Prediction of Daily Entrance and Exit Passenger Flow of Rail Transit Stations by Deep Learning Method
The prediction of entrance and exit passenger flow of rail transit stations is one of key research focuses in the area of intelligent transportation. Based on the big data of rail transit IC card (Public Transportation Card), this paper analyzes the data of major dynamic factors having effect on ent...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2018/6142724 |
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| _version_ | 1849396297066348544 |
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| author | Huaizhong Zhu Xiaoguang Yang Yizhe Wang |
| author_facet | Huaizhong Zhu Xiaoguang Yang Yizhe Wang |
| author_sort | Huaizhong Zhu |
| collection | DOAJ |
| description | The prediction of entrance and exit passenger flow of rail transit stations is one of key research focuses in the area of intelligent transportation. Based on the big data of rail transit IC card (Public Transportation Card), this paper analyzes the data of major dynamic factors having effect on entrance passenger flow and exit passenger flow of rail transit stations: weather data, atmospheric temperature data, holiday and festival data, ground index data, and elevated road data and calculates the daily entrance passenger flow and daily exit passenger flow of individual rail transit stations with data reduction. Furthermore, based on the history data of passenger flow of rail transit stations and relevant influence factors, it applies the deep learning method to choose the relatively optimal hidden layer node by means of the cut-and-try method, set up input data and labeled data, select the activation function and loss function, and use the Adam Gradient Descent Optimization Algorithm for iterative global convergence. The results verify that this method accurately predicts the daily entrance passenger flow and daily exit passenger flow of rail transit stations with the prediction error of less than 4.1%. Finally, the proposed model is compared with the linear regression model. |
| format | Article |
| id | doaj-art-b4ee054caed84d5a8defd91e1b59af21 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-b4ee054caed84d5a8defd91e1b59af212025-08-20T03:39:22ZengWileyJournal of Advanced Transportation0197-67292042-31952018-01-01201810.1155/2018/61427246142724Prediction of Daily Entrance and Exit Passenger Flow of Rail Transit Stations by Deep Learning MethodHuaizhong Zhu0Xiaoguang Yang1Yizhe Wang2The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai, ChinaThe Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai, ChinaThe Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai, ChinaThe prediction of entrance and exit passenger flow of rail transit stations is one of key research focuses in the area of intelligent transportation. Based on the big data of rail transit IC card (Public Transportation Card), this paper analyzes the data of major dynamic factors having effect on entrance passenger flow and exit passenger flow of rail transit stations: weather data, atmospheric temperature data, holiday and festival data, ground index data, and elevated road data and calculates the daily entrance passenger flow and daily exit passenger flow of individual rail transit stations with data reduction. Furthermore, based on the history data of passenger flow of rail transit stations and relevant influence factors, it applies the deep learning method to choose the relatively optimal hidden layer node by means of the cut-and-try method, set up input data and labeled data, select the activation function and loss function, and use the Adam Gradient Descent Optimization Algorithm for iterative global convergence. The results verify that this method accurately predicts the daily entrance passenger flow and daily exit passenger flow of rail transit stations with the prediction error of less than 4.1%. Finally, the proposed model is compared with the linear regression model.http://dx.doi.org/10.1155/2018/6142724 |
| spellingShingle | Huaizhong Zhu Xiaoguang Yang Yizhe Wang Prediction of Daily Entrance and Exit Passenger Flow of Rail Transit Stations by Deep Learning Method Journal of Advanced Transportation |
| title | Prediction of Daily Entrance and Exit Passenger Flow of Rail Transit Stations by Deep Learning Method |
| title_full | Prediction of Daily Entrance and Exit Passenger Flow of Rail Transit Stations by Deep Learning Method |
| title_fullStr | Prediction of Daily Entrance and Exit Passenger Flow of Rail Transit Stations by Deep Learning Method |
| title_full_unstemmed | Prediction of Daily Entrance and Exit Passenger Flow of Rail Transit Stations by Deep Learning Method |
| title_short | Prediction of Daily Entrance and Exit Passenger Flow of Rail Transit Stations by Deep Learning Method |
| title_sort | prediction of daily entrance and exit passenger flow of rail transit stations by deep learning method |
| url | http://dx.doi.org/10.1155/2018/6142724 |
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