A multi‐class transit assignment model for estimating transit passenger flows—a case study of Beijing subway network
This paper describes a case study comparing a multi‐class transit assignment model with its single class counterpart for estimating the passenger flows of the Beijing subway network—one of the largest railway transit networks in the world. Multi‐class traffic assignment has been widely considered as...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Si, Bingfeng [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Rechteinformationen: |
Nutzungsrecht: Copyright © 2015 John Wiley & Sons, Ltd. |
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| Schlagwörter: |
|---|
| Übergeordnetes Werk: |
Enthalten in: Journal of advanced transportation - Durham, NC : Assoc., 1979, 50(2016), 1, Seite 50-68 |
|---|---|
| Übergeordnetes Werk: |
volume:50 ; year:2016 ; number:1 ; pages:50-68 |
| Links: |
|---|
| DOI / URN: |
10.1002/atr.1309 |
|---|
| Katalog-ID: |
OLC1967972362 |
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| 520 | |a This paper describes a case study comparing a multi‐class transit assignment model with its single class counterpart for estimating the passenger flows of the Beijing subway network—one of the largest railway transit networks in the world. Multi‐class traffic assignment has been widely considered as a theoretically sound approach to capture the inherent variation in users' route choice behavior. However, few empirical studies have been devoted to showing the effectiveness of this approach in improving the accuracy of the underlying passenger flow estimation process. In this research, a passenger classification scheme is proposed on the basis of a dataset from a large stated preference survey conducted in the City of Beijing, China. Separate generalized cost functions are calibrated for different classes of subway users in Beijing and applied in a multi‐class transit assignment model for estimating passenger flows over a subway network. The case study has shown that the proposed multi‐class approach resulted in significantly improved estimation results with an average estimation error of less than 15% on the transfer flows as compared with 30% for the single class model. Copyright © 2015 John Wiley & Sons, Ltd. | ||
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10.1002/atr.1309 doi PQ20160617 (DE-627)OLC1967972362 (DE-599)GBVOLC1967972362 (PRQ)p909-4cb8f6de77c3874bfa6525ff23bc8f163328ea4db2a2880a87453c19082ef3860 (KEY)0082750920160000050000100050multiclasstransitassignmentmodelforestimatingtrans DE-627 ger DE-627 rakwb eng 380 ZDB 55.21 bkl Si, Bingfeng verfasserin aut A multi‐class transit assignment model for estimating transit passenger flows—a case study of Beijing subway network 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier This paper describes a case study comparing a multi‐class transit assignment model with its single class counterpart for estimating the passenger flows of the Beijing subway network—one of the largest railway transit networks in the world. Multi‐class traffic assignment has been widely considered as a theoretically sound approach to capture the inherent variation in users' route choice behavior. However, few empirical studies have been devoted to showing the effectiveness of this approach in improving the accuracy of the underlying passenger flow estimation process. In this research, a passenger classification scheme is proposed on the basis of a dataset from a large stated preference survey conducted in the City of Beijing, China. Separate generalized cost functions are calibrated for different classes of subway users in Beijing and applied in a multi‐class transit assignment model for estimating passenger flows over a subway network. The case study has shown that the proposed multi‐class approach resulted in significantly improved estimation results with an average estimation error of less than 15% on the transfer flows as compared with 30% for the single class model. Copyright © 2015 John Wiley & Sons, Ltd. Nutzungsrecht: Copyright © 2015 John Wiley & Sons, Ltd. networks travel time transit networks metro Fu, Liping oth Liu, Jianfeng oth Shiravi, Sajad oth Gao, Ziyou oth Enthalten in Journal of advanced transportation Durham, NC : Assoc., 1979 50(2016), 1, Seite 50-68 (DE-627)129616958 (DE-600)244227-9 (DE-576)015115658 0197-6729 nnns volume:50 year:2016 number:1 pages:50-68 http://dx.doi.org/10.1002/atr.1309 Volltext http://onlinelibrary.wiley.com/doi/10.1002/atr.1309/abstract http://search.proquest.com/docview/1756389497 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 55.21 AVZ AR 50 2016 1 50-68 |
