Unveiling the influential factors for customized bus service reopening from naturalistic observations in Shanghai

This work attempts to understand how a customized bus (CB) operator decides to open or close a CB line. We look into the changes in the operation status of CB lines (i.e. reopening and closure) from one of the largest CB operators in Shanghai, China, with a 22-month consecutive observation ranging f...
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Gespeichert in:

Autor*in:	Yu Shen [verfasserIn] Chenlong Xu [verfasserIn] Shengchuan Jiang [verfasserIn] Zhikang Zhai [verfasserIn] Yuxiong Ji [verfasserIn] Yuchuan Du [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Customized bus Decision-making Naturalistic observations Discrete choice models Explainable machine learning

Übergeordnetes Werk:	In: International Journal of Transportation Science and Technology - KeAi Communications Co., Ltd., 2017, 13(2024), Seite 106-121
Übergeordnetes Werk:	volume:13 ; year:2024 ; pages:106-121

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.ijtst.2023.12.002

Katalog-ID:	DOAJ098496425

Internformat


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allfields	10.1016/j.ijtst.2023.12.002 doi (DE-627)DOAJ098496425 (DE-599)DOAJf46b2a6f0ea74f50a8c8f89dbf2c53c2 DE-627 ger DE-627 rakwb eng TA1001-1280 Yu Shen verfasserin aut Unveiling the influential factors for customized bus service reopening from naturalistic observations in Shanghai 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This work attempts to understand how a customized bus (CB) operator decides to open or close a CB line. We look into the changes in the operation status of CB lines (i.e. reopening and closure) from one of the largest CB operators in Shanghai, China, with a 22-month consecutive observation ranging from January 2019 to October 2020. As all CB services were totally suspended at the beginning of 2020 due to the COVID-19 travel restriction and then gradually recovered in March 2020, we utilize this study period as a naturalistic observation experiment to investigate the changes in the operation status of each CB line before and after the travel restriction. Using the operation status at each month as the binary alternatives, the mixed logit models and the tree-based models with explainable machine learning techniques are respectively adopted to explore the factors that influence the decision-making process. The findings from both types of models are in general consistent. The results show that the characteristics of each CB line including the ridership, the length of the line, the closeness to charging stations, and the overlap of CB lines significantly impact the decisions. In addition, the land-use types around the CB stops and the market competition from alternative travel modes also play a key role in making the decisions. Customized bus Decision-making Naturalistic observations Discrete choice models Explainable machine learning Transportation engineering Chenlong Xu verfasserin aut Shengchuan Jiang verfasserin aut Zhikang Zhai verfasserin aut Yuxiong Ji verfasserin aut Yuchuan Du verfasserin aut In International Journal of Transportation Science and Technology KeAi Communications Co., Ltd., 2017 13(2024), Seite 106-121 (DE-627)880472332 (DE-600)2884863-9 20460449 nnns volume:13 year:2024 pages:106-121 https://doi.org/10.1016/j.ijtst.2023.12.002 kostenfrei https://doaj.org/article/f46b2a6f0ea74f50a8c8f89dbf2c53c2 kostenfrei http://www.sciencedirect.com/science/article/pii/S2046043023001077 kostenfrei https://doaj.org/toc/2046-0430 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 13 2024 106-121
spelling	10.1016/j.ijtst.2023.12.002 doi (DE-627)DOAJ098496425 (DE-599)DOAJf46b2a6f0ea74f50a8c8f89dbf2c53c2 DE-627 ger DE-627 rakwb eng TA1001-1280 Yu Shen verfasserin aut Unveiling the influential factors for customized bus service reopening from naturalistic observations in Shanghai 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This work attempts to understand how a customized bus (CB) operator decides to open or close a CB line. We look into the changes in the operation status of CB lines (i.e. reopening and closure) from one of the largest CB operators in Shanghai, China, with a 22-month consecutive observation ranging from January 2019 to October 2020. As all CB services were totally suspended at the beginning of 2020 due to the COVID-19 travel restriction and then gradually recovered in March 2020, we utilize this study period as a naturalistic observation experiment to investigate the changes in the operation status of each CB line before and after the travel restriction. Using the operation status at each month as the binary alternatives, the mixed logit models and the tree-based models with explainable machine learning techniques are respectively adopted to explore the factors that influence the decision-making process. The findings from both types of models are in general consistent. The results show that the characteristics of each CB line including the ridership, the length of the