Abstract
In this modern era of technology, time and energy are the most valuable assets of current civil societies. Due to the growing population and number of vehicles on the roads, traffic congestion is becoming an escalating complex problem, and this problem has now grown into a major issue for the urban planning authorities. Therefore, many researchers continue investigating this problem and exploiting modern science to develop reliable mathematical models, simulation scenarios and solution approaches for the traffic light control system that could optimally coordinate the traffic signals to time and reduce traffic congestion. In this paper, we propose a dynamic application that simulates many traffic light models using a Genetic algorithm. We have used the Genetic algorithm for traffic signal's time management and to compute optimal solutions for cycle times, offset times and green times according to the sequence orders of a set of traffic lights. We employed the fitness function in order to minimize the time waste on the model. The experimental prototype delivers sound results and adequate optimal solutions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Kwasnicka, H. and Stanek M.: Genetic approach to optimize traffic flow by timing plan manipulation. Intelligent Systems Design and Applications, ISDA’06. Sixth International Conference on, IEEE (2006).
Purohit, G., Sherry, M. and Saraswat, M.: Time optimization for real time traffic signal control system using Genetic Algorithm. Global Journal of Enterprise Information System, Volume 3, Issue 4, (2011).
Russell, S. J. and Norvig, P.: Artificial intelligence: a modern approach. Prentice hall Englewood Cliffs (1995).
T.-T.: Deployment of intelligent transport systems on the trans european road network, expert group on ITS for road traffic management (2002).
Van Zuylen, H. J. and Viti, F.: Delay at controlled intersections: the old theory revised. Intelligent Transportation Systems Conference, ITSC'06. IEEE, (2006).
Wargo, J., Wargo, L. and Alderman, N.: The harmful effects of vehicle exhaust: A case for policy change, environment & human health, (2006).
Yousef, K. M., Al-Karaki, J. N. and Shatnawi, A.: Intelligent traffic light flow control system using wireless sensors networks. Journal of Information Science and Engineering Volume 26, No. 3, pp. 753-768, (2010).
Zang, L., Hu, P. and Zhu, W.: Study on dynamic coordinated control of traffic signals for oversaturated arterials. Journal of Information and Computational Science, Volume 9, Issue 12, pp. 3625-3632, (2012).
Donis-Díaza,C.A., Muroa,A.G., Bello-Péreza, R., Morales, E.V.: A hybrid model of genetic algorithm with local search to discover linguistic data summaries from creep data. Expert Systems with Applications, Volume 41, Issue 4, pp. 2035–2042(2014).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Udagepola, K., Alshami, B.A., Afzal, N., Li, X. (2015). An Adaptive Controller of Traffic Lights using Genetic Algorithms. In: Selvaraj, H., Zydek, D., Chmaj, G. (eds) Progress in Systems Engineering. Advances in Intelligent Systems and Computing, vol 366. Springer, Cham. https://doi.org/10.1007/978-3-319-08422-0_94
Download citation
DOI: https://doi.org/10.1007/978-3-319-08422-0_94
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08421-3
Online ISBN: 978-3-319-08422-0
eBook Packages: EngineeringEngineering (R0)