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10.1002/atr.1309 doi PQ20160617 (DE-627)OLC1967972362 (DE-599)GBVOLC1967972362 (PRQ)p909-4cb8f6de77c3874bfa6525ff23bc8f163328ea4db2a2880a87453c19082ef3860 (KEY)0082750920160000050000100050multiclasstransitassignmentmodelforestimatingtrans DE-627 ger DE-627 rakwb eng 380 ZDB 55.21 bkl Si, Bingfeng verfasserin aut A multi‐class transit assignment model for estimating transit passenger flows—a case study of Beijing subway network 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier This paper describes a case study comparing a multi‐class transit assignment model with its single class counterpart for estimating the passenger flows of the Beijing subway network—one of the largest railway transit networks in the world. Multi‐class traffic assignment has been widely considered as a theoretically sound approach to capture the inherent variation in users' route choice behavior. However, few empirical studies have been devoted to showing the effectiveness of this approach in improving the accuracy of the underlying passenger flow estimation process. In this research, a passenger classification scheme is proposed on the basis of a dataset from a large stated preference survey conducted in the City of Beijing, China. Separate generalized cost functions are calibrated for different classes of subway users in Beijing and applied in a multi‐class transit assignment model for estimating passenger flows over a subway network. The case study has shown that the proposed multi‐class approach resulted in significantly improved estimation results with an average estimation error of less than 15% on the transfer flows as compared with 30% for the single class model. Copyright © 2015 John Wiley & Sons, Ltd. Nutzungsrecht: Copyright © 2015 John Wiley & Sons, Ltd. networks travel time transit networks metro Fu, Liping oth Liu, Jianfeng oth Shiravi, Sajad oth Gao, Ziyou oth Enthalten in Journal of advanced transportation Durham, NC : Assoc., 1979 50(2016), 1, Seite 50-68 (DE-627)129616958 (DE-600)244227-9 (DE-576)015115658 0197-6729 nnns volume:50 year:2016 number:1 pages:50-68 http://dx.doi.org/10.1002/atr.1309 Volltext http://onlinelibrary.wiley.com/doi/10.1002/atr.1309/abstract http://search.proquest.com/docview/1756389497 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 55.21 AVZ AR 50 2016 1 50-68 |
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10.1002/atr.1309 doi PQ20160617 (DE-627)OLC1967972362 (DE-599)GBVOLC1967972362 (PRQ)p909-4cb8f6de77c3874bfa6525ff23bc8f163328ea4db2a2880a87453c19082ef3860 (KEY)0082750920160000050000100050multiclasstransitassignmentmodelforestimatingtrans DE-627 ger DE-627 rakwb eng 380 ZDB 55.21 bkl Si, Bingfeng verfasserin aut A multi‐class transit assignment model for estimating transit passenger flows—a case study of Beijing subway network 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier This paper describes a case study comparing a multi‐class transit assignment model with its single class counterpart for estimating the passenger flows of the Beijing subway network—one of the largest railway transit networks in the world. Multi‐class traffic assignment has been widely considered as a theoretically sound approach to capture the inherent variation in users' route choice behavior. However, few empirical studies have been devoted to showing the effectiveness of this approach in improving the accuracy of the underlying passenger flow estimation process. In this research, a passenger classification scheme is proposed on the basis of a dataset from a large stated preference survey conducted in the City of Beijing, China. Separate generalized cost functions are calibrated for different classes of subway users in Beijing and applied in a multi‐class transit assignment model for estimating passenger flows over a subway network. The case study has shown that the proposed multi‐class approach resulted in significantly improved estimation results with an average estimation error of less than 15% on the transfer flows as compared with 30% for the single class model. Copyright © 2015 John Wiley & Sons, Ltd. Nutzungsrecht: Copyright © 2015 John Wiley & Sons, Ltd. networks travel time transit networks metro Fu, Liping oth Liu, Jianfeng oth Shiravi, Sajad oth Gao, Ziyou oth Enthalten in Journal of advanced transportation Durham, NC : Assoc., 1979 50(2016), 1, Seite 50-68 (DE-627)129616958 (DE-600)244227-9 (DE-576)015115658 0197-6729 nnns volume:50 year:2016 number:1 pages:50-68 http://dx.doi.org/10.1002/atr.1309 Volltext http://onlinelibrary.wiley.com/doi/10.1002/atr.1309/abstract http://search.proquest.com/docview/1756389497 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 55.21 AVZ AR 50 2016 1 50-68 |