line, the closeness to charging stations, and the overlap of CB lines significantly impact the decisions. In addition, the land-use types around the CB stops and the market competition from alternative travel modes also play a key role in making the decisions. Customized bus Decision-making Naturalistic observations Discrete choice models Explainable machine learning Transportation engineering Chenlong Xu verfasserin aut Shengchuan Jiang verfasserin aut Zhikang Zhai verfasserin aut Yuxiong Ji verfasserin aut Yuchuan Du verfasserin aut In International Journal of Transportation Science and Technology KeAi Communications Co., Ltd., 2017 13(2024), Seite 106-121 (DE-627)880472332 (DE-600)2884863-9 20460449 nnns volume:13 year:2024 pages:106-121 https://doi.org/10.1016/j.ijtst.2023.12.002 kostenfrei https://doaj.org/article/f46b2a6f0ea74f50a8c8f89dbf2c53c2 kostenfrei http://www.sciencedirect.com/science/article/pii/S2046043023001077 kostenfrei https://doaj.org/toc/2046-0430 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 13 2024 106-121
allfields_unstemmed	10.1016/j.ijtst.2023.12.002 doi (DE-627)DOAJ098496425 (DE-599)DOAJf46b2a6f0ea74f50a8c8f89dbf2c53c2 DE-627 ger DE-627 rakwb eng TA1001-1280 Yu Shen verfasserin aut Unveiling the influential factors for customized bus service reopening from naturalistic observations in Shanghai 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This work attempts to understand how a customized bus (CB) operator decides to open or close a CB line. We look into the changes in the operation status of CB lines (i.e. reopening and closure) from one of the largest CB operators in Shanghai, China, with a 22-month consecutive observation ranging from January 2019 to October 2020. As all CB services were totally suspended at the beginning of 2020 due to the COVID-19 travel restriction and then gradually recovered in March 2020, we utilize this study period as a naturalistic observation experiment to investigate the changes in the operation status of each CB line before and after the travel restriction. Using the operation status at each month as the binary alternatives, the mixed logit models and the tree-based models with explainable machine learning techniques are respectively adopted to explore the factors that influence the decision-making process. The findings from both types of models are in general consistent. The results show that the characteristics of each CB line including the ridership, the length of the line, the closeness to charging stations, and the overlap of CB lines significantly impact the decisions. In addition, the land-use types around the CB stops and the market competition from alternative travel modes also play a key role in making the decisions. Customized bus Decision-making Naturalistic observations Discrete choice models Explainable machine learning Transportation engineering Chenlong Xu verfasserin aut Shengchuan Jiang verfasserin aut Zhikang Zhai verfasserin aut Yuxiong Ji verfasserin aut Yuchuan Du verfasserin aut In International Journal of Transportation Science and Technology KeAi Communications Co., Ltd., 2017 13(2024), Seite 106-121 (DE-627)880472332 (DE-600)2884863-9 20460449 nnns volume:13 year:2024 pages:106-121 https://doi.org/10.1016/j.ijtst.2023.12.002 kostenfrei https://doaj.org/article/f46b2a6f0ea74f50a8c8f89dbf2c53c2 kostenfrei http://www.sciencedirect.com/science/article/pii/S2046043023001077 kostenfrei https://doaj.org/toc/2046-0430 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 13 2024 106-121
allfieldsGer	10.1016/j.ijtst.2023.12.002 doi (DE-627)DOAJ098496425 (DE-599)DOAJf46b2a6f0ea74f50a8c8f89dbf2c53c2 DE-627 ger DE-627 rakwb eng TA1001-1280 Yu Shen verfasserin aut Unveiling the influential factors for customized bus service reopening from naturalistic observations in Shanghai 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This work attempts to understand how a customized bus (CB) operator decides to open or close a CB line. We look into the changes in the operation status of CB lines (i.e. reopening and closure) from one of the largest CB operators in Shanghai, China, with a 22-month consecutive observation ranging from January 2019 to October 2020. As all CB services were totally suspended at the beginning of 2020 due to the COVID-19 travel restriction and then gradually recovered in March 2020, we utilize this study period as a naturalistic observation experiment to investigate the changes in the operation status of each CB line before and after the travel restriction. Using the operation status at each month as the binary alternatives, the mixed logit models and the tree-based models with explainable machine learning techniques are respectively adopted to explore the factors that influence the decision-making process. The findings from both types of models are in general consistent. The results show that the characteristics of each CB line including the ridership, the length of the line, the closeness to charging stations, and the overlap of CB lines significantly impact the decisions. In addition, the