Public Transit Fleet Size Models of Single-Line Autonomous Modular System

The advancements of technologies of autonomous and connected vehicle, modular vehicle, and of information and communication open the door to develop new and innovative public transit (PT) systems to reduce operational cost, and better match between passenger demand and service provided. This work analyses such a new system comprised of autonomous modular PT (AMPT) vehicles. In this analysis, one of the most challenging tasks is to accurately estimate the minimum number of vehicle modules, i.e., its minimum fleet size (MFS), required to perform a set of scheduled services. The solution of the MFS problem of a single-line AMPT system is based on the deficit function (DF) theory. The traditional DF model has been extended to accommodate the definitions of an AMPT system. Some numerical examples are provided to illustrate the mathematical formulations, theorems and proofs developed. The limitations of traditional continuum approximation models and the equivalence between the extended DF model and an integer programming model are also provided. The elaborated methodology was applied, as a case study, to an AMPT system, called dynamic autonomous road transport system in Singapore. The results show that the extended DF model is effective in solving the MFS problem and have the potential to be applied to solving real-life MFS problems of large-scale, multi-line and multi-terminal AMPT systems.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee AP040 Standing Committee on Automated Transit Systems.
  • Corporate Authors:

    Transportation Research Board

    ,    
  • Authors:
    • Liu, Tao
    • Ceder, Avishai (Avi)
    • Rau, Andreas
  • Conference:
  • Date: 2019

Language

  • English

Media Info

  • Media Type: Digital/other
  • Pagination: 2p

Subject/Index Terms

Filing Info

  • Accession Number: 01697487
  • Record Type: Publication
  • Report/Paper Numbers: 19-01575
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 7 2018 9:29AM