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Ankara Metrosu M1 Hattı Hareket Saatlerinin Çizelgelenmesi: Bir Karar Destek Sistemi Uygulaması

Year 2018, Volume: 1 Issue: 2, 108 - 128, 30.10.2018

Abstract

Sanayileşmenin ve iş olanaklarının artmasıyla
birlikte kırsal kesimden kentsel alanlara göç yaşanmaktadır. Bu göçün sonucu
olarak kentlerimizde nüfus artmaktadır. Nüfusun artması kentlerimizde ulaşımla
ilgili birçok problemi ve trafik sıkışıklığı problemini ortaya çıkarmıştır. Bu
sorunların ortadan kaldırılması için ulaşım planlaması gerekmektedir. Bu
problemler ortadan kaldırılırken veya azaltılırken aynı zamanda yolcu konforu
ve memnuniyeti de dikkate alınmalıdır. Bunların sağlanabilmesi yolcu talebini
de dikkate alarak sefer saatlerinin çizelgelenmesi ve planlanmasıyla mümkün
olmaktadır. Çizelgeleme ve planlama yapabilmek için yolcu sayıları, sefer
süreleri, tren kapasiteler vb. gibi birçok veriye ihtiyaç duyulmaktadır. Çalışmamızın
konusu olan M1(Kızılay-Batıkent) hattı için geçmiş yılların yolcu sayıları
değerlendirilip gelecek dönem tahmini yapılmıştır. Yolcu sayılarına göre
hattaki tren sayısı ve sefer aralıkları uzman sistemler kullanılarak
belirlenmiştir. 

