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GW and SVR-based bus station moving flow prediction method and system, and storage medium

A traffic forecasting and bus station technology, applied in forecasting, data processing applications, instruments, etc., can solve problems such as unrealizable, tedious manual parameter selection process, etc., to achieve the effect of strong search ability and eliminating complex manual parameter selection process.

Pending Publication Date: 2019-10-25
ANHUI UNIV OF SCI & TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The main purpose of the present invention is to propose a GW and SVR-based mobile flow forecasting method, system and storage medium at bus stations, aiming to solve the cumbersome manual parameter selection process in the prior art and the inability to realize simple and accurate forecasting of long-distance buses. Daily mobile traffic issues

Method used

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  • GW and SVR-based bus station moving flow prediction method and system, and storage medium
  • GW and SVR-based bus station moving flow prediction method and system, and storage medium
  • GW and SVR-based bus station moving flow prediction method and system, and storage medium

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Embodiment 1

[0066] In order to achieve the above purpose, this embodiment proposes a mobile traffic forecasting method for bus stations based on GW and SVR, using SVR for mobile traffic forecasting at long-distance bus stations, and using the gray wolf optimization algorithm to optimize the optimal parameters of the SVR, thereby saving Eliminate the cumbersome manual parameter selection process of SVR, and realize accurate prediction of mobile traffic at long-distance bus stations.

[0067] It should be noted that the present invention uses the SVR algorithm for mobile network traffic prediction at long-distance bus stations to realize accurate prediction of mobile traffic at bus stations and provide guarantees for network security and user experience at bus stations with heavy traffic during holidays.

[0068] In the specific implementation, the present invention uses the advanced meta-heuristic optimization algorithm to optimize the optimal parameters of SVR. The GW optimization algorith...

Embodiment 2

[0120] In addition, in order to achieve the above object, the present embodiment provides a storage medium, on which a GW and SVR-based bus station mobile flow prediction program is stored, and the GW and SVR-based bus station mobile flow prediction program is processed by the processor During execution, the calculation process of the above-mentioned GW and SVR-based mobile flow forecasting method for bus stations is realized.

Embodiment 3

[0122] In addition, to achieve the above purpose, see Image 6 : This embodiment also proposes a GW and SVR-based mobile traffic forecasting system at bus stations, which includes:

[0123] Traffic Acquisition and Processing Module: Obtain the daily mobile traffic data of the coach station at the granularity of 24 hours, map the acquired serialized traffic data of the bus station to a specific interval, and convert it into the input data of the equal-length SVR and the corresponding output;

[0124] Preliminary SVR traffic forecasting module: build a traffic forecasting model of the bus station mobile network based on the SVR algorithm, and calculate the model forecasting error;

[0125] GW optimization traffic forecasting module: take the preprocessed historical traffic data of the bus station as the input and output of the constructed SVR prediction model, and then construct the GW optimization model to search for the optimal parameters c and g for SVR to accurately predict...

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Abstract

The invention discloses a bus station moving flow prediction method and system based on GW and SVR and a storage medium. The SVR is used for predicting the movement flow of the long-distance bus station, and the optimal parameters of the SVR are optimized through the grey wolf optimization algorithm, so that the tedious manual parameter selection process of the SVR is omitted, and the movement flow of the long-distance bus station is accurately predicted. The invention has the advantages that (1) the SVR algorithm is used for predicting the mobile network flow of the long-distance bus station,so that the mobile flow of the bus station is accurately predicted, and the network security and the experience of the bus station with large pedestrian flow in holidays and festivals are guaranteed;and (2) the advanced meta-heuristic optimization algorithm is used for optimizing the optimal parameters of the SVR, the GW optimization algorithm selected by the invention not only inherits the advantages of the meta-heuristic optimization algorithm, but also has the advantages of strong search capability and difficulty in falling into local optimum, and the complex manual parameter selection process of the SVR algorithm is omitted.

Description

technical field [0001] The present invention relates to the field of large-scene mobile Internet traffic prediction, in particular to a GW and SVR-based mobile traffic prediction method, system and storage medium for bus stations. Background technique [0002] With the development of mobile Internet technology and the comprehensive popularization of smart phones, mobile Internet data traffic has shown explosive growth. Telecom network operators have changed from the traditional "call fee operation" model to the "data flow operation" model, and the "unlimited" packages launched have further promoted the growth of traffic. The rapid increase in mobile traffic and user scale has brought great challenges to the pressure on the current network, and also seriously affected user experience. Accurately predicting the total traffic of the mobile network and doing security work in advance can effectively ensure the safe and stable operation of the network. Due to the significant pos...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/30G06N3/00
CPCG06Q10/04G06N3/006G06Q50/40
Inventor 郑晓亮来文豪薛生李重情李尧斌江丙友郑春山陈华亮
Owner ANHUI UNIV OF SCI & TECH
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