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Transit Frequency Optimization Using Firefly Algorithm and Evaluation of the Parameters

Year 2021, Volume: 3 Issue: 3, 236 - 247, 16.09.2021
https://doi.org/10.47933/ijeir.897839

Abstract

ABSTRACT: Over the last few decades, rapidly growing cities in terms of population and land use have led to many transportation-based problems such as longer travel times, traffic congestion, traffic crashes, and air and noise pollution. Increasing the modal share of transit systems appears to be one of the most effective methods to solve transportation-based problems. However, transit systems, particularly in countries having limited resources, should be used efficiently to achieve sustainable urban mobility. Even only adjusting frequencies of transit lines, with no infrastructure investment cost requirements, can provide a more efficient transit system. In this paper, a transit frequency setting model based on the Firefly Algorithm (FA), which is a relatively new metaheuristic, for the transportation network design problems is presented to minimize total user cost under a fleet size constraint. The proposed model is performed on a 10-route Mandl’s Test Network using different combinations of parameters to demonstrate the effect of parameter values on the solution quality. After that, the best solution of 30 solutions obtained by the calibrated parameter values is compared to the existing frequency set of the 10-route transit network. The results show that the FA can obtain better frequency sets by selecting the proper values for the parameters.

References

  • Farahani RZ, Miandoabchi E, Szeto WY, Rashidi H (2013). “A review of urban transportation network design problems”. European Journal of Operational Research, 229(2), 281–302.
  • Ceder A (2007). Public Transit Planning and Operation: Modeling, Practice and Behavior. Elsevier, Oxford, UK.
  • Martinez H, Mauttone A, Urquhart ME (2014). “Frequency optimization in public transportation systems: Formulation and metaheuristic approach”. European Journal of Operational Research, 236 (1), 27-36.
  • Magnanti TL, Wong RT (1984). “Network Design and Transportation Planning: Models and Algorithms”. Transportation Science, 18 (1), 1-55.
  • Luo Z, Pang J, Ralph D (1996). Mathematical Programs with Equilibrium Constraints. Cambridge University Press, Cambridge, UK
  • Yu B, Yang Z, Yao J (2010). “Genetic Algorithm for Bus Frequency Optimization”. Journal of Transportation Engineering, 136 (6), 576-583.
  • Yoo GS, Kim DK, Chon KS (2010). “Frequency design in urban transit networks with variable demand: Model and algorithm”. KSCE Journal of Civil Engineering, 14 (3), 403-411.
  • Yu B, Yang Z, Sun X, Yao B, Zeng Q, Jeppesen E (2011). “Parallel genetic algorithm in bus route headway optimization”. Applied Soft Computing Journal, 11 (8), 5081-5091.
  • Dell'Olio L, Ibeas A, Ruisanchez F (2012). “Optimizing bus-size and headway in transit networks”. Transportation, 39 (2), 449-464.
  • Huang Z, Ren G, Liu H (2013). “Optimizing bus frequencies under uncertain demand: Case study of the transit network in a developing city”. Mathematical Problems in Engineering, 2013 (1).
  • Wu J, Song R, Wang Y, Chen F, Li S (2015). “Modeling the coordinated operation between bus rapid transit and bus”. Mathematical Problems in Engineering, 2015(1).
  • Verbas IO, Mahmassani HS (2015). “Integrated Frequency Allocation and User Assignment in Multimodal Transit Networks”. Transportation Research Record: Journal of the Transportation Research Board, 2498(1), 37-45.
  • Giesen R, Martinez H, Mauttone A, Urquhart ME (2016). “A method for solving the multi-objective transit frequency optimization problem”. Journal of Advanced Transportation, 50 (8), 2323-2337.
  • Cipriani E, Gori S, Petrelli M (2012). “Transit network design: A procedure and an application to a large urban area”. Transportation Research Part C: Emerging Technologies, 20 (1), 3-14.
  • Szeto WY, Jiang Y (2014). “Transit route and frequency design: Bi-level modeling and hybrid articial bee colony algorithm approach”. Transportation Research Part B: Methodological, 67, 235-263.
  • Nikolic M, Teodorovic D (2014). “A simultaneous transit network design and frequency setting: Computing with bees”. Expert Systems with Applications, 41(16), 7200-7209.
  • Zhao H, Xu W, Jiang R (2015). “The Memetic algorithm for the optimization of urban transit network”. Expert Systems with Applications, 42 (7), 376-3773.
  • Buba AT, Lee LS (2018). “A differential evolution for simultaneous transit network design and frequency setting problem”. Expert Systems with Applications, 106, 277-289.
  • Jha SB, Jha JK, Tiwari MK (2019). “A multi-objective meta-heuristic approach for transit network design and frequency setting problem in a bus transit system”. Computers and Industrial Engineering, 130, 166-186.
  • Duran J, Pradenas L, Parada V (2019). “Transit network design with pollution minimization”. Public Transport, 11(1), 189-210.
  • Uvaraja V, Lee LS (2017). “Metaheuristic approaches for urban transit scheduling problem: A review”. Journal of Advanced Review on Scientic Research, 34 (1), 11-25.
  • Baaj MH, Mahmassani H (1991). “An AI-Based Approach for Transit Route System Planning and Design”. Journal of Advanced Transportation, 25 (2), 187-209.
  • Afandizadeh S, Khaksar H, Kalantari N (2013). “Bus fleet optimization using genetic algorithm a case study of Mashhad”. International Journal of Civil Engineering, 11 (1), 43-52.
  • Arbex RO, da Cunha CB (2015). “Effcient transit network design and frequencies setting multi-objective optimization by alternating objective genetic algorithm”. Transportation Research Part B, 81, 355-376.
  • Yang XS (2010). Engineering Optimization An Introduction with Metaheuristics Applications. Wiley, NJ, USA.

