Classification of mountain-based rail transit stations and analysis of passenger flow influencing mechanisms.
Mountainous urban rail transit stations exhibit distinct characteristics. To investigate how these features affect passenger flow variations at rail stations, we analyze geographic-environmental data surrounding the stations and integrate road network topology, automatic fare collection data, and po...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0323937 |
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| author | Qingru Zou Yue Xia Xinchen Ran Xueli Guo Jiaxiao Feng |
| author_facet | Qingru Zou Yue Xia Xinchen Ran Xueli Guo Jiaxiao Feng |
| author_sort | Qingru Zou |
| collection | DOAJ |
| description | Mountainous urban rail transit stations exhibit distinct characteristics. To investigate how these features affect passenger flow variations at rail stations, we analyze geographic-environmental data surrounding the stations and integrate road network topology, automatic fare collection data, and point-of-interest (POI) data. We propose a method to classify rail transit stations by considering the mountainous features and establish a multiscale geographically weighted regression (MGWR) model to assess the classification results. This study focuses on 189 rail stations in Chongqing, identifying six station categories: comprehensive mountainous, comprehensive non-mountainous, employment mountainous, employment non-mountainous, residential mountainous, and residential non-mountainous. The MGWR results show that road growth coefficients, average longitudinal slopes, and road lengths significantly influence station performance. For instance, the average longitudinal slope substantially affects employment in mountainous stations, particularly during the morning peak. The analysis reveals that the average longitudinal slope exerts a stronger negative effect on morning peak inbound passenger flow at employment mountainous stations (-0.949), indicating that commuters are more sensitive to travel time during the morning peak. In contrast, the evening peak inbound passenger flow is less impacted (-0.409), suggesting that evening commuters face fewer time constraints. These findings offer strategic insights for zoning transit stations to support transit-oriented development(TOD). |
| format | Article |
| id | doaj-art-cb8b0c969fd349f7a1f6681a8d7ec8e2 |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-cb8b0c969fd349f7a1f6681a8d7ec8e22025-08-20T02:23:19ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01205e032393710.1371/journal.pone.0323937Classification of mountain-based rail transit stations and analysis of passenger flow influencing mechanisms.Qingru ZouYue XiaXinchen RanXueli GuoJiaxiao FengMountainous urban rail transit stations exhibit distinct characteristics. To investigate how these features affect passenger flow variations at rail stations, we analyze geographic-environmental data surrounding the stations and integrate road network topology, automatic fare collection data, and point-of-interest (POI) data. We propose a method to classify rail transit stations by considering the mountainous features and establish a multiscale geographically weighted regression (MGWR) model to assess the classification results. This study focuses on 189 rail stations in Chongqing, identifying six station categories: comprehensive mountainous, comprehensive non-mountainous, employment mountainous, employment non-mountainous, residential mountainous, and residential non-mountainous. The MGWR results show that road growth coefficients, average longitudinal slopes, and road lengths significantly influence station performance. For instance, the average longitudinal slope substantially affects employment in mountainous stations, particularly during the morning peak. The analysis reveals that the average longitudinal slope exerts a stronger negative effect on morning peak inbound passenger flow at employment mountainous stations (-0.949), indicating that commuters are more sensitive to travel time during the morning peak. In contrast, the evening peak inbound passenger flow is less impacted (-0.409), suggesting that evening commuters face fewer time constraints. These findings offer strategic insights for zoning transit stations to support transit-oriented development(TOD).https://doi.org/10.1371/journal.pone.0323937 |
| spellingShingle | Qingru Zou Yue Xia Xinchen Ran Xueli Guo Jiaxiao Feng Classification of mountain-based rail transit stations and analysis of passenger flow influencing mechanisms. PLoS ONE |
| title | Classification of mountain-based rail transit stations and analysis of passenger flow influencing mechanisms. |
| title_full | Classification of mountain-based rail transit stations and analysis of passenger flow influencing mechanisms. |
| title_fullStr | Classification of mountain-based rail transit stations and analysis of passenger flow influencing mechanisms. |
| title_full_unstemmed | Classification of mountain-based rail transit stations and analysis of passenger flow influencing mechanisms. |
| title_short | Classification of mountain-based rail transit stations and analysis of passenger flow influencing mechanisms. |
| title_sort | classification of mountain based rail transit stations and analysis of passenger flow influencing mechanisms |
| url | https://doi.org/10.1371/journal.pone.0323937 |
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