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An Integrated Decision Support System to Assess the Sustainability of Demand Responsive Transport Systems

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Operations Research and Big Data

Part of the book series: Studies in Big Data ((SBD,volume 15))

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Abstract

The provision of conventional public passenger transport services in rural areas have shown to be very inefficient and ineffective due to low levels of population density and high spatial and temporal dispersion. Demand Responsive Transport (DRT) systems have been seen as an interesting alternative solution, adopted in several countries as a way to increase user’s mobility, providing flexible transport services to meet trip requests and mitigate social exclusion. However, some DRT systems have revealed to be inadequate or even unsustainable, mainly because they are highly dependent on the correct tuning of organizational and functional parameters, namely the level of flexibility of schedules, routes and stop locations. Despite the existence of a vast literature concerning DRT systems, very few contributions have been put forward concerning comprehensive approaches to tackle these problems and to assess sustainability. This paper proposes a new integrated multi-disciplinary decision support system (DSS) to help decision-makers design and plan DRT systems, and assess their sustainability. The proposed DSS comprises analytical and simulation tools and consists of a procedure that iteratively simulates and evaluates alternative specifications of the system until an adequate and sustainable solution is encountered. The new tool has been applied to a case study in a rural area of the northwest of Portugal, and preliminary results suggest that the proposed approach was able to identify a set of conditions under which a DRT system can be sustainable.

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Correspondence to Ana Cecília Ribeiro .

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Ribeiro, A.C., Carvalho, M.S., Telhada, J. (2015). An Integrated Decision Support System to Assess the Sustainability of Demand Responsive Transport Systems. In: Póvoa, A., de Miranda, J. (eds) Operations Research and Big Data. Studies in Big Data, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-319-24154-8_22

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  • DOI: https://doi.org/10.1007/978-3-319-24154-8_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24152-4

  • Online ISBN: 978-3-319-24154-8

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