land-use types around the CB stops and the market competition from alternative travel modes also play a key role in making the decisions. Customized bus Decision-making Naturalistic observations Discrete choice models Explainable machine learning Transportation engineering Chenlong Xu verfasserin aut Shengchuan Jiang verfasserin aut Zhikang Zhai verfasserin aut Yuxiong Ji verfasserin aut Yuchuan Du verfasserin aut In International Journal of Transportation Science and Technology KeAi Communications Co., Ltd., 2017 13(2024), Seite 106-121 (DE-627)880472332 (DE-600)2884863-9 20460449 nnns volume:13 year:2024 pages:106-121 https://doi.org/10.1016/j.ijtst.2023.12.002 kostenfrei https://doaj.org/article/f46b2a6f0ea74f50a8c8f89dbf2c53c2 kostenfrei http://www.sciencedirect.com/science/article/pii/S2046043023001077 kostenfrei https://doaj.org/toc/2046-0430 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 13 2024 106-121
allfieldsSound	10.1016/j.ijtst.2023.12.002 doi (DE-627)DOAJ098496425 (DE-599)DOAJf46b2a6f0ea74f50a8c8f89dbf2c53c2 DE-627 ger DE-627 rakwb eng TA1001-1280 Yu Shen verfasserin aut Unveiling the influential factors for customized bus service reopening from naturalistic observations in Shanghai 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This work attempts to understand how a customized bus (CB) operator decides to open or close a CB line. We look into the changes in the operation status of CB lines (i.e. reopening and closure) from one of the largest CB operators in Shanghai, China, with a 22-month consecutive observation ranging from January 2019 to October 2020. As all CB services were totally suspended at the beginning of 2020 due to the COVID-19 travel restriction and then gradually recovered in March 2020, we utilize this study period as a naturalistic observation experiment to investigate the changes in the operation status of each CB line before and after the travel restriction. Using the operation status at each month as the binary alternatives, the mixed logit models and the tree-based models with explainable machine learning techniques are respectively adopted to explore the factors that influence the decision-making process. The findings from both types of models are in general consistent. The results show that the characteristics of each CB line including the ridership, the length of the line, the closeness to charging stations, and the overlap of CB lines significantly impact the decisions. In addition, the land-use types around the CB stops and the market competition from alternative travel modes also play a key role in making the decisions. Customized bus Decision-making Naturalistic observations Discrete choice models Explainable machine learning Transportation engineering Chenlong Xu verfasserin aut Shengchuan Jiang verfasserin aut Zhikang Zhai verfasserin aut Yuxiong Ji verfasserin aut Yuchuan Du verfasserin aut In International Journal of Transportation Science and Technology KeAi Communications Co., Ltd., 2017 13(2024), Seite 106-121 (DE-627)880472332 (DE-600)2884863-9 20460449 nnns volume:13 year:2024 pages:106-121 https://doi.org/10.1016/j.ijtst.2023.12.002 kostenfrei https://doaj.org/article/f46b2a6f0ea74f50a8c8f89dbf2c53c2 kostenfrei http://www.sciencedirect.com/science/article/pii/S2046043023001077 kostenfrei https://doaj.org/toc/2046-0430 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 13 2024 106-121
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callnumber-first	T - Technology
author	Yu Shen
spellingShingle	Yu Shen misc TA1001-1280 misc Customized bus misc Decision-making misc Naturalistic observations misc Discrete choice models misc Explainable machine learning misc Transportation engineering Unveiling the influential factors for customized bus service reopening from naturalistic observations in Shanghai
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topic_title	TA1001-1280 Unveiling the influential factors for customized bus service reopening from naturalistic observations in Shanghai Customized bus Decision-making Naturalistic observations Discrete choice models Explainable machine learning
topic	misc TA1001-1280 misc Customized bus misc Decision-making misc Naturalistic observations misc Discrete choice models misc Explainable machine learning misc Transportation engineering
topic_unstemmed	misc TA1001-1280 misc Customized bus misc Decision-making misc Naturalistic observations misc Discrete choice models misc Explainable machine learning misc Transportation engineering
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title	Unveiling the influential factors for customized bus service reopening from naturalistic observations in Shanghai
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title_full	Unveiling the influential factors for customized bus service reopening from naturalistic observations in Shanghai
author_sort	Yu Shen
journal	International Journal of Transportation Science and Technology
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title_sort	unveiling the influential factors for customized bus service reopening from naturalistic observations in shanghai
