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Modeling a rail transit alignment considering different objectives

Author

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  • Samanta, Sutapa
  • Jha, Manoj K.

Abstract

An optimization model for station locations for an on-ground rail transit line is developed using different objective functions of demand and cost as both influence the planning of a rail transit alignment. A microscopic analysis is performed to develop a rail transit alignment in a given corridor considering a many-to-one travel demand pattern. A variable demand case is considered as it replicates a realistic scenario for planning a rail transit line. A Genetic Algorithm (GA) based on a Geographical Information System (GIS) database is developed to optimize the station locations for a rail transit alignment. The first objective is to minimize the total system cost per person, which is a function of user cost, operator cost, and location cost. The second objective is to maximize the ridership or the service coverage of the rail transit alignment. The user cost per person is minimized separately as the third objective because the user cost is one of the most important decision-making factors for planning a transit system from the users' perspective. A transit planner can make an informed decision between various alternatives based on the results obtained using different objective functions. The model is applied in a case study in the Washington, DC area. The optimal locations and sequence of stations obtained using the three objective functions are presented and a comparative study between the results obtained is shown in the paper. In future works we will develop a combinatorial optimization problem using the aforementioned objectives for the rail transit alignment planning and design problem.

Suggested Citation

  • Samanta, Sutapa & Jha, Manoj K., 2011. "Modeling a rail transit alignment considering different objectives," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(1), pages 31-45, January.
  • Handle: RePEc:eee:transa:v:45:y:2011:i:1:p:31-45
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    References listed on IDEAS

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    1. Zhao, Fang & Zeng, Xiaogang, 2008. "Optimization of transit route network, vehicle headways and timetables for large-scale transit networks," European Journal of Operational Research, Elsevier, vol. 186(2), pages 841-855, April.
    2. Kuby, Michael & Barranda, Anthony & Upchurch, Christopher, 2004. "Factors influencing light-rail station boardings in the United States," Transportation Research Part A: Policy and Practice, Elsevier, vol. 38(3), pages 223-247, March.
    3. Bruno, Giuseppe & Ghiani, Gianpaolo & Improta, Gennaro, 1998. "A multi-modal approach to the location of a rapid transit line," European Journal of Operational Research, Elsevier, vol. 104(2), pages 321-332, January.
    4. Steven I. Chien * & Zhaoqiong Qin, 2004. "Optimization of bus stop locations for improving transit accessibility," Transportation Planning and Technology, Taylor & Francis Journals, vol. 27(3), pages 211-227, June.
    5. Mark Horner & Tony Grubesic, 2001. "A GIS-based planning approach to locating urban rail terminals," Transportation, Springer, vol. 28(1), pages 55-77, February.
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    Cited by:

    1. Xu, Wangtu & Lin, Weihua, 2016. "Selecting the public transit projects with PCA-DP technique: The example of Xiamen City," Transport Policy, Elsevier, vol. 46(C), pages 56-71.
    2. An, Kun & Lo, Hong K., 2016. "Two-phase stochastic program for transit network design under demand uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 84(C), pages 157-181.
    3. An, Kun & Lo, Hong K., 2015. "Robust transit network design with stochastic demand considering development density," Transportation Research Part B: Methodological, Elsevier, vol. 81(P3), pages 737-754.
    4. Aydin, Nezir & Celik, Erkan & Gumus, Alev Taskin, 2015. "A hierarchical customer satisfaction framework for evaluating rail transit systems of Istanbul," Transportation Research Part A: Policy and Practice, Elsevier, vol. 77(C), pages 61-81.

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