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Hafif Raylı Taşıma Sistemlerinde Sefer Aralıklarının Optimizasyonu için Mikrosimülasyon Yöntemi Uygulaması

Year 2025, Volume: 14 Issue: 2, 838 - 858, 30.06.2025
https://doi.org/10.17798/bitlisfen.1602799

Abstract

Şehirler son dönemdeki kentleşme ve nüfus artışı nedeniyle, yolcu taşıma sistemi önemli oranda tıkanıklık ve gecikme sorunlarıyla karşı karşıya kalmaktadır. Özellikle artan yolcu trafiği ile başa çıkmak için, demiryolu toplu taşıma sistemleri zirve saatlerde daha sık seferler düzenlemekte ve boş saatler için daha seyrek tren geliş aralıkları planlanarak sistem kullanımının artırılması hedeflenmektedir. Bu çalışma, değişken yolcu trafiğine sahip bir metro hattının seferlerinin optimizasyonu için bir simülasyon metodolojisi önermektedir. Deney ortamını oluşturmak için Arena yazılımı kullanılarak bir mikrosimülasyon modeli oluşturulmuştur. Model oluşturma sürecinde ayrık olay simülasyonu kavramından yararlanılmıştır. Yolcu trafiği ve tren sefer aralıkları , yolcu başlangıç-varış istasyon matrisi verilerinden elde edilen ana girdi parametreleri kullanılmıştır. Yapay arı kolonisi algoritması kullanılarak bir optimizasyon modülü model ile işletimiştir. Optimizasyon çalışması, sıfır başarısız biniş politikası göz önünde bulundurularak normal ve artırılmış kapasite senaryoları için günlük tren seferlerinin toplam sayısında sırasıyla %27,5 ve %25,5 azalma ile sonuçlanmıştır. Çalışmanın sonuçları literatür bulguları ile karşılaştırılmış ve bir tartışma sunulmuştur.