References

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  • [2]. Cordeau J. F; Toth P; Vigo D. A survey of optimization models for train routing and scheduling. Transportation science, 32(4), 1998: 380-404.
  • [3]. Caprara A; Fischetti M; Toth P. Modeling and solving the train timetabling problem. Operations research, 50(5), 2002: 851-861.
  • [4]. Ahuja R. K; Liu, J; Orlin J. B; Sharma D; Shughart L. A. Solving real-life locomotive scheduling problems. Transportation Science, 39(4), 2005: 503-517.
  • [5]. Chang S. C; Chung Y. C. From timetabling to train regulation—a new train operation model, Information and Software Technology, 47(9), 2005: 575-585.
  • [6]. Reimann M; Leal J. E; ACO for the single line train scheduling problem. In Proceedings of MIC. 2009.
  • [7]. Liebchen, C; Stiller S. Delay resistant timetabling. Public Transport 4, 2012: 55–72
  • [8]. Danescu, E. Integration and ınteroperability of rail transport in europe, implications of the network in Romania and Moldova, DH 34-08.00. 14-Nord Economy, International Economic Relations, 2013.
  • [9]. Gültekin N; Eren T. Demiryolu Çizelgeleme Probleminin Modellenmesi Ve Çözümü." Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi 29.2, 2014.
  • [10]. Eren T; Gencer M.A. Ankara Metrosu M1 (Kızılay-Batıkent) Hattı Hareket Saatlerinin Çizelgelenmesi. Academic Platform-Journal of Engineering and Science 4.2, 2016.
  • [11]. Halim H. I; Sakr A. M; Walid M. A. Metro timetable optimization from passenger perspective based on simulation models and incomplete data of passenger flow. Computational Intelligence (SSCI), 2016 IEEE Symposium Series on. IEEE, 2016.
  • [12]. Fournier D;, et al. Metro Energy Optimization through Rescheduling: Mathematical Models and Heuristic Algorithm Compared to MILP and CMA-ES. Diss. Inria Saclay Ile de France, 2016.
  • [13]. Hassannayebi E; Zegordi S.H; Yaghini M. Train timetabling for an urban rail transit line using a Lagrangian relaxation approach. Applied Mathematical Modelling 40.23-24, 2016: 9892-9913.
  • [14]. Yin J; et al. Energy-efficient metro train rescheduling with uncertain time-variant passenger demands: An approximate dynamic programming approach. Transportation Research Part B: Methodological 91. 2016: 178-210.
  • [15]. Gupta S. D; Tobin J. K; Pavel L. A two-step linear programming model for energy-efficient timetables in metro railway networks. Transportation Research Part B: Methodological 93, 2016: 57-74.
  • [16]. Yang X; et al. Bi-objective programming approach for solving the metro timetable optimization problem with dwell time uncertainty. Transportation Research Part E: Logistics and Transportation Review 97, 2017: 22-37.
  • [17]. Yin J.; et al. Metro train rescheduling by adding backup trains under disrupted scenarios. Frontiers of Engineering Management 4.4, 2017: 418-427.
  • [18]. Wei L; Zhenzhou Y. A Robust Timetabling Model for a Metro Line with Passenger Activity Information. Information 8.3, 2017: 102.
  • [19]. Xin G.; et al. Multiperiod-based timetable optimization for metro transit networks. Transportation Research Part B: Methodological 96, 2017: 46-67.
  • [20]. Hassannayebi E; et al. Train timetabling at rapid rail transit lines: a robust multi-objective stochastic programming approach. Operational Research 17.2, 2017: 435-477.
  • [21]. Wang H.; et al. Metro timetable optimisation for minimising carbon emission and passenger time: a bi-objective integer programming approach. IET Intelligent Transport Systems, 2018.
  • [22]. Zhou Y.; et al. Integrated Optimization on Train Control and Timetable to Minimize Net Energy Consumption of Metro Lines. Journal of Advanced Transportation, 2018.
  • [23]. Keping L; Huang H; Schonfeld P. Metro Timetabling for Time-Varying Passenger Demand and Congestion at Stations." Journal of Advanced Transportation, 2018.
  • [24]. Shi Jungang; et al. Service-oriented train timetabling with collaborative passenger flow control on an oversaturated metro line: An integer linear optimization approach. Transportation Research Part B: Methodological 110, 26-59,2018.
  • [25]. Tektaş M; Akbaş A; Topuz V. Yapay Zeka Tekniklerinin Trafik Kontrolünde Kullanılması Üzerine Bir İnceleme.2006.
  • [26]. İncekara H. Tıbbi tahlil sonuçlarının analizinde web ara yüzlü bulanık uzman sistem tasarımı, Doctoral dissertation, Selçuk Üniversitesi Fen Bilimleri Enstitüsü.2010
  • [27]. Durduran S. S; Fatih S. Konya İlinde Meydana Gelen Bisiklet Kazalarının Karar Destek Sistemleri Yardımıyla Web Tabanlı Mekânsal Analizi. Selçuk Üniversitesi Mühendislik, Bilim ve Teknoloji Dergisi, 26(1), 23-32.2011.
  • [28]. Hu Z.H; Sheng Z.H. A decision support system for public logistics information service management and optimization. Decision Support Systems, 219-229.2014.
  • [29]. Çolakoğlu A; Küçükpehlivan G. Kullanıcı odaklı bisiklet yolu güzergâhı belirlenmesi için karar destek modeli önerisi. VIII. Mimarlıkta Sayısal Tasarım Ulusal Sempozyumu, İzmir, İYTE.2014.
  • [30]. Lin C; Choy K; Ho, G; Lam H; Pang G. K; Chin K. A decision support system for optimizing dynamic courier routing operations. A decision support system for optimizing dynamic courier routing operations, 6917–6933.2014.
  • [31]. Lam H; Choy K; Ho G;Cheng S. W.; Lee C. A knowledge-based logistics operations planning system for mitigating risk in warehouse order fulfillment. International Journal of Production Economics, 1-17.2015.
  • [32]. Başoğlu B; Bulut, M. Kısa dönem elektrik talep tahminleri için yapay sinir ağları ve uzman sistemler tabanlı hibrit sistem geliştirilmesi. Journal of the Faculty of Engineering & Architecture of Gazi University, 32(2).2017.
  • [33]. Karacasu M; Kentiçi Otobüs Taşımacılığında Özelleştirme İçin Bir Karar Destek Modeli Önerisi: Eskisehir Örneği, Doktora Tezi, İstanbul Teknik Üniversitesi İstanbul, Türkiye, 2003
  • [34]. Macharis C; Caris A; Jourquin B; Pekin, E. A decision support framework for intermodal transport policy. European Transport Research Review, 3(4).2011, 167-178.
  • [35]. Caris, A; Macharis, C; Janssens, G. K. Decision support in intermodal transport: A new research agenda. Computers in industry, 64(2).2013, 105-112.
  • [36]. Sprenger, R.; Mönch, L. A decision support system for cooperative transportation planning: Design, implementation, and performance assessment. Expert Systems with Applications, 41(11). 2014, 5125-5138.
  • [37]. Sun, F., Dubey, A., White, J., & Gokhale, A. Transit-hub: A smart public transportation decision support system with multi-timescale analytical services. Cluster Computing, 2017.
  • [38]. Yang Shuo; et al. MILP formulations and a TS algorithm for reliable last train timetabling with uncertain transfer flows. Journal of the Operational Research Society 69.8. 2018, 1318-1334.
Year 2018, Volume: 1 Issue: 2, 108 - 128, 30.10.2018

