Passenger Flow Path Prediction Based on Urban Rail Transit AFC Data: An Example of Chengdu, China
The development of the automatic fare collection (AFC) systems provides significant support for predicting passenger flow on urban rail transit. This paper extracts passenger travel patterns using AFC data on urban rail transit in Chengdu, China, over a one-month period. Passengers are divided into...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2023/5596285 |
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| _version_ | 1832547898213007360 |
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| author | Yu Wang Qixuan Qin Jialiang Chen Jiangbo Wang Kai Liu |
| author_facet | Yu Wang Qixuan Qin Jialiang Chen Jiangbo Wang Kai Liu |
| author_sort | Yu Wang |
| collection | DOAJ |
| description | The development of the automatic fare collection (AFC) systems provides significant support for predicting passenger flow on urban rail transit. This paper extracts passenger travel patterns using AFC data on urban rail transit in Chengdu, China, over a one-month period. Passengers are divided into two categories based on their travel habits and data mining models, and multinomial logit (MNL) models are separately used to predict their destinations. Furthermore, a two-way search algorithm is developed to search the optimal paths between origin-destination (OD) pairs by considering interchange constraints. Start a path search through the origin point and destination point, respectively, until the shortest path is found. The maximum effectiveness of a path is measured by travel time, interchange time, and the number of interchanges between the OD pairs. Finally, the validity of the proposed passenger flow path prediction method is verified by using the AFC data of Chengdu metropolitan rail transit from April 2018. |
| format | Article |
| id | doaj-art-1651c48d94e24252a2108decf51bbdd2 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-1651c48d94e24252a2108decf51bbdd22025-02-03T06:42:53ZengWileyJournal of Advanced Transportation2042-31952023-01-01202310.1155/2023/5596285Passenger Flow Path Prediction Based on Urban Rail Transit AFC Data: An Example of Chengdu, ChinaYu Wang0Qixuan Qin1Jialiang Chen2Jiangbo Wang3Kai Liu4School of Traffic and Transportation EngineeringSchool of Traffic and Transportation EngineeringSchool of Transportation and LogisticsSchool of Transportation and LogisticsSchool of Transportation and LogisticsThe development of the automatic fare collection (AFC) systems provides significant support for predicting passenger flow on urban rail transit. This paper extracts passenger travel patterns using AFC data on urban rail transit in Chengdu, China, over a one-month period. Passengers are divided into two categories based on their travel habits and data mining models, and multinomial logit (MNL) models are separately used to predict their destinations. Furthermore, a two-way search algorithm is developed to search the optimal paths between origin-destination (OD) pairs by considering interchange constraints. Start a path search through the origin point and destination point, respectively, until the shortest path is found. The maximum effectiveness of a path is measured by travel time, interchange time, and the number of interchanges between the OD pairs. Finally, the validity of the proposed passenger flow path prediction method is verified by using the AFC data of Chengdu metropolitan rail transit from April 2018.http://dx.doi.org/10.1155/2023/5596285 |
| spellingShingle | Yu Wang Qixuan Qin Jialiang Chen Jiangbo Wang Kai Liu Passenger Flow Path Prediction Based on Urban Rail Transit AFC Data: An Example of Chengdu, China Journal of Advanced Transportation |
| title | Passenger Flow Path Prediction Based on Urban Rail Transit AFC Data: An Example of Chengdu, China |
| title_full | Passenger Flow Path Prediction Based on Urban Rail Transit AFC Data: An Example of Chengdu, China |
| title_fullStr | Passenger Flow Path Prediction Based on Urban Rail Transit AFC Data: An Example of Chengdu, China |
| title_full_unstemmed | Passenger Flow Path Prediction Based on Urban Rail Transit AFC Data: An Example of Chengdu, China |
| title_short | Passenger Flow Path Prediction Based on Urban Rail Transit AFC Data: An Example of Chengdu, China |
| title_sort | passenger flow path prediction based on urban rail transit afc data an example of chengdu china |
| url | http://dx.doi.org/10.1155/2023/5596285 |
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