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Construction, forecasting method and device for traffic volume forecasting model in expressway service area

A technology of expressways and forecasting models, applied in forecasting, computing models, biological models, etc., can solve the problems of low prediction accuracy of models, achieve significant engineering practical application value, expand research dimensions, and improve prediction accuracy

Active Publication Date: 2022-07-26
CHANGAN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] The purpose of the present invention is to provide a traffic volume forecasting model construction, forecasting method and device in expressway service areas, to solve the problem of low model forecasting accuracy existing in the forecasting methods and devices in the prior art

Method used

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  • Construction, forecasting method and device for traffic volume forecasting model in expressway service area
  • Construction, forecasting method and device for traffic volume forecasting model in expressway service area
  • Construction, forecasting method and device for traffic volume forecasting model in expressway service area

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Experimental program
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Effect test

Embodiment 1

[0090] In this embodiment, a method for constructing a traffic volume prediction model in an expressway service area is provided, and the method is performed according to the following steps:

[0091] Step 1, obtain multiple groups of traffic volume data in the service area, the traffic volume data in the service area include the hourly traffic volume of passenger cars in the service area, the hourly traffic volume of large passenger cars in the service area, and the hourly traffic volume of trucks in the service area;

[0092] Obtain the expressway section traffic volume data corresponding to each set of service area traffic volume data, and obtain multiple sets of expressway section traffic volume data. The hourly traffic flow of coaches in the section and the hourly traffic flow of trucks in the section of the expressway;

[0093] In this embodiment, the original traffic volume data of the service area and the corresponding expressway section are obtained from the service a...

example

[0122] The calculation example is as follows:

[0123] When the flow of small passenger cars is 17, the flow of large passenger cars is 9, and the flow of trucks is 9 in a certain hour, the vehicle equivalent and the person equivalent are calculated as:

[0124] Vehicle equivalent=17×1.0+9×1.5+9×3.0=57.5

[0125] Human equivalent = 17×3.3+9×46.0+9×2.1=489

[0126] Step 3. Use the service area vehicle equivalent and service area person equivalent corresponding to the rth group of service area traffic data and the rth group of service area traffic data as the rth label data, r=1,2,...,R, R is a positive integer;

[0127] Repeat this step until R label data are obtained, and a label set is obtained;

[0128] Step 4. Take the expressway section vehicle equivalent and expressway section person equivalent corresponding to the jth group of expressway section traffic data and the ith group of expressway section traffic data as the jth sample data, j=1, 2, ... ..., J, J are positiv...

Embodiment 2

[0173] A method for predicting traffic volume in an expressway service area, the method is performed according to the following steps:

[0174] Step A: Collect the traffic volume data of the expressway section corresponding to the service area to be predicted, and obtain the data to be predicted;

[0175] Step B: Input the to-be-predicted data into the prediction model constructed by the method for constructing the traffic volume prediction model in the expressway service area in the first embodiment, and obtain the traffic volume in the expressway service area to be predicted.

[0176] In this embodiment, as Image 6 shown, Image 6 It is the comparison of the accuracy of the forecast results of traffic volume in the service area by different models. It can be seen that Image 6 Consistent with the results in Table 2, the improved particle swarm algorithm combined with XGBoost's traffic volume prediction method in the expressway service area proposed by the present inventio...

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Abstract

The invention discloses an improved particle swarm algorithm provided by an improved particle swarm algorithm to optimize the XBOOST model, and the particle swarm is divided into a master-slave topology, which better avoids the optimization process of model parameters. This improved particle swarm algorithm can better optimize the ideal hyperparameters and improve the accuracy of traffic volume prediction.

Description

technical field [0001] The invention relates to a traffic volume prediction method and device in an expressway service area, in particular to a traffic volume prediction model construction and prediction method and device in an expressway service area. Background technique [0002] The expressway service area is an important part of the construction of the intelligent expressway. The intelligent process of the service area directly affects the intelligent construction level of the expressway. When evaluating the service level of the service area, it mainly analyzes and excavates the relationship between the supply capacity of the service area and the traffic volume of the expressway. Therefore, the effective quantification and prediction of the traffic volume of the expressway service area is an important technical means to scientifically and rationally evaluate the service capacity of the expressway service area. [0003] The traditional traffic volume forecast mainly reli...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04G06Q50/26G06K9/62G06N3/00
CPCG06Q10/04G06Q50/26G06N3/006G06F18/23213G06F18/241G06F18/214
Inventor 孙朝云杨荣新郝雪丽裴莉莉李伟赵怀鑫韩雨希袁博
Owner CHANGAN UNIV
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