Traffic simulation correction method based on genetic algorithm and generalized recurrent nerve network

Abstract

The invention discloses a traffic simulation correction method based on a genetic algorithm and a generalized recurrent nerve network. The method includes the following steps that firstly, average travel time of vehicles is selected and serves as an evaluation index, and a target of parameter correction is determined; secondly, need traffic data are collected so that a traffic simulation model can be established, needed parameters to be corrected and a corresponding value range are determined; thirdly, optimizing computing is performed on values of the determined parameters to the corrected with the genetic algorithm, and a combination of the corrected parameters after the iteration of the genetic algorithm is predicated with the generalized recurrent nerve network, when the combination of the corrected parameters after the iteration of the genetic algorithm is matched with the target of parameter correction, the corresponding combination of the corrected parameters is output, and if the combination of the corrected parameters after the iteration of the genetic algorithm is not matched with the target of parameter correction, iteration is conducted continuously with the genetic algorithm until the combination of the corrected parameters after the interaction is matched with the target of parameter correction after the test of the generalized recurrent nerve network. According to the traffic simulation correction method based on the genetic algorithm and the generalized recurrent nerve network, the high efficiency of parameter calibration correction is achieved, the accuracy of parameter correction is ensured, the application range is wide, and the method is safe and reliable.

Description

technical field [0001] The invention relates to a parameter correction method, in particular to a traffic simulation correction method based on a genetic algorithm and a generalized regression neural network, and belongs to the technical field of traffic simulation parameter correction. Background technique [0002] Microscopic traffic simulation software can not only be used to visually simulate and reproduce road traffic conditions, but also accurately analyze and evaluate traffic conditions, so it has been more and more widely used. A large number of independent parameters are used in the microscopic simulation model to represent the traffic flow and driver's driving behavior. The values ​​of these parameters play a decisive role in the accuracy and reliability of the simulation results. The default value of the model itself depends largely on the traffic flow conditions and the psychological characteristics of the driver in the country where the model was developed. Ther...

AI Technical Summary

Problems solved by technology

Take genetic algorithm as an example. Genetic algorithm is a global search algorithm that simulates the process of survival of the fittest in nature. It can realize the overall optimization process only by relying on the information of fitness function. It is currently the most widely used correction algorithm for simulation parameter correction. However, the application of genetic algorithm requires It takes about 20-30 rounds of iterations to get the final convergence results, and each round of iterations depends on running simulation software to get the corresponding output results, which will consume a lot of time

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