Mass Rapid Transit System Passenger Traffic Forecast Using a Re-Sample Recurrent Neural Network
In this study, we developed a model re-sample Recurrent Neural Network (RRNN) to forecast passenger traffic on Mass Rapid Transit Systems (MRT). The Recurrent Neural Network was applied to build a model to perform passenger traffic prediction, where the forecast task was transformed into a classific...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2019/8943291 |
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| _version_ | 1832567366531153920 |
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| author | Rong Hu Yi-Chang Chiu Chih-Wei Hsieh Tang-Hsien Chang Xingsi Xue Fumin Zou Lyuchao Liao |
| author_facet | Rong Hu Yi-Chang Chiu Chih-Wei Hsieh Tang-Hsien Chang Xingsi Xue Fumin Zou Lyuchao Liao |
| author_sort | Rong Hu |
| collection | DOAJ |
| description | In this study, we developed a model re-sample Recurrent Neural Network (RRNN) to forecast passenger traffic on Mass Rapid Transit Systems (MRT). The Recurrent Neural Network was applied to build a model to perform passenger traffic prediction, where the forecast task was transformed into a classification task. However, in this process, the training dataset usually ended up being imbalanced. To address this dataset imbalance, our research proposes re-sample Recurrent Neural Network. A case study of the California Mass Rapid Transit System revealed that the model introduced in this work could timely and effectively predict passenger traffic of MRT. The measurements of passenger traffic themselves were also studied and showed that the new method provided a good understanding of the level of passenger traffic and was able to achieve prediction accuracy upwards of 90% higher than standard tests. The development of this model adds value to the methodology of traffic applications by employing these Recurrent Neural Networks. |
| format | Article |
| id | doaj-art-44080ea475f14da98298380b6198c05b |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-44080ea475f14da98298380b6198c05b2025-02-03T01:01:37ZengWileyJournal of Advanced Transportation0197-67292042-31952019-01-01201910.1155/2019/89432918943291Mass Rapid Transit System Passenger Traffic Forecast Using a Re-Sample Recurrent Neural NetworkRong Hu0Yi-Chang Chiu1Chih-Wei Hsieh2Tang-Hsien Chang3Xingsi Xue4Fumin Zou5Lyuchao Liao6Fujian Province Key Laboratory of Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou 350118, ChinaDept. of Civil and Architectural Engineering and Mechanics, The University of Arizona, Tucson, AZ 85721, USADept. of Civil and Architectural Engineering and Mechanics, The University of Arizona, Tucson, AZ 85721, USASchool of Transportation, Fujian University of Technology, Fuzhou 350108, ChinaComputer Science and Technology, Fujian University of Technology, Fuzhou 350108, ChinaFujian Province Key Laboratory of Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou 350118, ChinaFujian Province Key Laboratory of Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou 350118, ChinaIn this study, we developed a model re-sample Recurrent Neural Network (RRNN) to forecast passenger traffic on Mass Rapid Transit Systems (MRT). The Recurrent Neural Network was applied to build a model to perform passenger traffic prediction, where the forecast task was transformed into a classification task. However, in this process, the training dataset usually ended up being imbalanced. To address this dataset imbalance, our research proposes re-sample Recurrent Neural Network. A case study of the California Mass Rapid Transit System revealed that the model introduced in this work could timely and effectively predict passenger traffic of MRT. The measurements of passenger traffic themselves were also studied and showed that the new method provided a good understanding of the level of passenger traffic and was able to achieve prediction accuracy upwards of 90% higher than standard tests. The development of this model adds value to the methodology of traffic applications by employing these Recurrent Neural Networks.http://dx.doi.org/10.1155/2019/8943291 |
| spellingShingle | Rong Hu Yi-Chang Chiu Chih-Wei Hsieh Tang-Hsien Chang Xingsi Xue Fumin Zou Lyuchao Liao Mass Rapid Transit System Passenger Traffic Forecast Using a Re-Sample Recurrent Neural Network Journal of Advanced Transportation |
| title | Mass Rapid Transit System Passenger Traffic Forecast Using a Re-Sample Recurrent Neural Network |
| title_full | Mass Rapid Transit System Passenger Traffic Forecast Using a Re-Sample Recurrent Neural Network |
| title_fullStr | Mass Rapid Transit System Passenger Traffic Forecast Using a Re-Sample Recurrent Neural Network |
| title_full_unstemmed | Mass Rapid Transit System Passenger Traffic Forecast Using a Re-Sample Recurrent Neural Network |
| title_short | Mass Rapid Transit System Passenger Traffic Forecast Using a Re-Sample Recurrent Neural Network |
| title_sort | mass rapid transit system passenger traffic forecast using a re sample recurrent neural network |
| url | http://dx.doi.org/10.1155/2019/8943291 |
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