Ateş Böceği Algoritması Kullanılarak Toplu Taşıma Frekans Optimizasyonu ve Parametrelerinin Değerlendirilmesi

Year 2021, Volume: 3 Issue: 3, 236 - 247, 16.09.2021
https://doi.org/10.47933/ijeir.897839

Abstract

Son yıllarda şehirlerin nüfus ve arazi kullanımı açısından hızla büyümeleri, trafik sıkışıklıkları, artan seyahat süreleri, trafik kazaları, hava ve gürültü kirliliği gibi ulaşım tabanlı problemlerin artmasına yol açmıştır. Toplu taşıma sistemlerinin türel payını artırmak, ortaya çıkan problemleri yatıştırmak için en etkili yöntemlerden biri olarak kabul edilmektedir. Bununla birlikte, özellikle kısıtlı kaynaklara sahip ülkelerde toplu taşıma sistemleri sürdürülebilir kentsel hareketliliği sağlayabilmek için verimli bir şekilde kullanılmalıdır. Yatırım maliyetine ihtiyaç duymadan, sadece toplu taşıma hatlarının frekanslarını ayarlayarak, daha verimli bir toplu taşıma sistemi sağlanabilir. Bu çalışmada, belirli bir filo kısıtı altında toplam kullanıcı maliyetini en küçüklemek için, ulaşım ağ tasarım problemlerinde nispeten yeni bir metasezgisel olan Ateşböceği Algoritması (AB) tabanlı bir toplu taşıma frekans ayarlama modeli sunulmaktadır. Önerilen model, seçilen parametre değerlerinin sonuçlar üzerindeki etkisini göstermek için farklı parametre kombinasyonları kullanılarak 10 rotalı Mandl Test Ağı üzerinde test edilmiştir. Ardından, kalibre edilmiş parametre değerleri ile elde edilen 30 çözüm arasından en iyi çözüm 10 rotalı toplu taşıma ağının mevcut frekans değerleri ile karşılaştırılmıştır. Sonuçlar, AB algoritmasının parametreleri için doğru değerler seçilerek daha iyi çözümler elde edebilebileceğini göstermektedir.

