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Electrical Engineering and Systems Science > Systems and Control

Title: Redesigning Large-Scale Multimodal Transit Networks with Shared Autonomous Mobility Services

Abstract: 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.
Comments: 48 pages, 18 figures, accepted for publication in Transportation Research Part C: Emerging Technologies, and presentation in the 25th International Symposium on Transportation and Traffic Theory (ISTTT25)
Subjects: Systems and Control (eess.SY); Optimization and Control (math.OC)
DOI: 10.1016/j.trc.2024.104575
Cite as: arXiv:2307.16075 [eess.SY]
  (or arXiv:2307.16075v4 [eess.SY] for this version)

Submission history

From: Max Ng [view email]
[v1] Sat, 29 Jul 2023 20:57:34 GMT (9218kb)
[v2] Sun, 20 Aug 2023 19:21:45 GMT (9211kb)
[v3] Tue, 16 Jan 2024 10:42:45 GMT (11066kb)
[v4] Wed, 27 Mar 2024 14:29:24 GMT (11066kb)

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