Benzetim modelleme ve deneysel tasarım ile sinyal kontrollü kentsel trafik akışının en iyilenmesi

Abstract

Kentsel alanlardaki trafik akışı, hem sürücü hem de yayalar için temel problemlerden biridir. Trafik yoğunluğu ve trafik ışıkları büyük zaman kayıplarına yol açmaktadır.  Trafikte beklemelerle oluşan bu zaman kayıpları, ülkeler için boşa harcanmış önemli yakıt miktarlarına ve dolayısıyla önemli maliyet kayıplarına neden olmaktadır. Bu çalışmada, İzmir, Türkiye’de bir anayoldaki trafik akışı incelenmiştir. Kentsel trafikte boşa harcanmış kaynakları azaltmak üzere; sinyal kontrollü trafik akışını etkileyen faktörleri dikkate alan bir deneysel tasarım çalışması yapılmıştır. Tasarım faktörleri; trafik ışıklarının sinyal süreleri, trafik yoğunluğu ve araçların hızı olarak belirlenmiştir. Bu faktörlerin, sistemde geçirilen süre, kırmızı ışıkta bekleme süreleri ve sistemden çıkabilen araç sayısı amaçları üzerindeki etkileri incelenmiştir. Seçilen 486 tasarım noktasının sonuçları, oluşturulan benzetim modelinden elde edilmiştir. Sonuç olarak, tasarım noktaları içinden toplam bekleme süresini en küçükleyen en iyi faktör düzeyleri belirlenmiştir.

Keywords

• Kentsel trafik akışı,Trafik sinyalleri,Kesirli çok etkenli tasarım,Benzetim,Trafik yoğunluğu,Trafik Optimizasyonu

Optimization of signal controlled urban traffic flow using simulation modeling and an experimental design

Abstract

Traffic flow in urban areas is one of the major problems both for drivers and pedestrians. Traffic congestion and traffic lights constitute a large portion of the time spent in traffic. This wasted time for waiting in traffic also costs countries considerable amount of wasted fuel and hence considerable amount of money. In this study, traffic flow of a road in Izmir, Turkey is considered. In order to decrease all the wasted resources in urban traffic, an experimental design is conducted on the factors affecting the signal controlled traffic flow. The design factors are determined to be signal times of traffic lights, traffic intensity and the speed of vehicles. The effects of these factors on the three performance measures of time in system, waiting time in red light and number of vehicles going out of the system are analyzed. A fractional factorial design is carried out on the 486 design points evaluated using simulation modeling. In results, among the design points, best level of factors to minimize total waiting time in traffic flow are determined.

Keywords

• Urban traffic flow,Traffic signals,Fractional factorial design,Simulation,Traffic intensity,Traffic optimization

References

1. Febbraro AD, Sacco N. “On evaluating traffic lights performance sensitivity via hybrid systems models”. Procedia-Social and Behavioral Sciences, 111, 272-281, 2014.
2. Papageorgiou M, Diakaki C, Dinopoulou V, Kotsialos A, Wang Y. “Review of road traffic control strategies”. Proceedings of the IEEE, 91(12), 2043-2067.2003.
3. Mitsakis E, Salanova JM, Giannopoulos G. “Combined dynamic traffic assignment and urban traffic control models”. Procedia Social and Behavioral Sciences, 20, 427-436, 2011.
4. Araghi S, Khosravi A, Creighton D. “A review on computational intelligence methods for controlling traffic signal timing”. Expert Systems with Applications, 42, 1538-1550, 2015.
5. Boumediene, A, Brahimi K, Belguesmia N, Bouakkaz K. “Saturation flow versus green time at two‐stagesignal controlled intersections”. Transport, 24(4), 288-295, 2009.
6. Babicheva TS, Babichev DS. “Numerical methods for modeling of traffic flows at research and optimization of traffic on the signal controlled road intersections”. Procedia Computer Science, 55, 461-468, 2015.
7. Mirchandani P, Head L. “A real-time traffic signal control system: architecture, algorithms, and analysis”. Transportation Research Part C, 9(6), 415-432, 2001.
8. Jahangiri A, Afandizadeh S, Kalantari N. “Theotimization of traffic signal timing for emergency evacuation using the simulated annealing algorithm”. Transport, 26(2), 133-140. 2011.

9. Pranevicius H, Kraujalis, T. “Knowledge based traffic control model for signalized intersection”. Transport 27(3), 263-267, 2012.
10. Varia HR, Gundaliya PJ, Dhingra SL. “Application of genetic algorithms for joint optimization of signal setting parameters and dynamic traffic assignment for the real network data”. Reseach in Transportation Economics, 38(1), 35-44, 2013.
11. Jeihani M, James P, Saka AA, Ardeshiri A. “Traffic recovery time estimation under different flow regimes in traffic simulation”. Journal of Traffic and Transportation Engineering (English Edition), 2(5), 291-300, 2015.
12. Hu W, Wang H, Du B, Yan L. “A Multi-Intersection Model And Signal Timing Plan Algorithm For Urban Traffic Signal Control”. Transport, 32(4), 368-378, 2017.
13. Grether D, Neumann A, Nagel K. “Simulation of urban traffic control: a queue model approach”. Procedia Computer Science, 10, 808-814, 2012.
14. BabichevaTS. “The use of queuing theory at research and optimization of traffic on the signal-controlled road intersections”. Procedia Computer Science 55, 469-478.2015.
15. Arena 14.0, Rockwell Automation Inc, 2012.
16. Minitab 17.3.1, Minitab, Inc., 2016.
17. OptQuest for ARENA 6.4, OptTek Systems Inc., 2006.