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- Publisher Website: 10.1080/01441647.2023.2165574
- Scopus: eid_2-s2.0-85146723197
- WOS: WOS:000909504000001
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Article: Adaptability analysis methods of demand responsive transit: a review and future directions
Title | Adaptability analysis methods of demand responsive transit: a review and future directions |
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Authors | |
Keywords | adaptability analysis agent-based models boundary condition decision models case studies Demand-responsive transit evaluation models |
Issue Date | 8-Jan-2023 |
Publisher | Taylor and Francis Group |
Citation | Transport Reviews, 2023, v. 43, n. 4, p. 676-697 How to Cite? |
Abstract | Demand responsive transit (DRT) echoes the new requirements of modern travel on flexibility and carbon reduction, as well as achieving a better match between demand and supply. However, many DRTs still failed. An important step named adaptability analysis helps to understand the context, desirability, and feasibility of introducing DRT. An adaptability analysis includes three sub-questions. Question 1 focuses on policy, regulation, funding, and technologies. Question 2 looks at the interactions of travel demand with operation parameters such as fare and fleet size. Question 3 tries to figure out the impacts of DRT on mobility, society, and the environment. To answer Question 1, macro-level methods collect information and generalise from empirical knowledge, including experience and barriers from real-world operation cases. To answer Question 2, meso-level methods determine the operation mode of DRT by quantifying related factors and establishing evaluation models or boundary condition decision models. To answer Question 3, micro-level methods use microscopic models for simulating the interaction between passengers and vehicles under different scenarios. This paper further discusses the advantages, disadvantages, and future directions of adaptability analysis methods of DRT. Overall, DRT presents great potential and future adaptability analysis should be developed by considering new trends in DRT and more complex and practical-oriented scenarios. |
Persistent Identifier | http://hdl.handle.net/10722/337424 |
ISSN | 2023 Impact Factor: 9.5 2023 SCImago Journal Rankings: 3.016 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Wang, Hui | - |
dc.contributor.author | Li, Jinyang | - |
dc.contributor.author | Wang, Pengling | - |
dc.contributor.author | Teng, Jing | - |
dc.contributor.author | Loo, Becky Pui Ying | - |
dc.date.accessioned | 2024-03-11T10:20:46Z | - |
dc.date.available | 2024-03-11T10:20:46Z | - |
dc.date.issued | 2023-01-08 | - |
dc.identifier.citation | Transport Reviews, 2023, v. 43, n. 4, p. 676-697 | - |
dc.identifier.issn | 0144-1647 | - |
dc.identifier.uri | http://hdl.handle.net/10722/337424 | - |
dc.description.abstract | <p>Demand responsive transit (DRT) echoes the new requirements of modern travel on flexibility and carbon reduction, as well as achieving a better match between demand and supply. However, many DRTs still failed. An important step named adaptability analysis helps to understand the context, desirability, and feasibility of introducing DRT. An adaptability analysis includes three sub-questions. Question 1 focuses on policy, regulation, funding, and technologies. Question 2 looks at the interactions of travel demand with operation parameters such as fare and fleet size. Question 3 tries to figure out the impacts of DRT on mobility, society, and the environment. To answer Question 1, macro-level methods collect information and generalise from empirical knowledge, including experience and barriers from real-world operation cases. To answer Question 2, meso-level methods determine the operation mode of DRT by quantifying related factors and establishing evaluation models or boundary condition decision models. To answer Question 3, micro-level methods use microscopic models for simulating the interaction between passengers and vehicles under different scenarios. This paper further discusses the advantages, disadvantages, and future directions of adaptability analysis methods of DRT. Overall, DRT presents great potential and future adaptability analysis should be developed by considering new trends in DRT and more complex and practical-oriented scenarios.<br></p> | - |
dc.language | eng | - |
dc.publisher | Taylor and Francis Group | - |
dc.relation.ispartof | Transport Reviews | - |
dc.subject | adaptability analysis | - |
dc.subject | agent-based models | - |
dc.subject | boundary condition decision models | - |
dc.subject | case studies | - |
dc.subject | Demand-responsive transit | - |
dc.subject | evaluation models | - |
dc.title | Adaptability analysis methods of demand responsive transit: a review and future directions | - |
dc.type | Article | - |
dc.identifier.doi | 10.1080/01441647.2023.2165574 | - |
dc.identifier.scopus | eid_2-s2.0-85146723197 | - |
dc.identifier.volume | 43 | - |
dc.identifier.issue | 4 | - |
dc.identifier.spage | 676 | - |
dc.identifier.epage | 697 | - |
dc.identifier.eissn | 1464-5327 | - |
dc.identifier.isi | WOS:000909504000001 | - |
dc.identifier.issnl | 0144-1647 | - |