Meta-Sezgisel Yöntemlerle Sabit Zamanlı Sinyalize Kavşaklar için Optimum Devre Süresi Modeli

Year 2019, Volume: 6 Issue: 2, 259 - 269, 31.05.2019

Ali Akgüngör , Özge Yılmaz Ersin Korkmaz , Erdem Doğan

https://doi.org/10.31202/ecjse.496257

Cited By: 3

Abstract

Son zamanlarda artan nüfus ve ekonomideki büyüme trafikteki araç
kullanımını arttırmakta ve buna bağlı olarak da kavşaklarda kapasite yetersizliklerine
neden olmaktadır. Kavşaklarda meydana gelen fazla zaman kayıplarından dolayı
gecikme, yakıt tüketimi, emisyon salınımı artarken sürücü davranışları da
olumsuzluk etkilenmektedir. Optimum devre süresinin doğru tespiti ve sinyal sürelerinin
düzenlenmesi ile bu sorunların minimuma indirilebilmesi mümkün olmaktadır. Bu
çalışmada Yapay Arı Kolonisi Algoritması kullanılarak optimum devre süresi
modelleri geliştirilmiştir. Ayrıca en düşük gecikmeye sahip olan devre
sürelerinin belirlenmesinde Diferansiyel Gelişim Algoritmasından
yararlanılmıştır. Kalibre edilen Webster modeline ilave olarak üstel ve
kuadratik formda modeller geliştirilmiştir. Geliştirilen bütün modeller Webster
modelinden istatistiksel olarak daha iyi performansa sahip olurken, en iyi
performansı da üstel model vermiştir. Bu modeller özellikle yüksek trafik
hacmine sahip trafik durumlarında yetersiz kalan Webster modelinin eksikliğini
kapatarak alternatif olarak kullanılabileceği görülmüştür.  

Keywords

Optimum devre süresi, Webster modeli, Yapay Arı Kolonisi Algoritması

Optimum Cycle Length Model for Fixed-Time Signalized Intersections with Meta-Heuristic Methods

Abstract

Recently, growth in population and economy has increased the use of
vehicles on highways. Depending on this, the capacity of intersections becomes
increasingly insufficient. Due to inefficient operation of intersections, the
delay, fuel consumption, and emission release increase, and also the driver
behaviors are negatively affected. It is possible to minimize these problems by
correctly determination of optimum cycle length and adjustment of signal times.
In this study, optimum cycle length models have been developed using Artificial
Bee Colony Algorithm (ABC). In addition, Differential Evolution Algorithm (DE)
has been used to determine the cycle length which has the minimum delay. In
addition to the Calibrated Webster model, exponential and quadratic forms have
been developed. All developed models have statistically better performance than
the Webster model and also exponential model has illustrated the best
performance. It has been seen that these models can be used as an alternative
estimating cycle length model by overcoming the deficiency of Webster model
which is insufficient especially in high traffic volumes.

References

• [1] Webster F. V., “Traffic signal settings”, Road research technical paper no. 39. Road Research Laboratory, (1958).
• [2] Chang T. H., Lin, J. T., “Optimal signal timing for an oversaturated intersection”, Transportation Research Part B: Methodological, 34(6), 471-491, (2000).
• [3] Cheng D., Messer C. J., Tian Z. Z., Liu, J., “Modification of Webster’s minimum delay cycle length equation based on HCM 2000”, In the 81st Annual Meeting of the Transportation Research Board in Washington, DC (2003).
• [4] Lan C. J., “New optimal cycle length formulation for pretimed signals at isolated intersections”, Journal of transportation engineering, 130(5), 637-647, (2004).
• [5] Cheng D., Tian Z. Z., Messer C. J., “Development of an improved cycle length model over the highway capacity manual 2000 quick estimation method”, Journal of transportation engineering, 131(12), 890-897, (2005).
• [6] Han L., Li J. M., “Short or Long—Which Is Better?: Probabilistic Approach to Cycle Length Optimization”, Transportation Research Record: Journal of the Transportation Research Board, vol, 2035, 150-157, (2007).
• [7] Day C., Bullock D., Sturdevant J., “Cycle-length performance measures: revisiting and extending Fundamentals”, Transportation Research Record: Journal of the Transportation Research Board, vol, 2128, 48-57, (2009).
• [8] Singh L., Tripathi S., Arora H., “Time optimization for traffic signal control using genetic algorithm”, International Journal of Recent Trends in Engineering, 2(2), 4-6, (2009).
• [9] Ma D., Nakamura H., “Cycle length optimization at isolated signalized intersections from the viewpoint of emission”, In Traffic and Transportation Studies 2010, 275-284, (2010).
• [10] Dai L. L., Sun Z. L., Liu D. B., Li Y., “An Improved Method of Traffic Control Period Division for Intersection Based on Signal Cycle Calculation”, In Applied Mechanics and Materials, 253, 1731-1735, (2013).
• [11] Al-Kubaisi, M. I., “Optimum Cycle Time Prediction for Signalized Intersections at Baghdad City”, Cankaya University Journal of Science and Engineering, 9(2), (2012).
• [12] Dell'Orco M., Baskan O., Marinelli M., “A Harmony Search Algorithm approach for optimizing traffic signal timings”, PROMET-Traffic&Transportation, 25(4), 349-358, (2013).
• [13] Dell’Orco M., Başkan Ö., Marinelli M., “Artificial Bee Colony-based algorithm for optimising traffic signal timings”, In Soft Computing in Industrial Applications, 327-337, (2014).
• [14] Murat Y. Ş., Çakıcı Z., “Sezgisel Optimizasyon Algoritmalarının Taşıt Gecikmesi Problemi Üzerine Uygulaması”, 7. Altyapı Sempozyumu, (2015).
• [15] Zakariya A. Y., Rabia S. I., “Estimating the minimum delay optimal cycle length based on a time-dependent delay formula”, Alexandria Engineering Journal, 55(3), 2509-2514, (2016).
• [16] Jovanović A., Nikolić M., Teodorović D., “Area-wide urban traffic control: A Bee Colony Optimization approach”, Transportation Research Part C: Emerging Technologies, 77, 329-350, (2017).
• [17] Storn R., Price K., “Differential Evolution – A Simple and Efficient Adaptive Scheme for Global Optimization Over Continuous Spaces”, Technical Report TR-95-012, ICSI, (1995).
• [18] Karaboğa D., “Yapay Zeka Optimizasyon Algoritmaları”, Nobel, İstanbul, 2014.