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Track geometry defect rectification based on track deterioration modelling and derailment risk assessment

Author

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  • Qing He

    (University at Buffalo, The State University of New York, Buffalo, NY, USA)

  • Hongfei Li

    (IBM T J Watson Research Center, Yorktown Heights, NY, USA)

  • Debarun Bhattacharjya

    (IBM T J Watson Research Center, Yorktown Heights, NY, USA)

  • Dhaivat P Parikh

    (IBM Global Business Service, Coppell, TX, USA)

  • Arun Hampapur

    (IBM T J Watson Research Center, Yorktown Heights, NY, USA)

Abstract

Analysing track geometry defects is critical for safe and effective railway transportation. Rectifying the appropriate number, types and combinations of geo-defects can effectively reduce the probability of derailments. In this paper, we propose an analytical framework to assist geo-defect rectification decision making. Our major contributions lie in formulating and integrating the following three data-driven models: (1) A track deterioration model to capture the degradation process of different types of geo-defects; (2) A survival model to assess the dynamic derailment risk as a function of track defect and traffic conditions; (3) An optimization model to plan track rectification activities with two different objectives: a cost-based formulation (CF) and a risk-based formulation (RF). We apply these approaches to solve the optimal rectification planning problem for a real-world railway application. We show that the proposed formulations are efficient as well as effective, as compared with existing strategies currently in practice.

Suggested Citation

  • Qing He & Hongfei Li & Debarun Bhattacharjya & Dhaivat P Parikh & Arun Hampapur, 2015. "Track geometry defect rectification based on track deterioration modelling and derailment risk assessment," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(3), pages 392-404, March.
    • Handle: RePEc:pal:jorsoc:v:66:y:2015:i:3:p:392-404
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    Cited by:

    1. Mohammadi, Reza & He, Qing & Karwan, Mark, 2021. "Data-driven robust strategies for joint optimization of rail renewal and maintenance planning," Omega, Elsevier, vol. 103(C).
    2. Cárdenas-Gallo, Iván & Sarmiento, Carlos A. & Morales, Gilberto A. & Bolivar, Manuel A. & Akhavan-Tabatabaei, Raha, 2017. "An ensemble classifier to predict track geometry degradation," Reliability Engineering and System Safety, Elsevier, vol. 161(C), pages 53-60.

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