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- Publisher Website: 10.1080/21680566.2024.2423235
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Article: Quantifying traffic emission reductions and traffic congestion alleviation from high-capacity ride-sharing
| Title | Quantifying traffic emission reductions and traffic congestion alleviation from high-capacity ride-sharing |
|---|---|
| Authors | |
| Keywords | high-capacity ride-sharing On-demand mobility shared mobility traffic congestion traffic emissions |
| Issue Date | 5-Nov-2024 |
| Publisher | Taylor and Francis Group |
| Citation | Transportmetrica B: Transport Dynamics, 2024, v. 12, n. 1 How to Cite? |
| Abstract | Despite the promising benefits that ride-sharing offers, there has been a lack of research on the benefits of high-capacity ride-sharing services. Prior research has also overlooked the relationship between traffic volume and the degree of traffic congestion and emissions. To address these gaps, this study develops an open-source agent-based simulation platform and a heuristic algorithm to quantify the benefits of high-capacity ride-sharing with significantly lower computational costs. The simulation platform integrates a traffic emission model and a speed-density traffic flow model to characterise the interactions between traffic congestion levels and emissions. The experiment results demonstrate that ride-sharing with vehicle capacities of 2, 4, and 6 passengers can alleviate total traffic congestion by approximately 3%, 4%, and 5%, and reduce traffic emissions of a ride-sourcing system by approximately 30%, 45%, and 50%, respectively. This study can guide transportation network companies in designing and managing more efficient and environment-friendly mobility systems. |
| Persistent Identifier | http://hdl.handle.net/10722/353788 |
| ISSN | 2023 Impact Factor: 3.3 2023 SCImago Journal Rankings: 1.188 |
| ISI Accession Number ID |
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Chen, Wang | - |
| dc.contributor.author | Ke, Jintao | - |
| dc.contributor.author | Chen, Xiqun | - |
| dc.date.accessioned | 2025-01-24T00:35:51Z | - |
| dc.date.available | 2025-01-24T00:35:51Z | - |
| dc.date.issued | 2024-11-05 | - |
| dc.identifier.citation | Transportmetrica B: Transport Dynamics, 2024, v. 12, n. 1 | - |
| dc.identifier.issn | 2168-0566 | - |
| dc.identifier.uri | http://hdl.handle.net/10722/353788 | - |
| dc.description.abstract | Despite the promising benefits that ride-sharing offers, there has been a lack of research on the benefits of high-capacity ride-sharing services. Prior research has also overlooked the relationship between traffic volume and the degree of traffic congestion and emissions. To address these gaps, this study develops an open-source agent-based simulation platform and a heuristic algorithm to quantify the benefits of high-capacity ride-sharing with significantly lower computational costs. The simulation platform integrates a traffic emission model and a speed-density traffic flow model to characterise the interactions between traffic congestion levels and emissions. The experiment results demonstrate that ride-sharing with vehicle capacities of 2, 4, and 6 passengers can alleviate total traffic congestion by approximately 3%, 4%, and 5%, and reduce traffic emissions of a ride-sourcing system by approximately 30%, 45%, and 50%, respectively. This study can guide transportation network companies in designing and managing more efficient and environment-friendly mobility systems. | - |
| dc.language | eng | - |
| dc.publisher | Taylor and Francis Group | - |
| dc.relation.ispartof | Transportmetrica B: Transport Dynamics | - |
| dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
| dc.subject | high-capacity ride-sharing | - |
| dc.subject | On-demand mobility | - |
| dc.subject | shared mobility | - |
| dc.subject | traffic congestion | - |
| dc.subject | traffic emissions | - |
| dc.title | Quantifying traffic emission reductions and traffic congestion alleviation from high-capacity ride-sharing | - |
| dc.type | Article | - |
| dc.identifier.doi | 10.1080/21680566.2024.2423235 | - |
| dc.identifier.scopus | eid_2-s2.0-85209583848 | - |
| dc.identifier.volume | 12 | - |
| dc.identifier.issue | 1 | - |
| dc.identifier.eissn | 2168-0582 | - |
| dc.identifier.isi | WOS:001349154900001 | - |
| dc.identifier.issnl | 2168-0566 | - |
