Traffic signal optimization method

Abstract

The invention discloses a traffic signal optimization method, and relates to the field of traffic signal control. The method comprises the following steps of: A: establishing a root node T(n) of a decision tree; B: self-increasing n by 1; and establishing a node T(n) corresponding to each u(n) in Uad according to constraint conditions, and connecting T(n) serving as a sub-node of T(n-1) to the decision tree; C: judging whether n is equal to N, if the n is equal to N, executing a step D, otherwise, performing pruning operation on the decision tree, and then executing the step B; D: finding outa branch with the minimum total waiting time from the decision tree to obtain a corresponding control sequence; and E: outputting a control sequence for previous k green light phases according to thecontrol sequence, and executing the step A. Through the traffic signal optimization method, the total waiting time of all vehicles at road crossings can be obviously shortened, meanwhile, since the pruning operation is performed on the decision tree, the method has controllable amount of calculation and is convenient to popularize.

Description

technical field [0001] The invention relates to the technical field of traffic signal control, in particular to a traffic signal optimization method. Background technique [0002] Traffic signal optimization methods at road intersections can be divided into two categories: offline optimization using historical data and online optimization using real-time vehicle information. The former is mainly timing control, while the latter can be further divided into two categories: non-model optimization methods and model-based optimization methods. In general, model-based optimization methods can achieve better optimization results. However, its limitation lies in that, on the one hand, the complex and changeable real environment makes the pursuit of precise optimization algorithms not necessarily able to obtain the desired control effect; on the other hand, such algorithms often have high computational complexity, which limits the expansion of the scale of the problem. Therefore, f...

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Problems solved by technology

But its limitation is that, on the one hand, the complex and changeable real environment makes the pursuit of precise optimization algorithms not necessarily able to obtain the desired control effect; on the other hand, such algorithms often have high computational complexity, which limits the expansion of the scale of the problem.

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