matrix of each iteration of this node; obtaining the message function matrix of the downstream node; calculating the optimal signal control quantity in the iteration; calculating the global optimization result; judging whether a system is stable; if the system is stable, stopping cyclic iteration; and otherwise, continuing the next cyclic iteration. The invention provides the signal optimization control method obtaining traffic flow information in real time in a traffic network and using the message function matrixes to realize stable optimization, rapid convergence and high real-time performance in a signal optimization iteration algorithm.

Description

technical field [0001] The invention relates to the field of intelligent traffic systems, in particular to an iterative method for traffic signal optimization based on message matrix gridding. Background technique [0002] In the urban traffic network system, the traffic capacity of the intersection directly affects the traffic capacity of the entire traffic network system. The quality of signal control at road intersections is of great significance to reduce vehicle delays and reduce the average road area occupied by road vehicles. [0003] Existing control systems, such as TRANSYT, SCOOT or SCATS, only optimize the green signal ratio, phase difference, phase and phase sequence independently, without considering the correlation between intersections, and it is difficult to ensure optimal network optimization parameter. Moreover, the transportation system is a nonlinear system with large randomness, and the system model is very complex. Scholars use different control metho...

AI Technical Summary

Problems solved by technology

However, because some algorithms for simplifying the model cannot guarantee the optimal control parameters in large random situations, and complex control methods require a large amount of calculation, the real-time performance of the method is relatively low under the limitation of the existing computer computing capacity. Poor, it is difficult to apply to the actual control system

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