Redesigning large-scale multimodal transit networks with shared autonomous mobility services

Max T.M. Ng, Hani S. Mahmassani*, Ömer Verbas, Taner Cokyasar, Roman Engelhardt

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


This study addresses a large-scale multimodal transit network design problem, with Shared Autonomous Mobility Services (SAMS) as both transit feeders and an origin-to-destination mode. The framework captures spatial demand and modal characteristics, considers intermodal transfers and express services, determines transit infrastructure investment and path flows, and generates transit routes. A system-optimal multimodal transit network is designed with minimum total door-to-door generalized costs of users and operators, satisfying transit origin–destination demand within a pre-set infrastructure budget. Firstly, the geography, demand, and modes in each zone are characterized with continuous approximation. The decisions of network link investment and multimodal path flows in zonal connection optimization are formulated as a minimum-cost multi-commodity network flow (MCNF) problem and solved efficiently with a mixed-integer linear programming (MILP) solver. Subsequently, the route generation problem is solved by expanding the MCNF formulation to minimize intramodal transfers. The model is illustrated through a set of experiments with the Chicago network comprised of 50 zones and seven modes, under three scenarios. The computational results present savings in traveler journey time and operator cost demonstrating the potential benefits of collaboration between multimodal transit systems and SAMS.

Original languageEnglish (US)
Article number104575
JournalTransportation Research Part C: Emerging Technologies
StateAccepted/In press - 2024


  • Autonomous vehicles
  • Multimodal
  • Network optimization
  • Shared Autonomous Mobility Services (SAMS)
  • Transit network design

ASJC Scopus subject areas

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


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