Volume List  / Volume 5 (4)



DOI: 10.7708/ijtte.2015.5(4).07

5 / 4 / 425-441 Pages


Shahriar Afandizadeh Zargari - Iran University of Science and Technology, Department of Civil Engineering, Tehran, Iran -

Atousa Tajaddini - Islamic Azad University, South Tehran Branch, Tehran, Iran -

Mohammadreza Khalilzadeh - Islamic Azad University, South Tehran Branch, Tehran, Iran -


The primary objective of this research is to optimize signal timing in consecutive signalized intersections. In this paper, the combination of genetic programming (GP) with genetic algorithms (GA) and neural network (NN) with genetic algorithm (GA) were used and compared in order to optimize signal timing in consecutive signalized intersections. First, genetic programming and neural network were constructed from existing signal timing data to predict the delay of intersections. Then genetic algorithm was applied to optimize these predictive networks (GP and NN). The results and comparisons of timing process and error percentage showed that neural network is more efficient than genetic programming. However, the ability of genetic programming in producing formula is a specific characteristic which makes it more applicable than neural network. Finally, for validating the results, Aimsun and Synchro micro simulation software were used, and accuracy of our models was approved.

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Abdullahi, B.; Porwal, H.; Recker, W. 1999. Short Term Freeway Traffic Flow prediction Using Genetically-Optimized Time-Delay-Based Neural Networks, Institute Transportation Studies. California PATH Working Paper, UCB-ITS, PWP-99-1.


Adacher, L. 2012. A global optimization approach to solve the traffic signal synchronization problem, Procedia-Social and Behavioral Sciences. DOI: http://dx.doi.org/10.1016/j.sbspro.2012.09.841, 54: 1270-1277.


Alodat, M.; Al-Odat, I. 2013. Using Polygamy Technology with FL, GA and NN on Traffic Lights, The International Journal of Engineering and Science (IJES), 2(7): 39-45.


Chang, J.; Bertoli, B.; Xin, W. 2010. New Signal Control Optimization Policy for Oversaturated Arterial Systems. In Proceedings of the Transportation Research Board 89th Annual Meeting, Washington D.C., USA. 20 p.


Dell'Orco, M.; Baskan, O.; Marinelli, M. 2013. A harmony search algorithm approach for optimizing traffic signal timings, Promet-Traffic & Transportation. DOI: http://dx.doi.org/10.7307/ptt.v25i4.979, 25(4): 349-358.


Girianna, M.; Benekohal, R. 2002. Dynamic Signal Coordination for Networks with Oversaturated Intersections, Transportation Research Record. DOI: http://dx.doi.org/10.3141/1811-15, 1811: 122-130.


Hajbabaie, A.; Medina, J.C.; Benekohal, R.F. 2011. Traffic Signal Coordination and Queue Management in Oversaturated Intersection, NEXTRANS Project No. 047IY02. 108 p.


Hu, X.; Lu, J.; Wang, W.; Zhirui, Y. 2015. Traffic Signal Synchronization in the Saturated High-Density Grid Road Network, Computational Intelligence and Neuroscience, Article ID 532960. 1-11.


König, R. 2014. Enhancing Genetic programming for predictive modeling, Örebro: Örebro universitet. 240 p.


Lo, H.; Chow, A. 2004. Control Strategies for Oversaturated Traffic, Journal of Transportation Engineering. DOI: http://dx.doi.org/10.1061/(ASCE)0733-947X(2004)130:4(466), 130(4): 466-478.


Searson, D. 2009. GPTIPS: Genetic Programming & Symbolic Regression for MATLAB. Available from Internet: http://gptips.sourceforge.net.


Searson, D.P.; Leahy, D.E.; Willis, M.J. 2010. GPTIPS: An Open Source Genetic Programming Toolbox for Multigene Symbolic Regression, In Proceedings of International MultiConference of Engineers and Computer Scientists (IMECS). 77-80.


Teodorović, D.; Šelmić, M.; Vukićević, I. 2014. Locating Hubs in Transport Networks: An Artificial Intelligence Approach, International Journal for Traffic and Transport Engineering. DOI: http://dx.doi.org/10.7708/ijtte.2014.4(3).04, 4(3): 286-296.


Transportation Research Board. 2000. Highway Capacity Manual, Published by the National Research Council, Washington D.C.


Zang, L.; Zhu, W.; Hu, P.; Li, J. 2012. Study on Intelligent Control Algorithm for Traffic Signals at Multi-phase Intersections, Journal of Computational Information Systems, 8(24): 10477-10484.