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- Publisher Website: 10.1016/j.trc.2023.104372
- Scopus: eid_2-s2.0-85173617217
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Article: To share or not to share? Revealing determinants of individuals’ willingness to share rides through a big data approach
Title | To share or not to share? Revealing determinants of individuals’ willingness to share rides through a big data approach |
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Authors | |
Keywords | Discrete choice modelling Ridesplitting Shared mobility Spatiotemporal analysis Willingness to share |
Issue Date | 10-Oct-2023 |
Publisher | Elsevier |
Citation | Transportation Research Part C: Emerging Technologies, 2023, v. 157 How to Cite? |
Abstract | Ridesplitting has been widely recognised as a promising mobility mode for sustainable transportation, but its success largely depends on a sufficient number of passengers who are willing to share their rides. To uncover the determinants of willingness to share (WTS), prior studies typically relied on either individual-level survey-based or aggregate-level data-driven methods. To combine the former’s strength in capturing individual choice preferences and the latter’s advantage in utilising available multi-source big data, this study proposes a big data approach to modelling individual choices between the solo and shared options for each trip. To reconstruct the choice process, we leverage large-scale real-world trip records and propose a learning framework to not only retrieve the trip time and fare of the chosen option (solo or shared), but also impute the likely time and fare of the alternative option. These reconstructed trip attributes are then integrated with the sociodemographic, built environment and traffic features from other data sources. Finally, all these variables are fed into a random coefficient logit model to reveal passengers’ ridesplitting preferences. Through a case study of Manhattan, New York City, we reveal the spatiotemporal pattern of WTS and its determinants. Results show that WTS varies greatly across space and time. The time-fare trade-off is identified as the most essential factor, with the value of time revealed to be about $28-36/h. WTS decreases with longer trip distance/commuting time/distance to the urban centre, lower road speed, and higher speed fluctuation/bus station/crime density, but increases with a higher proportion of middle-class/female/young residents, residential land use and metro station. The proposed methodology can be used to explain and monitor WTS in a cost-effective way, complementing traditional survey-based methods to better design and promote ridesplitting services. |
Persistent Identifier | http://hdl.handle.net/10722/338890 |
ISSN | 2023 Impact Factor: 7.6 2023 SCImago Journal Rankings: 2.860 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Huang, G | - |
dc.contributor.author | Lian, T | - |
dc.contributor.author | Yeh, AGO | - |
dc.contributor.author | Zhao, Z | - |
dc.date.accessioned | 2024-03-11T10:32:17Z | - |
dc.date.available | 2024-03-11T10:32:17Z | - |
dc.date.issued | 2023-10-10 | - |
dc.identifier.citation | Transportation Research Part C: Emerging Technologies, 2023, v. 157 | - |
dc.identifier.issn | 0968-090X | - |
dc.identifier.uri | http://hdl.handle.net/10722/338890 | - |
dc.description.abstract | <p>Ridesplitting has been widely recognised as a promising mobility mode for sustainable transportation, but its success largely depends on a sufficient number of passengers who are willing to share their rides. To uncover the determinants of willingness to share (WTS), prior studies typically relied on either individual-level survey-based or aggregate-level data-driven methods. To combine the former’s strength in capturing individual choice preferences and the latter’s advantage in utilising available multi-source big data, this study proposes a big data approach to modelling individual choices between the solo and shared options for each trip. To reconstruct the choice process, we leverage large-scale real-world trip records and propose a learning framework to not only retrieve the trip time and fare of the chosen option (solo or shared), but also impute the likely time and fare of the alternative option. These reconstructed trip attributes are then integrated with the sociodemographic, built environment and traffic features from other data sources. Finally, all these variables are fed into a random coefficient logit model to reveal passengers’ ridesplitting preferences. Through a case study of Manhattan, New York City, we reveal the spatiotemporal pattern of WTS and its determinants. Results show that WTS varies greatly across space and time. The time-fare trade-off is identified as the most essential factor, with the value of time revealed to be about $28-36/h. WTS decreases with longer trip distance/commuting time/distance to the urban centre, lower road speed, and higher speed fluctuation/bus station/crime density, but increases with a higher proportion of middle-class/female/young residents, residential land use and metro station. The proposed methodology can be used to explain and monitor WTS in a cost-effective way, complementing traditional survey-based methods to better design and promote ridesplitting services.</p> | - |
dc.language | eng | - |
dc.publisher | Elsevier | - |
dc.relation.ispartof | Transportation Research Part C: Emerging Technologies | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Discrete choice modelling | - |
dc.subject | Ridesplitting | - |
dc.subject | Shared mobility | - |
dc.subject | Spatiotemporal analysis | - |
dc.subject | Willingness to share | - |
dc.title | To share or not to share? Revealing determinants of individuals’ willingness to share rides through a big data approach | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.trc.2023.104372 | - |
dc.identifier.scopus | eid_2-s2.0-85173617217 | - |
dc.identifier.volume | 157 | - |
dc.identifier.eissn | 1879-2359 | - |
dc.identifier.isi | WOS:001099603100001 | - |
dc.identifier.issnl | 0968-090X | - |