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Multiobjective Railway Alignment Optimization Using Ballastless Track and Reduced Cross-Section in Tunnel

Author

Listed:
  • Salvatore Antonio Biancardo

    (Department of Civil, Construction and Environmental Engineering, Federico II University of Naples, 80125 Naples, Italy)

  • Francesco Avella

    (Department of Civil, Construction and Environmental Engineering, Federico II University of Naples, 80125 Naples, Italy)

  • Ernesto Di Lisa

    (Department of Civil, Construction and Environmental Engineering, Federico II University of Naples, 80125 Naples, Italy)

  • Xinqiang Chen

    (Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China)

  • Francesco Abbondati

    (Department of Engineering, Parthenope University of Naples, 80133 Naples, Italy)

  • Gianluca Dell’Acqua

    (Department of Civil, Construction and Environmental Engineering, Federico II University of Naples, 80125 Naples, Italy)

Abstract

The increasing need for railway planning and design to connect growing cities in inland mountainous areas has pushed engineering efforts toward the research of railway tracks that must comply with more restrictive constraints. In this study, a multiobjective alignment optimization (HAO), commonly used for highway projects, was carried out to identify a better solution for constructing a high-speed railway track considering technical and economic feasibilities. Then, two different and innovative scenarios were investigated: an unconventional ballastless superstructure, which is more environment-friendly than a gravel superstructure, and a reduced cross-section in a tunnel, which enables a slower design speed and then, less restrictive geometric constraints and earthmoving. The results showed that the first solution obtained a better performance with a slight increase in cost. Moreover, both scenarios improved the preliminary alignment optimization, reducing the overall cost by 11% for the first scenario and 20% for the second one.

Suggested Citation

  • Salvatore Antonio Biancardo & Francesco Avella & Ernesto Di Lisa & Xinqiang Chen & Francesco Abbondati & Gianluca Dell’Acqua, 2021. "Multiobjective Railway Alignment Optimization Using Ballastless Track and Reduced Cross-Section in Tunnel," Sustainability, MDPI, vol. 13(19), pages 1-19, September.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:19:p:10672-:d:643295
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    References listed on IDEAS

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    1. Pushak, Yasha & Hare, Warren & Lucet, Yves, 2016. "Multiple-path selection for new highway alignments using discrete algorithms," European Journal of Operational Research, Elsevier, vol. 248(2), pages 415-427.
    2. Lin, Dung-Ying & Ku, Yu-Hsiung, 2014. "An implicit enumeration algorithm for the passenger service planning problem: Application to the Taiwan Railways Administration line," European Journal of Operational Research, Elsevier, vol. 238(3), pages 863-875.
    3. Kim, Eungcheol & Jha, Manoj K. & Son, Bongsoo, 2005. "Improving the computational efficiency of highway alignment optimization models through a stepwise genetic algorithms approach," Transportation Research Part B: Methodological, Elsevier, vol. 39(4), pages 339-360, May.
    4. David J. Thompson & Eduardo Latorre Iglesias & Xiaowan Liu & Jianyue Zhu & Zhiwei Hu, 2015. "Recent developments in the prediction and control of aerodynamic noise from high-speed trains," International Journal of Rail Transportation, Taylor & Francis Journals, vol. 3(3), pages 119-150, August.
    5. Vansteenwegen, Pieter & Dewilde, Thijs & Burggraeve, Sofie & Cattrysse, Dirk, 2016. "An iterative approach for reducing the impact of infrastructure maintenance on the performance of railway systems," European Journal of Operational Research, Elsevier, vol. 252(1), pages 39-53.
    6. Jong, Jyh-Cherng & Schonfeld, Paul, 2003. "An evolutionary model for simultaneously optimizing three-dimensional highway alignments," Transportation Research Part B: Methodological, Elsevier, vol. 37(2), pages 107-128, February.
    7. Chen, Xinqiang & Chen, Huixing & Yang, Yongsheng & Wu, Huafeng & Zhang, Wenhui & Zhao, Jiansen & Xiong, Yong, 2021. "Traffic flow prediction by an ensemble framework with data denoising and deep learning model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    8. Bob Hickish & David I. Fletcher & Robert F. Harrison, 2020. "Investigating Bayesian Optimization for rail network optimization," International Journal of Rail Transportation, Taylor & Francis Journals, vol. 8(4), pages 307-323, October.
    9. Annemieke Meghoe & Richard Loendersloot & Tiedo Tinga, 2020. "Rail wear and remaining life prediction using meta-models," International Journal of Rail Transportation, Taylor & Francis Journals, vol. 8(1), pages 1-26, January.
    10. Hare, Warren & Lucet, Yves & Rahman, Faisal, 2015. "A mixed-integer linear programming model to optimize the vertical alignment considering blocks and side-slopes in road construction," European Journal of Operational Research, Elsevier, vol. 241(3), pages 631-641.
    11. Shi, Yue & Xiang, Yisha & Xiao, Hui & Xing, Liudong, 2021. "Joint optimization of budget allocation and maintenance planning of multi-facility transportation infrastructure systems," European Journal of Operational Research, Elsevier, vol. 288(2), pages 382-393.
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    1. Ali Alqatawna & Santos Sánchez-Cambronero & Inmaculada Gallego & Juan Miguel López-Morales, 2022. "A Graphical Method for Designing the Horizontal Alignment and the Cant in High-Speed Railway Lines Aimed at Mixed-Speed Traffic," Sustainability, MDPI, vol. 14(14), pages 1-27, July.
    2. Yong Fang & Jiayi Zhou & Hua Hu & Yanxi Hao & Dianliang Xiao & Shaojie Li, 2022. "Combination Layout of Traffic Signs and Markings of Expressway Tunnel Entrance Sections: A Driving Simulator Study," Sustainability, MDPI, vol. 14(6), pages 1-13, March.

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