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Social distancing in public transport: mobilising new technologies for demand management under the Covid-19 crisis.
Hörcher, Daniel; Singh, Ramandeep; Graham, Daniel J.
  • Hörcher D; Transport Strategy Centre, Department of Civil and Environmental Engineering, Imperial College London, London, UK.
  • Singh R; Transport Strategy Centre, Department of Civil and Environmental Engineering, Imperial College London, London, UK.
  • Graham DJ; Transport Strategy Centre, Department of Civil and Environmental Engineering, Imperial College London, London, UK.
Transportation (Amst) ; 49(2): 735-764, 2022.
Article in English | MEDLINE | ID: covidwho-1202804
ABSTRACT
Dense urban areas are especially hardly hit by the Covid-19 crisis due to the limited availability of public transport, one of the most efficient means of mass mobility. In light of the Covid-19 pandemic, public transport operators are experiencing steep declines in demand and fare revenues due to the perceived risk of infection within vehicles and other facilities. The purpose of this paper is to explore the possibilities of implementing social distancing in public transport in line with epidemiological advice. Social distancing requires effective demand management to keep vehicle occupancy rates under a predefined threshold, both spatially and temporally. We review the literature of five demand management methods enabled by new information and ticketing technologies (i) inflow control with queueing, (ii) time and space dependent pricing, (iii) capacity reservation with advance booking, (iv) slot auctioning, and (v) tradeable travel permit schemes. Thus the paper collects the relevant literature into a single point of reference, and provides interpretation from the viewpoint of practical applicability during and after the pandemic.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Transportation (Amst) Year: 2022 Document Type: Article Affiliation country: S11116-021-10192-6

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Transportation (Amst) Year: 2022 Document Type: Article Affiliation country: S11116-021-10192-6