An Autonomous Modular Public Transit service

Xi Cheng, Yu (Marco) Nie, Jane Lin*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

In this work, we present a proof-of-concept investigation of Autonomous Modular Public Transit (AMPT) at a network scale and compare it with the traditional fixed-route, fixed-vehicle size transit service in terms of total cost, which consists of both agency's capital and operational cost (including energy cost) and passenger time cost. We formulate and solve stylized design models for AMPT on a grid network in a range of demand density scenarios with both homogenous and heterogeneous distributions. The AMPT models explicitly account for pod joining and disjoining (and therefore en-route transfers of passengers) and potential energy savings due to pod train formation (pod platooning), which represent major departures from the traditional transit models in the literature. Numerical results find that AMPT, if designed properly, may save the total cost compared to traditional transit systems thanks to demand responsive pod train capacity, particularly in the low demand scenarios. The cost savings of AMPT are largely attributed to passenger time saving by en-route transfer; the agency cost of AMPT has a mixed picture. The load factor of AMPT generally improves over the traditional transit service. We also show how key parameter values may affect the AMPT costs through sensitivity analysis.

Original languageEnglish (US)
Article number104746
JournalTransportation Research Part C: Emerging Technologies
DOIs
StateAccepted/In press - 2024

Funding

This research is funded by the U.S. National Science Foundation ( award # CMMI 2127677 ).

Keywords

  • Agency and passenger costs
  • Autonomous Modular Public Transit (AMPT)
  • Autonomous Modular Vehicle Technology (AMVT)
  • Gridded Fixed-route Transit Network
  • Joining/Disjoining of Pods
  • Pod

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Automotive Engineering
  • Transportation
  • Management Science and Operations Research

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