Let's Connect
Follow Us
Watch Us
(+385) 99 251 8510
journal.prometfpz.unizg.hr
Traffic&Transportation Journal

Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

Sign In or Register Submit

PDF
PUBLISHED
31.10.2024
DOI
https://doi.org/10.7307/ptt.v36i5.571
ISSUE
2024 (Vol 36), Issue 5
LICENSE
Copyright (c) 2024 Yong CUI, Qing YU, Ullrich MARTIN
Creative Commons 4.0 logo
This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Optimising the Economic Feasibility of High-Speed Maglev Systems: A Simulation-Based Approach for Variable and Parameter Analysis

Authors:Yong CUI, Qing YU, Ullrich MARTIN

Abstract

This study introduces an advanced software platform and process for the quantitative national economic evaluation of high-speed maglev systems, overcoming limitations of traditional methods through parameter variation experiments and automated solution search. Utilising the adapted German standardised evaluation, this research demonstrates how integrated modelling, evaluation and optimisation software can deeply analyse the impact of various variables and parameters on economic outcomes. By employing an optimisation algorithm, the software not only determines critical evaluation parameters to ensure benefits exceed costs but also deduces optimised model variables. The macroeconomic benefit-cost ratio guides the optimal design concept, with the research finding a critical value for ensuring economic feasibility. The proposed solution achieves a 22% improvement in this ratio (1.106 vs. 0.909) compared to the existing Hefei-Wuhu route, highlighting its potential for large-scale maglev implementation. Future development directions include integration with micro-simulation systems, support for random behaviour, sensitivity analysis, data-driven machine learning and enhanced user interface design for broader applicability. The findings underscore the software’s capability to provide robust, data-driven insights for economic feasibility studies of high-speed maglev systems, presenting a significant step forward in infrastructure project evaluation.

Keywords:standardised evaluation, high-speed maglev, modal variable, evaluation parameter, optimisation, economic feasibility, critical value

References

  1. [1] Intraplan, VWI. Standardisierte Bewertung von Verkehrswegeinvestitionen im öffentlichen Personennahverkehr (Version 2016+) - Verfahrensanleitung. Munich, Germany: Intraplan Consult; Stuttgart, Germany: Verkehrswissenschaftliches Institut Stuttgart GmbH; 2022. Developed on behalf of the German Federal Ministry of Digital and Transport as part of the research project FE 70.976/2019.
  2. [2] Rausch C, Janssen T, Kokott J. The Transrapid Munich Airport Link–Operation, Safety and Approval. In: Proceedings of the 18th International Conference on Magnetically Levitated Systems and Linear Drives, Maglev 2004; 2004; Shanghai, China. pp. 649-655.
  3. [3] Cui Y, Martin U. Standardised evaluation of the Hefei-Wuhu-Guangde high-speed maglev train project. ETR International Edition. 2023.
  4. [4] Elhorst JP, Oosterhaven J. Integral cost-benefit analysis of Maglev projects under market imperfections. Journal of Transport and Land Use. 2008;1(1):65–87. DOI: 10.5198/jtlu.v1i1.29.
  5. [5] Guerrieri M, et al. Hyperloop, HeliRail, Transrapid and high-speed rail systems: Technical characteristics and cost-benefit analyses. Research in Transportation Business & Management. 2022;43:100824. DOI:10.1016/j.rtbm.2022.100824.
  6. [6] Naumann R, Schach R, Jehle P. An entire comparison of maglev and high-speed railway systems. In Proceedings of the 19th International Conference on Magnetically Levitated Systems and Linear Drives; September 2006.
  7. [7] Janic M. Multicriteria evaluation of high-speed rail, transrapid maglev and air passenger transport in Europe. Transportation Planning and Technology. 2003;26(6):491-512. DOI: 10.1080/0308106032000167373.
  8. [8] Kim J, Park JS, Jeong DS. A study on the life cycle cost calculation of the Maglev vehicle based on the maintenance information. In Proceedings of the 21st International Conference on Magnetically Levitated Systems and Linear Drives; 2011; Daejeon, Korea.
  9. [9] Van Rhee CG, Pieters M, Van de Voort MP. Real Options applied to infrastructure projects: A new approach to value and manage risk and flexibility. In 2008 First International Conference on Infrastructure Systems and Services: Building Networks for a Brighter Future (INFRA); November 2008; IEEE. pp. 1-6.
  10. [10] Gao Y, Driouchi T. Incorporating Knightian uncertainty into real options analysis: Using multiple-priors in the case of rail transit investment. Transportation Research Part B: Methodological. 2013;55:23-40. DOI: 10.1016/j.trb.2013.04.004.
  11. [11] Huang J, et al. An overview of agent-based models for transport simulation and analysis. Journal of Advanced Transportation. 2022; (1):1–17. DOI: 10.1155/2022/1252534.
  12. Promet – Traffic&Transportation. 2024;36(5):779-796. Transport Engineering
  13. [12] Altiok T, et al. Simulation modeling and analysis with Arena. Amsterdam, Netherlands: Elsevier; 2010.
  14. [13] Banks J. Principles of simulation. In: Handbook of Simulation. Vol 12. 1998. p. 3-30.
  15. [14] Jahangirian M, et al. Simulation in manufacturing and business: A review. European Journal of Operational Research. 2010;203(1):1–13. DOI: 10.1016/j.ejor.2009.06.009.
  16. [15] AnyLogic Development Team. Introduction to digital twin development [Whitepaper]. 2018. Available from: https://www.anylogic.cn/resources/white-papers/an-introduction-to-digital-twin-development/ [Accessed 10th May 2024].
  17. [16] Cui Y, Martin U, Liang J. PULSim: user-based adaptable simulation tool for railway planning and operations. Journal of Advanced Transportation. 2018; 1–11. DOI: 10.1155/2018/5375136.
  18. [17] Shanghai Maglev Engineering Technology Research Center. The report of feasibility study on Shanghai-Hangzhou Maglev Inter-city Project. 2007.
  19. [18] National Development and Reform Commission. Reply to the feasibility study report of the new Shangqiu-Hefei-Hangzhou Railway. Beijing, China; 2015.
  20. [19] Passy P, Théry S. The use of SAGA GIS modules in QGIS. In: QGIS and Generic Tools. 2018. p. 107–149.
  21. [20] Laguna M. Optimisation of complex systems with OptQuest [White Paper]. Boulder, CO: OptTek Systems, Inc.; 1997. p. 1–13.
Show more

SPECIAL ISSUE CALL

Innovation and New Technologies in Transport and Logistics

Guest Editor: Eleonora Papadimitriou, PhD

Editors: Dario Babić, PhD; Marko Matulin, PhD; Marko Ševrović, PhD.

RECENT PAPERS

Browse through the selection of our recent research
Optimising the Economic Feasibility of High-Speed Maglev Systems: A Simulation-Based Approach for Variable and Parameter AnalysisBibliometric Analysis of Traffic Accident Prediction Studies from 2003 to 2023: Trends, Patterns and Future DirectionsResearch on the Efficiency of Major Airports in China’s Six Major Airport Cluster: Based on Common Frontier Super-Efficiency DEA and Malmquist MethodFactors Influencing Sustained Use of Shared E-Scooter Services in Urban Taiwan

Accelerating Discoveries in Traffic Science |
2024 © Promet - Traffic&Transportation journal