Abstract
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.
Similar content being viewed by others
References
Bonami P, Lee J (2007) Bonmin user’s manual. Numer Math 4:1–32
Cao Z, Ceder A, Li D, Zhang S (2020) Robust and optimized urban rail timetabling using a marshaling plan and skip-stop operation. Transportmetrica A Transp Sci 16(3):1217–1249
Chen J, Liu Z, Zhu S, Wang W (2015) Design of limited-stop bus service with capacity constraint and stochastic travel time. Transp Res Part E Logist Transp Rev 83:1–15
Chiraphadhanakul V, Barnhart C (2013) Incremental bus service design: combining limited-stop and local bus services. Public Transp 5(1):53–78
Eberlein XJ (1995) Real-time control strategies in transit operations: models and analysis. PhD thesis. Department of Civil and Environmental Engineering, Massachusetts Institute of Technology
Eberlein XJ, Wilson NHM, Bernstein D (1999) Modeling real-time control strategies in public transit operations. Computer-aided transit scheduling. Springer, Berlin, pp 325–346
Furth PG (1986) Zonal route design for transit corridors. Transp Sci 20(1):1–12
Hart N (2016) Methodology for evaluating potential for limited-stop bus service along existing local bus corridors. Transp Res Rec J Transp Res Board 2543(1):91–100
Jordan WC, Turnquist MA (1979) Zone scheduling of bus routes to improve service reliability. Transp Sci 13(3):242–268
Kaeoruean K, Phithakkitnukoon S, Demissie MG, Kattan L, Ratti C (2020) Analysis of demand-supply gaps in public transit systems based on census and GTFS data: a case study of Calgary, Canada. Public Transp 12(3):483–516
Larrain H, Muñoz JC (2016) When and where are limited-stop bus services justified? Transportmetrica A Transp Sci 12(9):811–831
Leiva C, Muñoz JC, Giesen R, Larrain H (2010) Design of limited-stop services for an urban bus corridor with capacity constraints. Transp Res B Methodol 44(10):1186–1201
Nesheli MM, Ceder AA (2015) A robust, tactic-based, real-time framework for public-transport transfer synchronization. Transp Res C Emerg Technol 60:105–123
Nesheli MM, Ceder AA (2017) Real-time public transport operations: library of control strategies. Transp Res Rec J Transp Res Board 2647(1):26–32
Rashedi Z, Hasnine MS, Habib KN (2021) Modelling second-best choices from the choice-based sample: revelation of potential mode-switching behaviour from transit passenger surveys. Public Transp. https://doi.org/10.1007/s12469-021-00275-z
Reed TB (1995) Reduction in the burden of waiting for public transit due to real-time schedule information: a conjoint analysis study. In: Vehicle navigation and information systems conference, 1995. proceedings. In conjunction with the Pacific Rim TransTech Conference. 6th International VNIS.’A Ride into the Future’, IEEE, pp 83–89
Soto G, Larrain H, Muñoz JC (2017) A new solution framework for the limited-stop bus service design problem. Transp Res B Methodol 105:67–85
Srikukenthiran S, Shalaby A (2017) Enabling large-scale transit microsimulation for disruption response support using the Nexus platform. Public Transp 9(1):411–435
Sun A, Hickman M (2005) The real-time stop-skipping problem. J Intell Transp Syst 9(2):91–109
Tétreault PR, El-Geneidy AM (2010) Estimating bus run times for new limited-stop service using archived AVL and APC data. Transp Res A Policy Pract 44(6):390–402
Ulusoy Y, Chien S, Wei CH (2010) Optimal all-stop, short-turn, and express transit services under heterogeneous demand. Transp Res Rec J Transp Res Board 2197:8–18
Vuchic VR (2005) Urban transit: operations, planning, and economics. Wiley, New York
Wang DZ, Nayan A, Szeto W (2018) Optimal bus service design with limited stop services in a travel corridor. Transp Res E Logist Transp Rev 111:70–86
Wen B, Srikukenthiran S, Shalaby A (2018) Data-driven mesoscopic simulation models of large-scale surface transit networks. Transportation Research Board 97th Annual Meeting Transportation Research Board
Wirasinghe S, Vandebona U (2011) Route layout analysis for express buses. Transp Res C Emerg Technol 19(2):374–385
Acknowledgements
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.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Nesheli, M.M., Srikukenthiran, S. & Shalaby, A. An optimization model for planning limited-stop transit operations. Public Transp 14, 63–83 (2022). https://doi.org/10.1007/s12469-022-00293-5
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12469-022-00293-5