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An optimization model for planning limited-stop transit operations


Surface transit lines in North America commonly feature a basic service pattern consisting of a single branch of all-stop service, with stops usually tightly spaced. Such a configuration is inefficient for the operator and unattractive for the users, particularly if the prevailing passenger demand is unevenly distributed along the line. In such cases, it is more effective to tailor the scheduled services to passenger demand, both spatially and temporally. Public Transit agencies have increasingly adopted various stop and service pattern strategies in order to provide high-quality services while reducing operating costs. This study is focused on one such strategy, namely limited-stop operation. It proposes a new mathematical programming model to find the best candidate route stops for this strategy to minimize the total passenger travel time. The adopted approach consists of three steps: optimization, post-optimization, and simulation. An agent-based simulation platform, called Nexus, is used to represent real-life operating conditions, generate input data for the optimization model, enable post-optimization pattern recognition for grouping trips, and finally help assess the optimization results and present a best possible strategy. The developed approach is tested in a case study of a transit system in Hamilton, Ontario, Canada. Multiple analysis and algorithm test cases are demonstrated.

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This research was supported by NSERC, OCE, Trapeze and SOSCIP. We also gratefully acknowledge the data support from the Trapeze group and the Hamilton Street Railway service.

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Correspondence to Mahmood Mahmoodi Nesheli.

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Nesheli, M.M., Srikukenthiran, S. & Shalaby, A. An optimization model for planning limited-stop transit operations. Public Transp 14, 63–83 (2022).

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