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Determining the Market Uptake of Demand Responsive Transport Enabled Public Transport Service

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  • Neeraj Saxena

    (Research Centre for Integrated Transport Innovation, School of Civil and Environmental Engineering, UNSW Australia, Sydney, NSW 2052, Australia)

  • Taha Rashidi

    (Research Centre for Integrated Transport Innovation, School of Civil and Environmental Engineering, UNSW Australia, Sydney, NSW 2052, Australia)

  • David Rey

    (Research Centre for Integrated Transport Innovation, School of Civil and Environmental Engineering, UNSW Australia, Sydney, NSW 2052, Australia)

Abstract

Demand responsive transport (DRT) alternatives offer improved mobility to travellers through station-to-destination or door-to-transit operations. In particular, door-to-transit DRT service acts as a feeder to major public transport hubs, making public transport more accessible and attractive to travellers. This work aims to study the mode choice behaviour of travellers between their current modes and a new service, which is a combination of DRT and public transport. The study is conducted in the Northern Beaches area of Sydney, Australia where DRT is expected to serve as a feeder to the newly introduced express bus service called B-Line. A stated preference (SP) experiment is designed where multiple-choice scenarios involving two modes, status quo (SQ) and the new service (combined DRT and public transit), are presented to the participants. The survey uses trip specific information obtained from Google API to form the attributes for the new service. The collected data are analysed using a latent class choice model (LCCM), which segments the observed sample into distinct groups where each group has its own taste and preferences towards the new service option. Results from the study reveal that one of the identified user segments shows 96 percent uptake towards the new service option, while the other user segment shows an uptake of 44 percent. Results also show that individuals making work trips are more likely to opt for the new service. Findings from this study can provide information to urban planners regarding the market uptake of DRT services. Furthermore, the findings can also help planners in implementing segment specific policies aimed at further improving uptake towards DRT along with public transport.

Suggested Citation

  • Neeraj Saxena & Taha Rashidi & David Rey, 2020. "Determining the Market Uptake of Demand Responsive Transport Enabled Public Transport Service," Sustainability, MDPI, vol. 12(12), pages 1-18, June.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:12:p:4914-:d:372271
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    References listed on IDEAS

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    1. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, September.
    2. Saxena, N. & Rashidi, T.H. & Dixit, V.V. & Waller, S.T., 2019. "Modelling the route choice behaviour under stop-&-go traffic for different car driver segments," Transportation Research Part A: Policy and Practice, Elsevier, vol. 119(C), pages 62-72.
    3. Yim, Y. B. & Khattak, Asad J., 2000. "Personalized Demand Responsive Transit Systems," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt47w589jx, Institute of Transportation Studies, UC Berkeley.
    4. Anspacher, David & Khattak, Asad J. & Yim, Youngbin, 2004. "Demand-Responsive Transit Shuttles: Who Will Use Them?," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt2th2n99r, Institute of Transportation Studies, UC Berkeley.
    5. Ryley, Tim J. & A. Stanley, Peter & P. Enoch, Marcus & M. Zanni, Alberto & A. Quddus, Mohammed, 2014. "Investigating the contribution of Demand Responsive Transport to a sustainable local public transport system," Research in Transportation Economics, Elsevier, vol. 48(C), pages 364-372.
    6. Vij, Akshay & Carrel, André & Walker, Joan L., 2013. "Incorporating the influence of latent modal preferences on travel mode choice behavior," Transportation Research Part A: Policy and Practice, Elsevier, vol. 54(C), pages 164-178.
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    Cited by:

    1. Maciej Kruszyna & Jacek Makuch, 2023. "Mobility Nodes as an Extension of the Idea of Transfer Nodes—Solutions for Smaller Rail Stations with an Example from Poland," Sustainability, MDPI, vol. 15(3), pages 1-15, January.
    2. Sungwon Lee & Devon Farmer & Jooyoung Kim & Hyun Kim, 2022. "Shared Autonomous Vehicles Competing with Shared Electric Bicycles: A Stated-Preference Analysis," Sustainability, MDPI, vol. 14(21), pages 1-19, November.
    3. Nael Alsaleh & Bilal Farooq & Yixue Zhang & Steven Farber, 2021. "On-Demand Transit User Preference Analysis using Hybrid Choice Models," Papers 2102.08256, arXiv.org, revised Aug 2023.
    4. Alsaleh, Nael & Farooq, Bilal & Zhang, Yixue & Farber, Steven, 2023. "On-demand transit user preference analysis using hybrid choice models," Journal of choice modelling, Elsevier, vol. 49(C).
    5. Xuemei Zhou & Guohui Wei & Yunbo Zhang & Qianlin Wang & Huanwu Guo, 2023. "Optimizing Multi-Vehicle Demand-Responsive Bus Dispatching: A Real-Time Reservation-Based Approach," Sustainability, MDPI, vol. 15(7), pages 1-18, March.

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