Adaptive Park-and-ride Choice on Time-dependent Stochastic Multimodal Transportation Network

Pramesh Kumar, Alireza Khani

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

In transportation networks with stochastic and dynamic travel times, park-and-ride decisions are often made adaptively considering the realized state of traffic. That is, users continue driving towards their destination if the congestion level is low, but may consider taking transit when the congestion level is high. This adaptive behavior determines whether and where people park-and-ride. We propose to use a Markov decision process to model the problem of commuters’ adaptive park-and-ride choice behavior in a transportation network with time-dependent and stochastic link travel times. The model evaluates a routing policy by minimizing the expected cost of travel that leverages the online information about the travel time on outgoing links in making park-and-ride decisions. We provide a case study of park-and-ride facilities located on freeway I-394 in Twin Cities, Minnesota. The results show a significant improvement in the travel time by the use of park-and-ride during congested conditions. It also reveals the time of departure, the state of the traffic, and the location from where park-and-ride becomes an attractive option to the commuters. Finally, we show the benefit of using online routing in comparison to an offline routing algorithm.

Original languageEnglish (US)
Pages (from-to)771-800
Number of pages30
JournalNetworks and Spatial Economics
Volume21
Issue number4
DOIs
StatePublished - Jul 17 2021

Bibliographical note

Funding Information:
- National Science Foundation, award CMMI-1831140

Funding Information:
? This research is conducted at the University of Minnesota Transit Lab, currently supported by the following, but not limited to, projects: - National Science Foundation, award CMMI-1831140 - Freight Mobility Research Institute (FMRI), Tier 1 Transportation Center, U.S. Department of Transportation: award RR-K78/FAU SP#16-532 AM2 and AM3 -Minnesota Department of Transportation, Contract No. 1003325 Work Order No. 44 and 111 - Minnesota Department of Transportation, Agreement No. 1044277. ? The authors are grateful to the Minnesota Department of Transportation and Metro Transit for sharing the data. Any limitation of this study remains the responsibility of the authors. ? The authors are grateful to anonymous TRB and NETS reviewers for their wonderful insights.

Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Keywords

  • Adaptive route choice
  • Online shortest path
  • Park-and-ride
  • Recourse
  • Stochastic shortest path
  • Transit

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