Abstract

References

  • [1]. Rainer R. K; Cegielski C. G. Introduction to Information Systems: Enabling and Transforming Business. Hoboken: John Wiley & Sons.2010.
  • [2]. Cordeau J. F; Toth P; Vigo D. A survey of optimization models for train routing and scheduling. Transportation science, 32(4), 1998: 380-404.
  • [3]. Caprara A; Fischetti M; Toth P. Modeling and solving the train timetabling problem. Operations research, 50(5), 2002: 851-861.
  • [4]. Ahuja R. K; Liu, J; Orlin J. B; Sharma D; Shughart L. A. Solving real-life locomotive scheduling problems. Transportation Science, 39(4), 2005: 503-517.
  • [5]. Chang S. C; Chung Y. C. From timetabling to train regulation—a new train operation model, Information and Software Technology, 47(9), 2005: 575-585.
  • [6]. Reimann M; Leal J. E; ACO for the single line train scheduling problem. In Proceedings of MIC. 2009.
  • [7]. Liebchen, C; Stiller S. Delay resistant timetabling. Public Transport 4, 2012: 55–72
  • [8]. Danescu, E. Integration and ınteroperability of rail transport in europe, implications of the network in Romania and Moldova, DH 34-08.00. 14-Nord Economy, International Economic Relations, 2013.
  • [9]. Gültekin N; Eren T. Demiryolu Çizelgeleme Probleminin Modellenmesi Ve Çözümü." Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi 29.2, 2014.
  • [10]. Eren T; Gencer M.A. Ankara Metrosu M1 (Kızılay-Batıkent) Hattı Hareket Saatlerinin Çizelgelenmesi. Academic Platform-Journal of Engineering and Science 4.2, 2016.
  • [11]. Halim H. I; Sakr A. M; Walid M. A. Metro timetable optimization from passenger perspective based on simulation models and incomplete data of passenger flow. Computational Intelligence (SSCI), 2016 IEEE Symposium Series on. IEEE, 2016.
  • [12]. Fournier D;, et al. Metro Energy Optimization through Rescheduling: Mathematical Models and Heuristic Algorithm Compared to MILP and CMA-ES. Diss. Inria Saclay Ile de France, 2016.
  • [13]. Hassannayebi E; Zegordi S.H; Yaghini M. Train timetabling for an urban rail transit line using a Lagrangian relaxation approach. Applied Mathematical Modelling 40.23-24, 2016: 9892-9913.
  • [14]. Yin J; et al. Energy-efficient metro train rescheduling with uncertain time-variant passenger demands: An approximate dynamic programming approach. Transportation Research Part B: Methodological 91. 2016: 178-210.
  • [15]. Gupta S. D; Tobin J. K; Pavel L. A two-step linear programming model for energy-efficient timetables in metro railway networks. Transportation Research Part B: Methodological 93, 2016: 57-74.
  • [16]. Yang X; et al. Bi-objective programming approach for solving the metro timetable optimization problem with dwell time uncertainty. Transportation Research Part E: Logistics and Transportation Review 97, 2017: 22-37.
  • [17]. Yin J.; et al. Metro train rescheduling by adding backup trains under disrupted scenarios. Frontiers of Engineering Management 4.4, 2017: 418-427.
  • [18]. Wei L; Zhenzhou Y. A Robust Timetabling Model for a Metro Line with Passenger Activity Information. Information 8.3, 2017: 102.
  • [19]. Xin G.; et al. Multiperiod-based timetable optimization for metro transit networks. Transportation Research Part B: Methodological 96, 2017: 46-67.
  • [20]. Hassannayebi E; et al. Train timetabling at rapid rail transit lines: a robust multi-objective stochastic programming approach. Operational Research 17.2, 2017: 435-477.
  • [21]. Wang H.; et al. Metro timetable optimisation for minimising carbon emission and passenger time: a bi-objective integer programming approach. IET Intelligent Transport Systems, 2018.
  • [22]. Zhou Y.; et al. Integrated Optimization on Train Control and Timetable to Minimize Net Energy Consumption of Metro Lines. Journal of Advanced Transportation, 2018.
