Exploring for Route Preferences of Subway Passengers Using Smart Card and Train Log Data
As the mode share of the subway in Seoul has increased, the estimation of passenger travel routes has become a crucial issue to identify the congestion sections in the subway network. This paper aims to estimate the travel train of subway passengers in Seoul. The alternative routes are generated bas...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/6657486 |
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| _version_ | 1832559193357287424 |
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| author | Eun Hak Lee Kyoungtae Kim Seung-Young Kho Dong-Kyu Kim Shin-Hyung Cho |
| author_facet | Eun Hak Lee Kyoungtae Kim Seung-Young Kho Dong-Kyu Kim Shin-Hyung Cho |
| author_sort | Eun Hak Lee |
| collection | DOAJ |
| description | As the mode share of the subway in Seoul has increased, the estimation of passenger travel routes has become a crucial issue to identify the congestion sections in the subway network. This paper aims to estimate the travel train of subway passengers in Seoul. The alternative routes are generated based on the train log data. The travel route is then estimated by the empirical cumulative distribution functions (ECDFs) of access time, egress time, and transfer time. The train choice probability is estimated for alternative train combinations and the train combination with the highest probability is assigned to the subway passenger. The estimated result is validated using the transfer gate data which are recorded on private subway lines. The result showed that the accuracy of the estimated travel train is shown to be 95.6%. The choice ratios for no-transfer, one-transfer, two-transfer, three-transfer, and four-transfer trips are estimated to be 53.9%, 37.7%, 6.5%, 1.5%, and 0.4%, respectively. Regarding the practical application, the passenger kilometers by lines are estimated with the travel route estimation of the whole network. As results of the passenger kilometer calculation, the passenger kilometer of the proposed algorithm is estimated to be 88,314 million passenger kilometer. The proposed algorithm estimates the passenger kilometer about 13% higher than the shortest path algorithm. This result implies that the passengers do not always prefer the shortest path and detour about 13% for their convenience. |
| format | Article |
| id | doaj-art-1eaf1ba3a6ca405a9d0af9da94a2c5a3 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-1eaf1ba3a6ca405a9d0af9da94a2c5a32025-02-03T01:30:38ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/6657486Exploring for Route Preferences of Subway Passengers Using Smart Card and Train Log DataEun Hak Lee0Kyoungtae Kim1Seung-Young Kho2Dong-Kyu Kim3Shin-Hyung Cho4Institute of Construction and Environmental EngineeringFuture Transport Policy Research DivisionInstitute of Construction and Environmental EngineeringInstitute of Construction and Environmental EngineeringSchool of Civil and Environmental EngineeringAs the mode share of the subway in Seoul has increased, the estimation of passenger travel routes has become a crucial issue to identify the congestion sections in the subway network. This paper aims to estimate the travel train of subway passengers in Seoul. The alternative routes are generated based on the train log data. The travel route is then estimated by the empirical cumulative distribution functions (ECDFs) of access time, egress time, and transfer time. The train choice probability is estimated for alternative train combinations and the train combination with the highest probability is assigned to the subway passenger. The estimated result is validated using the transfer gate data which are recorded on private subway lines. The result showed that the accuracy of the estimated travel train is shown to be 95.6%. The choice ratios for no-transfer, one-transfer, two-transfer, three-transfer, and four-transfer trips are estimated to be 53.9%, 37.7%, 6.5%, 1.5%, and 0.4%, respectively. Regarding the practical application, the passenger kilometers by lines are estimated with the travel route estimation of the whole network. As results of the passenger kilometer calculation, the passenger kilometer of the proposed algorithm is estimated to be 88,314 million passenger kilometer. The proposed algorithm estimates the passenger kilometer about 13% higher than the shortest path algorithm. This result implies that the passengers do not always prefer the shortest path and detour about 13% for their convenience.http://dx.doi.org/10.1155/2022/6657486 |
| spellingShingle | Eun Hak Lee Kyoungtae Kim Seung-Young Kho Dong-Kyu Kim Shin-Hyung Cho Exploring for Route Preferences of Subway Passengers Using Smart Card and Train Log Data Journal of Advanced Transportation |
| title | Exploring for Route Preferences of Subway Passengers Using Smart Card and Train Log Data |
| title_full | Exploring for Route Preferences of Subway Passengers Using Smart Card and Train Log Data |
| title_fullStr | Exploring for Route Preferences of Subway Passengers Using Smart Card and Train Log Data |
| title_full_unstemmed | Exploring for Route Preferences of Subway Passengers Using Smart Card and Train Log Data |
| title_short | Exploring for Route Preferences of Subway Passengers Using Smart Card and Train Log Data |
| title_sort | exploring for route preferences of subway passengers using smart card and train log data |
| url | http://dx.doi.org/10.1155/2022/6657486 |
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