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İzole Sinyalize Kavşaklar için Çiçek Tozlaşma Algoritması Kullanılarak Devre Süresi Modellerinin Geliştirilmesi

Year 2020, Volume: 9 Issue: 3, 1401 - 1414, 26.09.2020
https://doi.org/10.17798/bitlisfen.750884

Abstract

Son zamanlarda nüfus ve ekonomideki büyüme karayollarında araç kullanımını arttırmakta, buna bağlı olarak da kavşakların kapasitesi yetersiz kalmaktadır. Kavşakların verimsiz çalışmasından dolayı gecikme, yakıt tüketimi, emisyon salınımı artarken sürücü davranışları da olumsuz etkilenmektedir. Kavşak geometrilerinin iyileştirilmesinin yanı sıra, optimum devre süresinin doğru tespiti ve sinyal sürelerinin düzenlenmesi ile de bu sorunların minimuma indirilebilmesi mümkün olmaktadır. Bu çalışmada Çiçek Tozlaşma Algoritması (ÇTA) kullanılarak optimum devre süresi modelleri geliştirilmiştir. Ayrıca en düşük gecikmeye sahip olan devre sürelerinin belirlenmesinde Diferansiyel Gelişim Algoritmasından (DGA) yararlanılmıştır. Kalibre edilen Webster modeline ilave olarak sabit eklenmiş Webster model formu ve üstel formda devre süresi modelleri geliştirilmiştir. VISSIM simülasyon programı ile elde edilen gecikme değerlerine göre geliştirilen bütün modeller Webster modeli ve VISTRO optimizasyon programı ile karşılaştırılmış ve önerilen modellerin istatistiksel olarak daha iyi performansa sahip olduğu görülmüştür. Bu modellerin özellikle yüksek trafik hacmine sahip trafik durumlarında yetersiz kalan Webster modelindeki eksiklikleri kapatarak alternatif bir devre süresi tahmin modeli olarak kullanılabileceği ön görülmektedir.

Supporting Institution

Kırıkkale Üniversitesi Bilimsel Araştırma Projeleri Komisyon Birimi

Project Number

2018/031

References

  • [1] Webster F.V. 1958. Traffic signal settings, road research technical paper. No: 39, Road Research Laboratory.
  • [2] Thomas G.B., Upchurch J.E. 1998. Effect of non-optimal cycle lengths and traffic volumes on progression. Institute of Transportation Engineers. ITE Journal, 68 (5): 38.
  • [3] Lan C.J. 2004. New optimal cycle length formulation for pretimed signals at isolated intersections. Journal of Transportation Engineering, 130 (5): 637-647.
  • [4] Cheng D., Tian Z. Z., Messer C. J. 2005. Development of an improved cycle length model over the highway capacity manual 2000 quick estimation method. Journal of Transportation Engineering, 131(12): 890-897.
  • [5] Day C., Bullock D., Sturdevant J. 2009. Cycle-length performance measures: revisiting and extending fundamentals. Transportation Research Record: Journal of the Transportation Research Board, (2128): 48-57.
  • [6] Singh L., Tripathi S., Arora H. 2009. Time optimization for traffic signal control using genetic algorithm. International Journal of Recent Trends in Engineering, 2 (2): 4-6.
  • [7] Al-Kubaisi M. I. 2012. Optimum Cycle Time Prediction for Signalized Intersections at Baghdad City. Cankaya University Journal of Science and Engineering, 9 (2): 149-166.
  • [8] Sun S.N., Xiao W., Qiu X., Chen J.D., Ying L.X. 2012. Design and Simulation of Urban Road Intersection Signal Linear Control System. In: Applied Mechanics and Materials, 178: 2713-2716.
  • [9] Dai L. L., Sun Z. L., Liu D. B., Li Y. 2013. An Improved Method of Traffic Control Period Division for Intersection Based on Signal Cycle Calculation. In Applied Mechanics and Materials 253: 1731-1735.
  • [10] Dell'Orco M., Baskan O., Marinelli M. 2013. A Harmony Search Algorithm approach for optimizing traffic signal timings. PROMET-Traffic & Transportation, 25 (4): 349-358.
  • [11] Wu Y., Lu J., Chen H., Yang H. 2015. Development of an optimization traffic signal cycle length model for signalized intersections in China. Mathematical Problems in Engineering, 954295: 1-9.
  • [12] Çakıcı Z., Murat Y.Ş. 2015. Sezgisel Optimizasyon Algoritmalarının Taşıt Gecikmesi Problemi Üzerine Uygulaması.7. Kentsel Altyapı Sempozyumu, Trabzon, 615-625.
  • [13] Zakariya A.Y., Rabia S.I. 2016. Estimating the minimum delay optimal cycle length based on a time-dependent delay formula. Alexandria Engineering Journal, 55 (3): 2509-2514.
  • [14] Jovanović A., Nikolić M., Teodorović D. 2017. Area-wide urban traffic control: A Bee Colony Optimization approach. Transportation Research Part C: Emerging Technologies, 77: 329-350.
  • [15] Storn R., Price K. 1997. Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 11 (4): 341-359.
  • [16] Yang X.-S. 2014. Nature-inspired optimization algorithms. Elsevier.
  • [17] http://vision-traffic.ptvgroup.com/en-us/products/ptv-vissim/use-cases/ (ErişimTarihi:15.04.2019).
  • [18] Mallipeddi R., Suganthan P.N., Pan Q.-K., Tasgetiren M.F. 2011. Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl. Soft Comput., 11(2): 1679-1696.
  • [19] Yang X.-S. 2012. Flower pollination algorithm for global optimization. In: International conference on unconventional computing and natural computation, 240-249.
Year 2020, Volume: 9 Issue: 3, 1401 - 1414, 26.09.2020
https://doi.org/10.17798/bitlisfen.750884

