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Multilevel modelling of Demand Responsive Transport (DRT) trips in Greater Manchester based on area-wide socio-economic data

Author

Listed:
  • Chao Wang

  • Mohammed Quddus
  • Marcus Enoch
  • Tim Ryley
  • Lisa Davison

Abstract

Providing public transport in areas of low demand has long proved to be a challenge to policy makers and practitioners. With the developing economic, social and environmental trends, there is pressure for alternative solutions to the policy of subsidising conventional bus services. One potential solution is to adopt more flexible routes and/or timetables to better match the required demand. Therefore such ‘on demand’ or ‘Demand Responsive Transport’ (DRT) services (known as paratransit in the US) have been adopted in a number of locations. This paper seeks to explore the effects of area-wide factors on the demand of DRT by reporting the results of a statistical analysis of DRT service provision in the metropolitan region of Greater Manchester, the public transport authority of which offers one of the largest and most diverse range of DRT schemes in the UK. Specifically, this paper employs a multilevel modelling approach to investigate the impact of both DRT supply-oriented factors at the service area level and socio-economic factors at the lower super output area (LSOA) level on the average number of trips made by DRT per year. This hierarchical or ‘nested’ structure was adopted because typically the LSOAs within the same Service Area may share similar characteristics. It is found that the demand for DRT services was higher in areas with low car ownership, low population density, high proportion of white people, and high levels of social deprivation, measured in terms of income, employment, education, housing and services, health and disability, and living environment. Copyright The Author(s) 2014

Suggested Citation

  • Chao Wang & Mohammed Quddus & Marcus Enoch & Tim Ryley & Lisa Davison, 2014. "Multilevel modelling of Demand Responsive Transport (DRT) trips in Greater Manchester based on area-wide socio-economic data," Transportation, Springer, vol. 41(3), pages 589-610, May.
    • Handle: RePEc:kap:transp:v:41:y:2014:i:3:p:589-610
    DOI: 10.1007/s11116-013-9506-1
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    References listed on IDEAS

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