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NOAH as an Innovative Tool for Modeling the Use of Suburban Railways

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  • Maciej Kruszyna

    (Faculty of Civil Engineering, Wrocław University of Science and Technology (Politechnika Wrocławska), Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland)

Abstract

The paper presents an innovative method called the “Nest of Apes Heuristic” (NOAH) for modeling specific problems by combining technical aspects of transport systems with human decision-making. The method is inspired by nature. At the beginning of the paper, potential problems related to modeling a suburban rail system were presented. The literature review is supplemented with a short description of known heuristics. The basic terminology, procedures, and algorithm are then introduced in detail. The factors of the suburban rail system turn into “Monkeys”. Monkeys change their position in the nest, creating leaders and followers. This allows for the comparison of the factor sets in a real system. The case study area covers the vicinity of Wroclaw, the fourth largest city in Poland. Two experiments were conducted. The first takes into account the average values of the factors in order to observe the algorithm’s work and formulate the stopping criteria. The second is based on the current values of the factors. The purpose of this work was to evaluate these values and to assess the possibilities of changing them. The obtained results show that the new tool may be useful for modeling and analyzing such problems.

Suggested Citation

  • Maciej Kruszyna, 2022. "NOAH as an Innovative Tool for Modeling the Use of Suburban Railways," Sustainability, MDPI, vol. 15(1), pages 1-17, December.
    • Handle: RePEc:gam:jsusta:v:15:y:2022:i:1:p:193-:d:1012021
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    References listed on IDEAS

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    1. Wang, Yihui & D’Ariano, Andrea & Yin, Jiateng & Meng, Lingyun & Tang, Tao & Ning, Bin, 2018. "Passenger demand oriented train scheduling and rolling stock circulation planning for an urban rail transit line," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 193-227.
    2. Yan, Fei & Goverde, Rob M.P., 2019. "Combined line planning and train timetabling for strongly heterogeneous railway lines with direct connections," Transportation Research Part B: Methodological, Elsevier, vol. 127(C), pages 20-46.
    3. Zhang, Yongxiang & Peng, Qiyuan & Yao, Yu & Zhang, Xin & Zhou, Xuesong, 2019. "Solving cyclic train timetabling problem through model reformulation: Extended time-space network construct and Alternating Direction Method of Multipliers methods," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 344-379.
    4. Hoong Chuin Lau & Günther R. Raidl & Pascal Hentenryck, 2016. "New developments in metaheuristics and their applications," Journal of Heuristics, Springer, vol. 22(4), pages 359-363, August.
    5. Adilson Elias Xavier & Vinicius Layter Xavier, 2016. "Flying elephants: a general method for solving non-differentiable problems," Journal of Heuristics, Springer, vol. 22(4), pages 649-664, August.
    6. Wei, Sheng & Zheng, Wei & Wang, Lei, 2021. "Understanding the configuration of bus networks in urban China from the perspective of network types and administrative division effect," Transport Policy, Elsevier, vol. 104(C), pages 1-17.
    7. Hansson, Joel & Pettersson-Löfstedt, Fredrik & Svensson, Helena & Wretstrand, Anders, 2021. "Replacing regional bus services with rail: Changes in rural public transport patronage in and around villages," Transport Policy, Elsevier, vol. 101(C), pages 89-99.
    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. Shen, Weiwei & Xiao, Weizhou & Wang, Xin, 2016. "Passenger satisfaction evaluation model for Urban rail transit: A structural equation modeling based on partial least squares," Transport Policy, Elsevier, vol. 46(C), pages 20-31.
    10. Zhang, Yongxiang & D'Ariano, Andrea & He, Bisheng & Peng, Qiyuan, 2019. "Microscopic optimization model and algorithm for integrating train timetabling and track maintenance task scheduling," Transportation Research Part B: Methodological, Elsevier, vol. 127(C), pages 237-278.
    11. Zhang, Qin & Lusby, Richard Martin & Shang, Pan & Zhu, Xiaoning, 2022. "A heuristic approach to integrate train timetabling, platforming, and railway network maintenance scheduling decisions," Transportation Research Part B: Methodological, Elsevier, vol. 158(C), pages 210-238.
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