References

  • Farahani RZ, Miandoabchi E, Szeto WY, Rashidi H (2013). “A review of urban transportation network design problems”. European Journal of Operational Research, 229(2), 281–302.
  • Ceder A (2007). Public Transit Planning and Operation: Modeling, Practice and Behavior. Elsevier, Oxford, UK.
  • Martinez H, Mauttone A, Urquhart ME (2014). “Frequency optimization in public transportation systems: Formulation and metaheuristic approach”. European Journal of Operational Research, 236 (1), 27-36.
  • Magnanti TL, Wong RT (1984). “Network Design and Transportation Planning: Models and Algorithms”. Transportation Science, 18 (1), 1-55.
  • Luo Z, Pang J, Ralph D (1996). Mathematical Programs with Equilibrium Constraints. Cambridge University Press, Cambridge, UK
  • Yu B, Yang Z, Yao J (2010). “Genetic Algorithm for Bus Frequency Optimization”. Journal of Transportation Engineering, 136 (6), 576-583.
  • Yoo GS, Kim DK, Chon KS (2010). “Frequency design in urban transit networks with variable demand: Model and algorithm”. KSCE Journal of Civil Engineering, 14 (3), 403-411.
  • Yu B, Yang Z, Sun X, Yao B, Zeng Q, Jeppesen E (2011). “Parallel genetic algorithm in bus route headway optimization”. Applied Soft Computing Journal, 11 (8), 5081-5091.
  • Dell'Olio L, Ibeas A, Ruisanchez F (2012). “Optimizing bus-size and headway in transit networks”. Transportation, 39 (2), 449-464.
  • Huang Z, Ren G, Liu H (2013). “Optimizing bus frequencies under uncertain demand: Case study of the transit network in a developing city”. Mathematical Problems in Engineering, 2013 (1).
  • Wu J, Song R, Wang Y, Chen F, Li S (2015). “Modeling the coordinated operation between bus rapid transit and bus”. Mathematical Problems in Engineering, 2015(1).
  • Verbas IO, Mahmassani HS (2015). “Integrated Frequency Allocation and User Assignment in Multimodal Transit Networks”. Transportation Research Record: Journal of the Transportation Research Board, 2498(1), 37-45.
  • Giesen R, Martinez H, Mauttone A, Urquhart ME (2016). “A method for solving the multi-objective transit frequency optimization problem”. Journal of Advanced Transportation, 50 (8), 2323-2337.
  • Cipriani E, Gori S, Petrelli M (2012). “Transit network design: A procedure and an application to a large urban area”. Transportation Research Part C: Emerging Technologies, 20 (1), 3-14.
  • Szeto WY, Jiang Y (2014). “Transit route and frequency design: Bi-level modeling and hybrid articial bee colony algorithm approach”. Transportation Research Part B: Methodological, 67, 235-263.
  • Nikolic M, Teodorovic D (2014). “A simultaneous transit network design and frequency setting: Computing with bees”. Expert Systems with Applications, 41(16), 7200-7209.
  • Zhao H, Xu W, Jiang R (2015). “The Memetic algorithm for the optimization of urban transit network”. Expert Systems with Applications, 42 (7), 376-3773.
  • Buba AT, Lee LS (2018). “A differential evolution for simultaneous transit network design and frequency setting problem”. Expert Systems with Applications, 106, 277-289.
  • Jha SB, Jha JK, Tiwari MK (2019). “A multi-objective meta-heuristic approach for transit network design and frequency setting problem in a bus transit system”. Computers and Industrial Engineering, 130, 166-186.
  • Duran J, Pradenas L, Parada V (2019). “Transit network design with pollution minimization”. Public Transport, 11(1), 189-210.
  • Uvaraja V, Lee LS (2017). “Metaheuristic approaches for urban transit scheduling problem: A review”. Journal of Advanced Review on Scientic Research, 34 (1), 11-25.
  • Baaj MH, Mahmassani H (1991). “An AI-Based Approach for Transit Route System Planning and Design”. Journal of Advanced Transportation, 25 (2), 187-209.
  • Afandizadeh S, Khaksar H, Kalantari N (2013). “Bus fleet optimization using genetic algorithm a case study of Mashhad”. International Journal of Civil Engineering, 11 (1), 43-52.
  • Arbex RO, da Cunha CB (2015). “Effcient transit network design and frequencies setting multi-objective optimization by alternating objective genetic algorithm”. Transportation Research Part B, 81, 355-376.
  • Yang XS (2010). Engineering Optimization An Introduction with Metaheuristics Applications. Wiley, NJ, USA.
There are 25 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

İlyas Cihan Aksoy 0000-0002-4256-8222

Mehmet Metin Mutlu 0000-0003-0008-8279

Yalçın Alver 0000-0002-9833-4505

Publication Date September 16, 2021
Acceptance Date April 26, 2021
Published in Issue Year 2021 Volume: 3 Issue: 3

Cite

APA Aksoy, İ. C., Mutlu, M. M., & Alver, Y. (2021). Transit Frequency Optimization Using Firefly Algorithm and Evaluation of the Parameters. International Journal of Engineering and Innovative Research, 3(3), 236-247. https://doi.org/10.47933/ijeir.897839

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