callnumber	TA1001-1280
title_auth	Unveiling the influential factors for customized bus service reopening from naturalistic observations in Shanghai
abstract	This work attempts to understand how a customized bus (CB) operator decides to open or close a CB line. We look into the changes in the operation status of CB lines (i.e. reopening and closure) from one of the largest CB operators in Shanghai, China, with a 22-month consecutive observation ranging from January 2019 to October 2020. As all CB services were totally suspended at the beginning of 2020 due to the COVID-19 travel restriction and then gradually recovered in March 2020, we utilize this study period as a naturalistic observation experiment to investigate the changes in the operation status of each CB line before and after the travel restriction. Using the operation status at each month as the binary alternatives, the mixed logit models and the tree-based models with explainable machine learning techniques are respectively adopted to explore the factors that influence the decision-making process. The findings from both types of models are in general consistent. The results show that the characteristics of each CB line including the ridership, the length of the line, the closeness to charging stations, and the overlap of CB lines significantly impact the decisions. In addition, the land-use types around the CB stops and the market competition from alternative travel modes also play a key role in making the decisions.
abstractGer	This work attempts to understand how a customized bus (CB) operator decides to open or close a CB line. We look into the changes in the operation status of CB lines (i.e. reopening and closure) from one of the largest CB operators in Shanghai, China, with a 22-month consecutive observation ranging from January 2019 to October 2020. As all CB services were totally suspended at the beginning of 2020 due to the COVID-19 travel restriction and then gradually recovered in March 2020, we utilize this study period as a naturalistic observation experiment to investigate the changes in the operation status of each CB line before and after the travel restriction. Using the operation status at each month as the binary alternatives, the mixed logit models and the tree-based models with explainable machine learning techniques are respectively adopted to explore the factors that influence the decision-making process. The findings from both types of models are in general consistent. The results show that the characteristics of each CB line including the ridership, the length of the line, the closeness to charging stations, and the overlap of CB lines significantly impact the decisions. In addition, the land-use types around the CB stops and the market competition from alternative travel modes also play a key role in making the decisions.
abstract_unstemmed	This work attempts to understand how a customized bus (CB) operator decides to open or close a CB line. We look into the changes in the operation status of CB lines (i.e. reopening and closure) from one of the largest CB operators in Shanghai, China, with a 22-month consecutive observation ranging from January 2019 to October 2020. As all CB services were totally suspended at the beginning of 2020 due to the COVID-19 travel restriction and then gradually recovered in March 2020, we utilize this study period as a naturalistic observation experiment to investigate the changes in the operation status of each CB line before and after the travel restriction. Using the operation status at each month as the binary alternatives, the mixed logit models and the tree-based models with explainable machine learning techniques are respectively adopted to explore the factors that influence the decision-making process. The findings from both types of models are in general consistent. The results show that the characteristics of each CB line including the ridership, the length of the line, the closeness to charging stations, and the overlap of CB lines significantly impact the decisions. In addition, the land-use types around the CB stops and the market competition from alternative travel modes also play a key role in making the decisions.
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title_short	Unveiling the influential factors for customized bus service reopening from naturalistic observations in Shanghai
url	https://doi.org/10.1016/j.ijtst.2023.12.002 https://doaj.org/article/f46b2a6f0ea74f50a8c8f89dbf2c53c2 http://www.sciencedirect.com/science/article/pii/S2046043023001077 https://doaj.org/toc/2046-0430
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author2	Chenlong Xu Shengchuan Jiang Zhikang Zhai Yuxiong Ji Yuchuan Du
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up_date	2024-07-03T17:35:56.127Z
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Unveiling the influential factors for customized bus service reopening from naturalistic observations in Shanghai

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