Traffic simulation software parameter calibration method and system based on learning algorithm and medium

A technology of traffic simulation and software parameters, applied in the field of traffic simulation, can solve the problems of low efficiency, inability to actually evaluate engineering applications in traffic, and time-consuming

Pending Publication Date: 2019-12-27
SHANGHAI JIAO TONG UNIV
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Problems solved by technology

[0005] The existing micro-traffic simulation software parameter calibration method is not efficient and the algorithm rules are complicated, so it cannot be well applied in actual traffic evaluation projects
In addition, the existing parameter calibration methods are often used in co

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  • Traffic simulation software parameter calibration method and system based on learning algorithm and medium
  • Traffic simulation software parameter calibration method and system based on learning algorithm and medium
  • Traffic simulation software parameter calibration method and system based on learning algorithm and medium

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Embodiment Construction

[0126] The present invention will be described in detail below in conjunction with specific embodiments. The following examples will help those skilled in the art to further understand the present invention, but do not limit the present invention in any form. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0127] According to a kind of traffic simulation software parameter calibration method based on learning algorithm provided by the present invention, comprising:

[0128] Step A: Preliminary screening of the parameters, and sensitivity analysis of the screened parameters, obtaining the first parameter to be calibrated that meets the preset requirements, and collecting training data sets;

[0129] Step B: Perform neural network model training according to the obtained first parameters to be calibrated an...

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Abstract

The invention provides a traffic simulation software parameter calibration method and system based on a learning algorithm, and a medium, and the method comprises the steps: A, carrying out the preliminary screening of parameters, carrying out the sensitivity analysis of the screened parameters, obtaining a first to-be-calibrated parameter meeting a preset requirement, and collecting a training data set; b, according to the obtained first to-be-calibrated parameters and the training data set, carrying out neural network model training, and obtaining a trained neural network model; and C, performing particle swarm optimization according to the obtained trained neural network model. The method provided by the invention can efficiently find the optimal parameter suitable for the current roadnetwork, and is suitable for practical application.

Description

technical field [0001] The invention relates to the field of traffic simulation, in particular to a method, system and medium for calibrating traffic simulation software parameters based on a learning algorithm. In particular, it relates to a method for calibrating parameters of micro-traffic simulation software based on machine learning algorithms. Background technique [0002] Micro-traffic simulation technology is the main technology for traffic engineers to evaluate traffic impacts. The effectiveness of the traffic planning scheme is verified by analyzing the output results of the simulation technology. To ensure the accuracy of the results, the micro-simulation software needs to conform to the actual traffic conditions as much as possible. However, factors such as driving behavior and road alignment in different locations are different. A software parameter calibration method is needed to quickly and accurately adjust relevant software parameters to provide support for ...

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Application Information

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IPC IPC(8): G06N3/08G06Q10/06G06Q10/04G06Q50/26
CPCG06N3/086G06Q10/04G06Q10/0639G06Q10/067G06Q50/26
Inventor 倪安宁刘晏尘李桃俞岑歆张小宁
Owner SHANGHAI JIAO TONG UNIV
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