Passenger Flow Scale Prediction of Urban Rail Transit Stations Based on Multilayer Perceptron (MLP)
Accurately predicting passenger flow at rail stations is an effective way to reduce operation and maintenance costs, improve the quality of passenger travel while meeting future passenger travel demand. The improvement of data acquisition capability allows fine-grained and large-scale built environm...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2023/1430449 |
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| _version_ | 1832551164311240704 |
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| author | Luzhou Lin Yuezhe Gao Bingxin Cao Zifan Wang Cai Jia |
| author_facet | Luzhou Lin Yuezhe Gao Bingxin Cao Zifan Wang Cai Jia |
| author_sort | Luzhou Lin |
| collection | DOAJ |
| description | Accurately predicting passenger flow at rail stations is an effective way to reduce operation and maintenance costs, improve the quality of passenger travel while meeting future passenger travel demand. The improvement of data acquisition capability allows fine-grained and large-scale built environment data to be extracted. Therefore, this paper focuses on investigating the relationship between the built environment around the station and the station passenger flow and discusses whether the built environment data can be applied to the station passenger flow prediction. Firstly, the evaluation system of station passenger flow influencing factors is built based on multisource data. The inner relationship between built environment factors and station passenger flow is investigated using the Pearson correlation analysis. Based on this, a multilayer perceptron (MLP)-based passenger flow prediction model was developed to predict the passenger flow at key stations. The study results show that the built environment factors impact station passenger flow, and the MLP prediction model has better prediction accuracy and applicability. The results of the study can be applied to predict the passenger flow scale of rail stations without historical passenger flow data and thus are also applicable to new rail stations. |
| format | Article |
| id | doaj-art-0f9700da87534fad949045af6ea3bfa5 |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-0f9700da87534fad949045af6ea3bfa52025-02-03T06:04:40ZengWileyComplexity1099-05262023-01-01202310.1155/2023/1430449Passenger Flow Scale Prediction of Urban Rail Transit Stations Based on Multilayer Perceptron (MLP)Luzhou Lin0Yuezhe Gao1Bingxin Cao2Zifan Wang3Cai Jia4School of EconomicsBeijing Key Laboratory of Traffic Engineering, Beijing University of TechnologyBeijing Key Laboratory of Traffic Engineering, Beijing University of TechnologyBeijing Key Laboratory of Traffic Engineering, Beijing University of TechnologySchool of Geography and TourismAccurately predicting passenger flow at rail stations is an effective way to reduce operation and maintenance costs, improve the quality of passenger travel while meeting future passenger travel demand. The improvement of data acquisition capability allows fine-grained and large-scale built environment data to be extracted. Therefore, this paper focuses on investigating the relationship between the built environment around the station and the station passenger flow and discusses whether the built environment data can be applied to the station passenger flow prediction. Firstly, the evaluation system of station passenger flow influencing factors is built based on multisource data. The inner relationship between built environment factors and station passenger flow is investigated using the Pearson correlation analysis. Based on this, a multilayer perceptron (MLP)-based passenger flow prediction model was developed to predict the passenger flow at key stations. The study results show that the built environment factors impact station passenger flow, and the MLP prediction model has better prediction accuracy and applicability. The results of the study can be applied to predict the passenger flow scale of rail stations without historical passenger flow data and thus are also applicable to new rail stations.http://dx.doi.org/10.1155/2023/1430449 |
| spellingShingle | Luzhou Lin Yuezhe Gao Bingxin Cao Zifan Wang Cai Jia Passenger Flow Scale Prediction of Urban Rail Transit Stations Based on Multilayer Perceptron (MLP) Complexity |
| title | Passenger Flow Scale Prediction of Urban Rail Transit Stations Based on Multilayer Perceptron (MLP) |
| title_full | Passenger Flow Scale Prediction of Urban Rail Transit Stations Based on Multilayer Perceptron (MLP) |
| title_fullStr | Passenger Flow Scale Prediction of Urban Rail Transit Stations Based on Multilayer Perceptron (MLP) |
| title_full_unstemmed | Passenger Flow Scale Prediction of Urban Rail Transit Stations Based on Multilayer Perceptron (MLP) |
| title_short | Passenger Flow Scale Prediction of Urban Rail Transit Stations Based on Multilayer Perceptron (MLP) |
| title_sort | passenger flow scale prediction of urban rail transit stations based on multilayer perceptron mlp |
| url | http://dx.doi.org/10.1155/2023/1430449 |
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