References

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  • P. Potti, M. Marinov, and E. Sweeney, “A Simulation Study on the Potential of Moving Urban Freight by a Cross-City Railway Line,” Sustainability, vol. 11, no. 21, pp. 6088–6098, Nov. 2019, doi: 10.3390/su11216088.
  • M. S. Yıldırım, M. Karaşahin, and Ü. Gökkuş, “Scheduling of the Shuttle Freight Train Services for Dry Ports Using Multimethod Simulation–Optimization Approach,” International Journal of Civil Engineering, vol. 19, no. 1, pp. 67–83, Jan. 2021, doi: 10.1007/s40999-020-00553-0.
  • M. S. Yıldırım, “A Management System for Autonomous Shuttle Freight Train Service in Shared Railway Corridors,” International Journal of Civil Engineering, vol. 20, no. 3, pp. 273–290, Mar. 2022, doi: 10.1007/s40999-021-00663-3.
  • L. Zhang, M. Liu, X. Wu, and S. M. AbouRizk, “Simulation-based route planning for pedestrian evacuation in metro stations: A case study,” Autom Constr, vol. 71, pp. 430–442, Nov. 2016, doi: 10.1016/j.autcon.2016.08.031.
  • P. K. Kwok, M. Yan, B. K. P. Chan, and H. Y. K. Lau, “Crisis management training using discrete-event simulation and virtual reality techniques,” Comput Ind Eng, vol. 135, pp. 711–722, Sep. 2019, doi: 10.1016/j.cie.2019.06.035.
  • Ö. Yalçinkaya and G. Mirac Bayhan, “A feasible timetable generator simulation modelling framework for train scheduling problem,” Simul Model Pract Theory, vol. 20, no. 1, pp. 124–141, Jan. 2012, doi: 10.1016/J.SIMPAT.2011.09.005.
  • E. Tischer, P. Nachtigall, and J. Široký, “The use of simulation modelling for determining the capacity of railway lines in the Czech conditions,” Open Engineering, vol. 10, no. 1, pp. 224–231, Jan. 2020, doi: 10.1515/eng-2020-0026.
  • J. You, W. Guo, Y. Zhang, and J. Hu, “An effective simulation model for multi-line metro systems based on origin-destination data,” in IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2016. doi: 10.1109/ITSC.2016.7795578.
  • B. Birol and A. F. Ergenç, “A modelling and simulation study of a metro line as a time-delayed switched system,” Journal of Rail Transport Planning and Management, vol. 22, 2022, doi: 10.1016/j.jrtpm.2022.100318.
  • D. Schmaranzer, A. Kiefer, R. Braune, and K. F. Doerner, “Simulation-Based Replacement Line and Headway Optimization,” in Proceedings - Winter Simulation Conference, 2020. doi: 10.1109/WSC48552.2020.9384022.
  • M. Rosetti, Simulation modelling and Arena, 2nd ed. New Jersey: Wiley, 2016.
  • T. Altiok and B. Melamed, Simulation modeling and analysis with ARENA. Burlington: Elsevier, 2007.
  • M. Marinov and J. Viegas, “Tactical management of rail freight transportation services: evaluation of yard performance,” Transportation Planning and Technology, vol. 34, no. 4, pp. 363–387, Jun. 2011, doi: 10.1080/03081060.2011.577155.
  • S. Tanyel and I. Candemir, “Hızlı Raylı Sistemlerin Yolcu Taşıma Kapasite Hesaplamaları ve Türkiye’deki Benzer Sistemlerin Birbirleriyle Karşılaştırılması,” in 6. Ulaştırma Kongresi, Istanbul, 2005.
  • Tom. Parkinson and I. (Operations planning manager) Fisher, Rail transit capacity. Transportation Research Board, National Research Council, 1996.
  • G. Garrisi and C. Cervelló-Pastor, “Train-Scheduling Optimization Model for Railway Networks with Multiplatform Stations,” Sustainability 2020, Vol. 12, Page 257, vol. 12, no. 1, p. 257, Dec. 2019, doi: 10.3390/SU12010257.
  • M. V. H. Als, M. B. Madsen, and R. M. Jensen, “A data-driven bi-objective matheuristic for energy-optimising timetables in a passenger railway network,” Journal of Rail Transport Planning & Management, vol. 26, p. 100374, Jun. 2023, doi: 10.1016/J.JRTPM.2023.100374.
  • D. Schmaranzer, R. Braune, and K. F. Doerner, “Simulation-based headway optimization for a subway network: A performance comparison of population-based algorithms,” Proceedings - Winter Simulation Conference, vol. 2018-December, pp. 1957–1968, Jul. 2018, doi: 10.1109/WSC.2018.8632362.
  • S. K. Chang and T. S. Chu, “Optimal headway and route length for a public transit System under the consideration of externality,” Journal of the Eastern Asia Society for Transportation Studies, vol. 6, no. 1, pp. 4001–4016, 2005.
  • L. Xu, X. Zhao, Y. Tao, Q. Zhang, and X. Liu, “Optimization of train headway in moving block based on a particle swarm optimization algorithm,” in 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014, 2014. doi: 10.1109/ICARCV.2014.7064429.

Utilizing Discrete Event Simulation to Optimize Headway Times in a Light Rail Transit System

Year 2025, Volume: 14 Issue: 2, 838 - 858, 30.06.2025
https://doi.org/10.17798/bitlisfen.1602799

Abstract

Due to recent urbanization and population growth in cities, the passenger transport system is faced with significant congestion and delay problems. In particular, to handle peak-time passenger traffic, railway public transport systems are operated with more frequent trips during the peak hours, and less-frequent train interarrivals are scheduled during off-peak hours to increase the total system utilization. This study proposes a simulation methodology for optimizing the trips of a metro line with variable passenger traffic. A microsimulation model was constructed using the Arena software to create an experimental medium for the optimization phase. The discrete-event simulation concept was utilized for the model building. The passenger flow and train arrival rates were the main input parameters obtained from the passenger origin-destination station matrix and train arrival data. The model was coupled with the artificial bee colony algorithm for determining the optimized train time headways. The optimization study resulted in a decrease of 27.5% and 25.5%in the total number of daily train trips for the normal and increased train capacity scenarios under a zero failed boarding policy. The results of the study were also compared with the literature findings which are discussed in detail.

Ethical Statement

The study is complied with research and publication ethics.

Thanks

The authors thanks to the Izmir Metro organization for providing the necessary data.

