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Optimizing the number and location of time point stops

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Abstract

Public transport service is subject to multiple sources of uncertainty that impact its reliability. Holding control strategies are a common method to prevent the deterioration of service reliability along the route. This paper expands on previous studies by considering the general case of determining both the optimal number and optimal location of the time point stops (TPS) where holding takes place, and assessing their impacts on transit performance using simulation. Holding times are determined based on a real-time headway-based holding strategy designed to improve service regularity by seeking uniform headways along the route. The evaluation of the performance of alternative TPS layouts is simulation-based, using BusMezzo, a transit operations simulation model which models the dynamic performance of bus transit systems. The proposed framework also considers the multiple objectives incorporating passenger and operator points of view. The simulation-based optimization framework was applied in a case study with one of the premium bus lines in Stockholm, Sweden, using two solution methods—greedy and genetic algorithms. A multi-objective evaluation was conducted considering both passenger and operator perspectives. The results demonstrate that transit performance varies considerably with alternative TPS layouts. The best solution obtained by the proposed methodology reduces total weighted passenger journey times and cycle times compared to both the current layout and the case of no holding control. The proposed method could assist transit agencies and operators in evaluating and recommending alternative time point layouts.

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References

  • Abkowitz M, Engelstein I (1984) Methods for maintaining transit service regularity. Transp Res Rec 961:1–8

    Google Scholar 

  • Cats O, Larijani AN, Burghout W, Koutsopoulos HN (2011) Impacts of holding control strategies on transit performance: a bus simulation model analysis. Transp Res Rec 2216:51–58

    Article  Google Scholar 

  • Cats O, Larijani AN, Ólafsdóttir A, Burghout W, Andreasson I, Koutsopoulos HN (2012) Holding control strategies: a simulation-based evaluation and guidelines for implementation. Transp Res Rec 2274:100–108

    Article  Google Scholar 

  • Cortes CE, Saez D, Milla F, Nunez A, Riquelme M (2010) Hybrid predictive control for real-time optimization of public transport systems’ operations based on evolutionary multi-objective optimization. Transp Res Part C 18:757–769

    Article  Google Scholar 

  • Delgado F, Munoz JC, Giesen R, Cipriano A (2009) Real-time control of buses in a transit corridor based on vehicle holding and boarding limits. Transp Res Rec 2090:59–67

    Article  Google Scholar 

  • Eberlein XJ, Wilson NHM, Bernstein D (2001) The holding problem with real-time information available. Transp Sci 35(1):1–18

    Article  Google Scholar 

  • Furth PG, Miller THJ (2009) Optimality conditions for public transport schedules with timepoint holding. Public Transp 1:87–102

    Article  Google Scholar 

  • Grube P, Cipriano A (2010) Comparison of simple and model predictive control strategies for the holding problem in a metro train system. IET Intel Transp Syst 4(2):161–175

    Article  Google Scholar 

  • Hickman MD (2001) An analytical stochastic model for the transit vehicle holding problem. Transp Sci 35(3):215–237

    Article  Google Scholar 

  • Holland JH (1992) Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control and artificial intelligence. MIT press

  • Konak A, Coit DW, Smith AC (2006) Multi-objective optimization using genetic algorithms: a tutorial. Reliab Eng Syst Saf 91(9):992–1007. doi:10.1016/j.ress.2005.11.018

    Article  Google Scholar 

  • Liu G, Wirasinghe SC (2001) A simulation model of reliable schedule design for a fixed transit route. J Adv Transp 35(2):145–174

    Article  Google Scholar 

  • Mazloumi E, Mesbah M, Ceder A, Moridpour S, Currie G (2012) Efficient transit schedule design of timing points: a comparison of ant colony and genetic algorithms. Transp Res Part B 46:217–234

    Article  Google Scholar 

  • Rossetti MD, Turitto T (1998) Comparing static and dynamic threshold based control strategies. Transp Res Part A 32(8):607–620

    Google Scholar 

  • Seneviratne PN (1990) Analysis of on-time performance of bus services using simulation. J Transp Eng 116:517–531

    Article  Google Scholar 

  • Sun A, Hickman M (2008) The holding problem at multiple holding stations. Computer-aided Systems in Public Transport. Lect Notes Econ Math Sys 600(3):339–359

    Article  Google Scholar 

  • TCRP (2000) Data analysis for bus planning and monitoring. Transportation Research Board, Synthesis of transit practice 34, Washington, DC

  • TCRP (2003) Transit capacity and quality of service manual (TCQSM) 2nd edition. Transportation Research Board, TCRP Report 100, Washington, DC. http://onlinepubs.trb.org/onlinepubs/tcrp/tcrp100/part%203.pdf

  • Toledo T, Cats O, Burghout W, Koutsopoulos HN (2010) Mesoscopic simulation for transit operations. Transp Res Part C Emerg Technol 18(6):896–908

    Article  Google Scholar 

  • Turnquist MA, Blume SW (1980) Evaluating potential effectiveness of headway control strategies for transit systems. Transp Res Rec 746:25–29

    Google Scholar 

  • Yu B, Yang Z (2009) A dynamic holding strategy in public transport systems with real-time information. Appl Intell 31:69–80

    Article  Google Scholar 

Download references

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Correspondence to Oded Cats.

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Cats, O., Rufi, F.M. & Koutsopoulos, H.N. Optimizing the number and location of time point stops. Public Transp 6, 215–235 (2014). https://doi.org/10.1007/s12469-014-0092-1

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  • DOI: https://doi.org/10.1007/s12469-014-0092-1

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