  • [23]. Keping L; Huang H; Schonfeld P. Metro Timetabling for Time-Varying Passenger Demand and Congestion at Stations." Journal of Advanced Transportation, 2018.
  • [24]. Shi Jungang; et al. Service-oriented train timetabling with collaborative passenger flow control on an oversaturated metro line: An integer linear optimization approach. Transportation Research Part B: Methodological 110, 26-59,2018.
  • [25]. Tektaş M; Akbaş A; Topuz V. Yapay Zeka Tekniklerinin Trafik Kontrolünde Kullanılması Üzerine Bir İnceleme.2006.
  • [26]. İncekara H. Tıbbi tahlil sonuçlarının analizinde web ara yüzlü bulanık uzman sistem tasarımı, Doctoral dissertation, Selçuk Üniversitesi Fen Bilimleri Enstitüsü.2010
  • [27]. Durduran S. S; Fatih S. Konya İlinde Meydana Gelen Bisiklet Kazalarının Karar Destek Sistemleri Yardımıyla Web Tabanlı Mekânsal Analizi. Selçuk Üniversitesi Mühendislik, Bilim ve Teknoloji Dergisi, 26(1), 23-32.2011.
  • [28]. Hu Z.H; Sheng Z.H. A decision support system for public logistics information service management and optimization. Decision Support Systems, 219-229.2014.
  • [29]. Çolakoğlu A; Küçükpehlivan G. Kullanıcı odaklı bisiklet yolu güzergâhı belirlenmesi için karar destek modeli önerisi. VIII. Mimarlıkta Sayısal Tasarım Ulusal Sempozyumu, İzmir, İYTE.2014.
  • [30]. Lin C; Choy K; Ho, G; Lam H; Pang G. K; Chin K. A decision support system for optimizing dynamic courier routing operations. A decision support system for optimizing dynamic courier routing operations, 6917–6933.2014.
  • [31]. Lam H; Choy K; Ho G;Cheng S. W.; Lee C. A knowledge-based logistics operations planning system for mitigating risk in warehouse order fulfillment. International Journal of Production Economics, 1-17.2015.
  • [32]. Başoğlu B; Bulut, M. Kısa dönem elektrik talep tahminleri için yapay sinir ağları ve uzman sistemler tabanlı hibrit sistem geliştirilmesi. Journal of the Faculty of Engineering & Architecture of Gazi University, 32(2).2017.
  • [33]. Karacasu M; Kentiçi Otobüs Taşımacılığında Özelleştirme İçin Bir Karar Destek Modeli Önerisi: Eskisehir Örneği, Doktora Tezi, İstanbul Teknik Üniversitesi İstanbul, Türkiye, 2003
  • [34]. Macharis C; Caris A; Jourquin B; Pekin, E. A decision support framework for intermodal transport policy. European Transport Research Review, 3(4).2011, 167-178.
  • [35]. Caris, A; Macharis, C; Janssens, G. K. Decision support in intermodal transport: A new research agenda. Computers in industry, 64(2).2013, 105-112.
  • [36]. Sprenger, R.; Mönch, L. A decision support system for cooperative transportation planning: Design, implementation, and performance assessment. Expert Systems with Applications, 41(11). 2014, 5125-5138.
  • [37]. Sun, F., Dubey, A., White, J., & Gokhale, A. Transit-hub: A smart public transportation decision support system with multi-timescale analytical services. Cluster Computing, 2017.
  • [38]. Yang Shuo; et al. MILP formulations and a TS algorithm for reliable last train timetabling with uncertain transfer flows. Journal of the Operational Research Society 69.8. 2018, 1318-1334.
There are 38 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Muhammet Abdullah Gencer 0000-0001-9955-5468

Haci Mehmet Alakaş 0000-0002-9874-7588

Tamer Eren 0000-0001-5282-3138

Mustafa Hamurcu 0000-0002-6166-3946

Publication Date October 30, 2018
Submission Date October 11, 2018
Acceptance Date October 30, 2018
Published in Issue Year 2018 Volume: 1 Issue: 2

Cite

APA Gencer, M. A., Alakaş, H. M., Eren, T., Hamurcu, M. (2018). Ankara Metrosu M1 Hattı Hareket Saatlerinin Çizelgelenmesi: Bir Karar Destek Sistemi Uygulaması. Akıllı Ulaşım Sistemleri Ve Uygulamaları Dergisi, 1(2), 108-128.