Flexible mobility on-demand: an environmental scan
Open accessAbstract
On-demand shared mobility is increasingly being promoted as an influential strategy to address urban transport challenges in large and fast-growing cities. The appeal of this form of transport is largely attributed to its convenience, ease of use, and affordability made possible through digital platforms and innovations. The convergence of the shared economy with a number of established and emerging technologies—such as artificial intelligence (AI), Internet of Things (IoT), and Cloud and Fog computing—is helping to expedite their deployment as a new form of public transport. Recently, this has manifested itself in the form of Flexible Mobility on Demand (FMoD) solutions, aimed at meeting personal travel demands through flexible routing and scheduling. Increasingly, these shared mobility solutions are blurring the boundaries with existing forms of public transport, particularly bus operations. This paper presents an environmental scan and analysis of the technological, social, and economic impacts surrounding disruptive technology-driven shared mobility trends. Specifically, the paper includes an examination of current and anticipated external factors that are of direct relevance to collaborative and low carbon mobility. The paper also outlines how these trends are likely to influence the mobility industries now and into the future. The paper collates information from a wide body of literature and reports on findings from actual ‘use cases’ that exist today which have used these disruptive mobility solutions to deliver substantial benefits to travellers around the world. Finally, the paper provides stakeholders with insight into identifying and responding to the likely needs and impacts of FMoD and informs their policy and strategy positions on the implementation of smart mobility systems in their cities and jurisdictions.
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Related initiatives in sustainable transportation have been advanced by applying ML, CV, and DSS utilizing ANN, DT, NC, SVM, FL, and time series models.
“Related initiatives have been advanced by applying ML, CV, and DSS utilizing ANN, DT, NC, SVM, FL, and time series models. Key studies in this area include those by Jiang and Zhang [187] and Liyanage et al. [101].”
From Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review (2024) · cites this paper as [101]
Research in sustainable transportation mainly considered ML (ANN, DT, and conventional statistical and time series models) for traffic planning and control, and on-demand public transit.
“Research in this domain mainly considered ML (ANN, DT, and conventional statistical and time series models) for traffic planning and control, and on-demand public transit (Liyanage et al., 2019).”
From Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda (2020) · cites this paper as Liyanage et al., 2019
FL decision support systems were applied to problems such as routing, fraud prevention, and mobility on-demand.
“FL decision support systems were applied to problems such as routing, fraud prevention, and mobility on-demand (Liyanage et al., 2019; Wen et al., 2019).”
From Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda (2020) · cites this paper as Liyanage et al., 2019
Applications of CV and decision support were found in traffic management, traffic safety, public transportation, and urban mobility.
“but applications of CV and decision support were found in traffic management, traffic safety, public transportation, and urban mobility (Liyanage et al., 2019).”
From Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda (2020) · cites this paper as Liyanage et al., 2019
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Cited by
Sorted by citations
Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda
Nishant, R., Kennedy, M., & Corbett, J. (2020) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2020.102104 · Cited by 1,203 · see citing works
Bibri, S. E., Krogstie, J., Kaboli, A., & Alahi, A. (2024) · Environmental Science and Ecotechnology · doi:10.1016/j.ese.2023.100330 · Cited by 536 · see citing works
- Authors
- Liyanage, S., Dia, H., Abduljabbar, R., & Bagloee, S.
- Affiliations
- Swinburne University of Technology (AU)
- Corresponding author
- Liyanage, Sohani
- Dia, Hussein
- Venue
- Sustainability
- Publisher
- MDPI AG
- Published
- 2019-02-27
- Volume
- 11
- Issue
- 5
- Pages
- 1262
- ISSN
- 2071-1050 (ISSN-L)
- OpenAlex
- W2917854307
- Cited by
- 111
- References
- 193
- Citations by year
- 2026: 13 · 2025: 21 · 2024: 17 · 2023: 12 · 2022: 16 · 2021: 13 · 2020: 15 · 2019: 4
- FWCI
- 5.47
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Sustainability (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Swinburne figshare (Swinburne University of Technology) (submittedVersion, other-oa)
- Funders
- Swinburne University of Technology
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Transportation and Mobility Innovations
- Social Sciences › Transportation › Transportation Planning and Optimization
- Social Sciences › Business, Management and Accounting › Marketing › Sharing Economy and Platforms
- Keywords
- shared mobility, on-demand public transport, disruptive technologies, sharing economy, artificial intelligence, Internet of Things, low-carbon transportation, urban transport, bus operations, digital platforms
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