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multi‐class transit assignment model for estimating transit passenger flows—a case study of beijing subway network |
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A multi‐class transit assignment model for estimating transit passenger flows—a case study of Beijing subway network |
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This paper describes a case study comparing a multi‐class transit assignment model with its single class counterpart for estimating the passenger flows of the Beijing subway network—one of the largest railway transit networks in the world. Multi‐class traffic assignment has been widely considered as a theoretically sound approach to capture the inherent variation in users' route choice behavior. However, few empirical studies have been devoted to showing the effectiveness of this approach in improving the accuracy of the underlying passenger flow estimation process. In this research, a passenger classification scheme is proposed on the basis of a dataset from a large stated preference survey conducted in the City of Beijing, China. Separate generalized cost functions are calibrated for different classes of subway users in Beijing and applied in a multi‐class transit assignment model for estimating passenger flows over a subway network. The case study has shown that the proposed multi‐class approach resulted in significantly improved estimation results with an average estimation error of less than 15% on the transfer flows as compared with 30% for the single class model. Copyright © 2015 John Wiley & Sons, Ltd. |
| abstractGer |
This paper describes a case study comparing a multi‐class transit assignment model with its single class counterpart for estimating the passenger flows of the Beijing subway network—one of the largest railway transit networks in the world. Multi‐class traffic assignment has been widely considered as a theoretically sound approach to capture the inherent variation in users' route choice behavior. However, few empirical studies have been devoted to showing the effectiveness of this approach in improving the accuracy of the underlying passenger flow estimation process. In this research, a passenger classification scheme is proposed on the basis of a dataset from a large stated preference survey conducted in the City of Beijing, China. Separate generalized cost functions are calibrated for different classes of subway users in Beijing and applied in a multi‐class transit assignment model for estimating passenger flows over a subway network. The case study has shown that the proposed multi‐class approach resulted in significantly improved estimation results with an average estimation error of less than 15% on the transfer flows as compared with 30% for the single class model. Copyright © 2015 John Wiley & Sons, Ltd. |
| abstract_unstemmed |
This paper describes a case study comparing a multi‐class transit assignment model with its single class counterpart for estimating the passenger flows of the Beijing subway network—one of the largest railway transit networks in the world. Multi‐class traffic assignment has been widely considered as a theoretically sound approach to capture the inherent variation in users' route choice behavior. However, few empirical studies have been devoted to showing the effectiveness of this approach in improving the accuracy of the underlying passenger flow estimation process. In this research, a passenger classification scheme is proposed on the basis of a dataset from a large stated preference survey conducted in the City of Beijing, China. Separate generalized cost functions are calibrated for different classes of subway users in Beijing and applied in a multi‐class transit assignment model for estimating passenger flows over a subway network. The case study has shown that the proposed multi‐class approach resulted in significantly improved estimation results with an average estimation error of less than 15% on the transfer flows as compared with 30% for the single class model. Copyright © 2015 John Wiley & Sons, Ltd. |
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A multi‐class transit assignment model for estimating transit passenger flows—a case study of Beijing subway network |
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Fu, Liping Liu, Jianfeng Shiravi, Sajad Gao, Ziyou |
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