References

  • Turkish Statistical Institute, “Main economic developments report,” Ankara, Dec. 2025. Accessed: Jan. 02, 2023. [Online]. Available: https://www.tcmb.gov.tr/wps/wcm/connect/1d0a74af-e436-4377-af71-6dee68bec478/Main+Economic+Developments_December.pdf?MOD=AJPERES&CACHEID=ROOTWORKSPACE-1d0a74af-e436-4377-af71-6dee68bec478-ojytIq2
  • WPR, “World population review: Izmir city.”
  • Izmir Metro Ulaşım AŞ, “Izmir Metrosu 2020-2024 Stratejik Planı,” Izmir, 2020.
  • G. Öztürk, “Simulation & Analysis of Izmir Metro Transportation System,” Yaşar University, İzmir, 2012.
  • Ö. Yalçinkaya and G. M. Bayhan, “Modelling and optimization of average travel time for a metro line by simulation and response surface methodology,” Eur J Oper Res, vol. 196, no. 1, pp. 225–233, Jul. 2009, doi: 10.1016/J.EJOR.2008.03.010.
  • Transport Research Board, “Quantifying Transit’s Impact on GHG Emissions and Energy Use— The Land Use Component,” 2015. Accessed: Apr. 12, 2022. [Online]. Available: www.TRB.org
  • A. Landex, “Evaluation of Railway Networks with Single Track Operation Using the UIC 406 Capacity Method,” Netw Spat Econ, vol. 9, pp. 7–23, 2009, doi: 10.1007/s11067-008-9090-7.
  • N. Weik, J. Warg, I. Johansson, M. Bohlin, and N. Nießen, “Extending UIC 406-based capacity analysis-New approaches for railway nodes and network effects,” Journal of Rail Transport Planning & Management, vol. 15, p. 100199, 2020, doi: 10.1016/j.jrtpm.2020.100199.
  • P. Grube, F. Núñez, and A. Cipriano, “An event-driven simulator for multi-line metro systems and its application to Santiago de Chile metropolitan rail network,” Simul Model Pract Theory, vol. 19, no. 1, pp. 393–405, Jan. 2011, doi: 10.1016/j.simpat.2010.07.012.
  • M. A. Salido, F. Barber, and L. Ingolotti, “Robustness for a single railway line: Analytical and simulation methods,” Expert Syst Appl, vol. 39, no. 18, pp. 13305–13327, Dec. 2012, doi: 10.1016/J.ESWA.2012.05.071.
  • H. Huang, K. Li, and Y. Wang, “A Simulation Method for Analyzing and Evaluating Rail System Performance Based on Speed Profile,” J Syst Sci Syst Eng, vol. 27, no. 6, pp. 810–834, Dec. 2018, doi: 10.1007/s11518-017-5358-0.
  • N. Agatz, A. Erera, M. W. P. Savelsbergh, and X. Wang, “Dynamic ride-sharing: A simulation study in metro Atlanta,” Procedia Soc Behav Sci, vol. 17, pp. 532–550, 2011, doi: 10.1016/J.SBSPRO.2011.04.530.
  • J. Wales and M. Marinov, “Analysis of delays and delay mitigation on a metropolitan rail network using event based simulation,” Simul Model Pract Theory, vol. 52, pp. 52–77, 2015, doi: 10.1016/j.simpat.2015.01.002.
  • P. Potti, M. Marinov, and E. Sweeney, “A Simulation Study on the Potential of Moving Urban Freight by a Cross-City Railway Line,” Sustainability, vol. 11, no. 21, pp. 6088–6098, Nov. 2019, doi: 10.3390/su11216088.
  • M. S. Yıldırım, M. Karaşahin, and Ü. Gökkuş, “Scheduling of the Shuttle Freight Train Services for Dry Ports Using Multimethod Simulation–Optimization Approach,” International Journal of Civil Engineering, vol. 19, no. 1, pp. 67–83, Jan. 2021, doi: 10.1007/s40999-020-00553-0.
  • M. S. Yıldırım, “A Management System for Autonomous Shuttle Freight Train Service in Shared Railway Corridors,” International Journal of Civil Engineering, vol. 20, no. 3, pp. 273–290, Mar. 2022, doi: 10.1007/s40999-021-00663-3.
  • L. Zhang, M. Liu, X. Wu, and S. M. AbouRizk, “Simulation-based route planning for pedestrian evacuation in metro stations: A case study,” Autom Constr, vol. 71, pp. 430–442, Nov. 2016, doi: 10.1016/j.autcon.2016.08.031.
  • P. K. Kwok, M. Yan, B. K. P. Chan, and H. Y. K. Lau, “Crisis management training using discrete-event simulation and virtual reality techniques,” Comput Ind Eng, vol. 135, pp. 711–722, Sep. 2019, doi: 10.1016/j.cie.2019.06.035.