Abstract

Project Number

2018/031

References

  • [1] Webster F.V. 1958. Traffic signal settings, road research technical paper. No: 39, Road Research Laboratory.
  • [2] Thomas G.B., Upchurch J.E. 1998. Effect of non-optimal cycle lengths and traffic volumes on progression. Institute of Transportation Engineers. ITE Journal, 68 (5): 38.
  • [3] Lan C.J. 2004. New optimal cycle length formulation for pretimed signals at isolated intersections. Journal of Transportation Engineering, 130 (5): 637-647.
  • [4] Cheng D., Tian Z. Z., Messer C. J. 2005. Development of an improved cycle length model over the highway capacity manual 2000 quick estimation method. Journal of Transportation Engineering, 131(12): 890-897.
  • [5] Day C., Bullock D., Sturdevant J. 2009. Cycle-length performance measures: revisiting and extending fundamentals. Transportation Research Record: Journal of the Transportation Research Board, (2128): 48-57.
  • [6] Singh L., Tripathi S., Arora H. 2009. Time optimization for traffic signal control using genetic algorithm. International Journal of Recent Trends in Engineering, 2 (2): 4-6.
  • [7] Al-Kubaisi M. I. 2012. Optimum Cycle Time Prediction for Signalized Intersections at Baghdad City. Cankaya University Journal of Science and Engineering, 9 (2): 149-166.
  • [8] Sun S.N., Xiao W., Qiu X., Chen J.D., Ying L.X. 2012. Design and Simulation of Urban Road Intersection Signal Linear Control System. In: Applied Mechanics and Materials, 178: 2713-2716.
  • [9] Dai L. L., Sun Z. L., Liu D. B., Li Y. 2013. An Improved Method of Traffic Control Period Division for Intersection Based on Signal Cycle Calculation. In Applied Mechanics and Materials 253: 1731-1735.
  • [10] Dell'Orco M., Baskan O., Marinelli M. 2013. A Harmony Search Algorithm approach for optimizing traffic signal timings. PROMET-Traffic & Transportation, 25 (4): 349-358.
  • [11] Wu Y., Lu J., Chen H., Yang H. 2015. Development of an optimization traffic signal cycle length model for signalized intersections in China. Mathematical Problems in Engineering, 954295: 1-9.
  • [12] Çakıcı Z., Murat Y.Ş. 2015. Sezgisel Optimizasyon Algoritmalarının Taşıt Gecikmesi Problemi Üzerine Uygulaması.7. Kentsel Altyapı Sempozyumu, Trabzon, 615-625.
  • [13] Zakariya A.Y., Rabia S.I. 2016. Estimating the minimum delay optimal cycle length based on a time-dependent delay formula. Alexandria Engineering Journal, 55 (3): 2509-2514.
  • [14] Jovanović A., Nikolić M., Teodorović D. 2017. Area-wide urban traffic control: A Bee Colony Optimization approach. Transportation Research Part C: Emerging Technologies, 77: 329-350.
  • [15] Storn R., Price K. 1997. Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 11 (4): 341-359.
  • [16] Yang X.-S. 2014. Nature-inspired optimization algorithms. Elsevier.
  • [17] http://vision-traffic.ptvgroup.com/en-us/products/ptv-vissim/use-cases/ (ErişimTarihi:15.04.2019).
  • [18] Mallipeddi R., Suganthan P.N., Pan Q.-K., Tasgetiren M.F. 2011. Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl. Soft Comput., 11(2): 1679-1696.
  • [19] Yang X.-S. 2012. Flower pollination algorithm for global optimization. In: International conference on unconventional computing and natural computation, 240-249.
There are 19 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Araştırma Makalesi
Authors

Ali Payidar Akgüngör 0000-0003-0669-5715

Sevim Yavuz This is me 0000-0002-9262-7278

Ersin Korkmaz 0000-0003-3725-164X

Erdem Doğan 0000-0001-7802-641X

Project Number 2018/031
Publication Date September 26, 2020
Submission Date June 10, 2020
Acceptance Date July 1, 2020
Published in Issue Year 2020 Volume: 9 Issue: 3

Cite

IEEE A. P. Akgüngör, S. Yavuz, E. Korkmaz, and E. Doğan, “İzole Sinyalize Kavşaklar için Çiçek Tozlaşma Algoritması Kullanılarak Devre Süresi Modellerinin Geliştirilmesi”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 9, no. 3, pp. 1401–1414, 2020, doi: 10.17798/bitlisfen.750884.

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