  • Ö. Yalçinkaya and G. Mirac Bayhan, “A feasible timetable generator simulation modelling framework for train scheduling problem,” Simul Model Pract Theory, vol. 20, no. 1, pp. 124–141, Jan. 2012, doi: 10.1016/J.SIMPAT.2011.09.005.
  • E. Tischer, P. Nachtigall, and J. Široký, “The use of simulation modelling for determining the capacity of railway lines in the Czech conditions,” Open Engineering, vol. 10, no. 1, pp. 224–231, Jan. 2020, doi: 10.1515/eng-2020-0026.
  • J. You, W. Guo, Y. Zhang, and J. Hu, “An effective simulation model for multi-line metro systems based on origin-destination data,” in IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2016. doi: 10.1109/ITSC.2016.7795578.
  • B. Birol and A. F. Ergenç, “A modelling and simulation study of a metro line as a time-delayed switched system,” Journal of Rail Transport Planning and Management, vol. 22, 2022, doi: 10.1016/j.jrtpm.2022.100318.
  • D. Schmaranzer, A. Kiefer, R. Braune, and K. F. Doerner, “Simulation-Based Replacement Line and Headway Optimization,” in Proceedings - Winter Simulation Conference, 2020. doi: 10.1109/WSC48552.2020.9384022.
  • M. Rosetti, Simulation modelling and Arena, 2nd ed. New Jersey: Wiley, 2016.
  • T. Altiok and B. Melamed, Simulation modeling and analysis with ARENA. Burlington: Elsevier, 2007.
  • M. Marinov and J. Viegas, “Tactical management of rail freight transportation services: evaluation of yard performance,” Transportation Planning and Technology, vol. 34, no. 4, pp. 363–387, Jun. 2011, doi: 10.1080/03081060.2011.577155.
  • S. Tanyel and I. Candemir, “Hızlı Raylı Sistemlerin Yolcu Taşıma Kapasite Hesaplamaları ve Türkiye’deki Benzer Sistemlerin Birbirleriyle Karşılaştırılması,” in 6. Ulaştırma Kongresi, Istanbul, 2005.
  • Tom. Parkinson and I. (Operations planning manager) Fisher, Rail transit capacity. Transportation Research Board, National Research Council, 1996.
  • G. Garrisi and C. Cervelló-Pastor, “Train-Scheduling Optimization Model for Railway Networks with Multiplatform Stations,” Sustainability 2020, Vol. 12, Page 257, vol. 12, no. 1, p. 257, Dec. 2019, doi: 10.3390/SU12010257.
  • M. V. H. Als, M. B. Madsen, and R. M. Jensen, “A data-driven bi-objective matheuristic for energy-optimising timetables in a passenger railway network,” Journal of Rail Transport Planning & Management, vol. 26, p. 100374, Jun. 2023, doi: 10.1016/J.JRTPM.2023.100374.
  • D. Schmaranzer, R. Braune, and K. F. Doerner, “Simulation-based headway optimization for a subway network: A performance comparison of population-based algorithms,” Proceedings - Winter Simulation Conference, vol. 2018-December, pp. 1957–1968, Jul. 2018, doi: 10.1109/WSC.2018.8632362.
  • S. K. Chang and T. S. Chu, “Optimal headway and route length for a public transit System under the consideration of externality,” Journal of the Eastern Asia Society for Transportation Studies, vol. 6, no. 1, pp. 4001–4016, 2005.
  • L. Xu, X. Zhao, Y. Tao, Q. Zhang, and X. Liu, “Optimization of train headway in moving block based on a particle swarm optimization algorithm,” in 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014, 2014. doi: 10.1109/ICARCV.2014.7064429.
There are 33 citations in total.

Details

Primary Language English
Subjects Transportation Engineering
Journal Section Research Article
Authors

Mehmet Sinan Yıldırım 0000-0001-5347-2456

Ziya Çakıcı 0000-0001-7003-815X

Early Pub Date June 27, 2025
Publication Date June 30, 2025
Submission Date January 2, 2025
Acceptance Date June 16, 2025
Published in Issue Year 2025 Volume: 14 Issue: 2

Cite

IEEE M. S. Yıldırım and Z. Çakıcı, “Utilizing Discrete Event Simulation to Optimize Headway Times in a Light Rail Transit System”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 14, no. 2, pp. 838–858, 2025, doi: 10.17798/bitlisfen.1602799.

Bitlis Eren University
Journal of Science Editor
Bitlis Eren University Graduate Institute
Bes Minare Mah. Ahmet Eren Bulvari, Merkez Kampus, 13000 BITLIS