Optimization of Traffic Network Signal Durations with Heuristic Algorithm and the Effect of Number of Individuals

Yıl 2023, Cilt: 6 Sayı: 1, 78 - 88, 31.05.2023

Cihan Karakuzu , Emin Topal

https://doi.org/10.34088/kojose.1159113

Öz

In the traffic network that we frequently use in our daily life, the primary demand of people has been to reduce the time they spend in traffic and to travel to the points they want to reach as quickly as possible. Developing countries want to meet this demand with the least cost in order to meet this demand. This study aims to manage the traffic network with the best times by optimizing the traffic signal durations in order to minimize the travel time for a road network chosen as a benchmark. For the optimization process, it is aimed to run a population-based heuristic algorithm with different numbers of individuals and obtain the best travel time. With the help of an open-source code traffic simulation program, which was run by modeling the benchmark road network, the received traffic data was also visually analyzed and compared. The effects of the heuristic algorithms applied with different numbers of individuals on the travel times according to the starting-destination points were examined before and after the optimization. As a result of the study, it has been observed that travel times and traffic signal times can be reduced with heuristic algorithms. Based on both numerical metrics and visual results, it has been determined that optimized traffic light durations give better results than non-optimized ones.

Anahtar Kelimeler

Differential Evolution Algorithm, Heuristics Algorithms, Modeling and Simulation, Traffic Lights Optimization, Traffic Network Simulation, Soft Computing

Kaynakça

• [1] Pan J., Popa I. S., Zeitouni K. and Borcea C., 2013. Proactive Vehicular Traffic Rerouting for Lower Travel Time. IEEE Transactions on Vehicular Technology, 62 (8), pp. 3551-3568.
• [2] Namlı R., 2015. Köprülü kavşaklar ve trafik güvenliği. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, 31(2), pp. 129-134.
• [3] Claes R. and Holvoet T., 2011. Ant Colony Optimization Applied to Route Planning Using Link Travel Time Predictions. 2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum, 16-20 May, pp. 358-365.
• [4] Jindal V. and Bedi P., 2018. An improved hybrid ant particle optimization (IHAPO) algorithm for reducing travel time in VANETs. Applied Soft Computing, 64, pp. 526-535.
• [5] Chiou S. -W., 1998. Bi-level formulation for equilibrium traffic flow and signal settings. Mathematics in Transport Planning and Control. Emerald Group Publishing Limited, Bingley, pp. 59-68.
• [6] Allsop R. E. and Charlesworth J. A., 1977. Traffic in a signal-controlled road network: An example of different signal timings including different routing. Traffic Engineering & Control, 18(5), pp. 262-264.
• [7] Sheffi Y. and Powell W. B., 1983. Optimal signal settings over transportation networks. Journal of Transportation Engineering, 109(6), pp. 824-839.
• [8] Başkan Ö., Ceylan H., Ozan C., 2020. Investigating Acceptable Level of Travel Demand Before Capacity Enhancement for Signalized Urban Road Networks. Teknik Dergi, 31(2), pp. 9897-9917.
• [9] Krylatov A., Puzach V., Shatalova N., Asaul M., 2020. Optimization of traffic lights operation using network load data. Transportation Research Procedia, 50, pp. 321-329.
• [10] Safadi Y., Haddad J., 2021. Optimal combined traffic routing and signal control in simple road networks: an analytical solution. Transportmetrica A Transport Science, 17(3), pp. 308-339.
• [11] Chiou S.W., 2019. A two-stage model for period-dependent traffic signal control in a road networked system with stochastic travel demand. Information Sciences, 476, pp. 256-273.
• [12] Zhu R., Li L., Wu S., Lv P., Li Y., Xu M., 2023. Multi-agent broad reinforcement learning for intelligent traffic light control. Information Sciences, 619, pp.509-525.
• [13] Haddad T.A., Hedjazi D., Aouag S., 2022. A deep reinforcement learning-based cooperative approach for multi-intersection traffic signal control, Engineering Applications of Artificial Intelligence, 114, 105019.
• [14] Younes M.B., Boukerche A., De Rango F., 2022. SmartLight: A smart efficient traffic light scheduling algorithm for green road intersections. Ad Hoc Networks, 103061.
• [15] Tong C. O. and Wong S. C., 2010. Heuristic algorithms for simulation-based dynamic traffic assignment. Transportmetrica, 6(2), pp. 97-120.
• [16] Abdalhaq B. K. and Baker M. I. A., 2014. Using meta heuristic algorithms to improve traffic simulation. Journal of Algorithms, 2(4), pp. 110-128.
• [17] Rodrigues de Campos G., Falcone P., Hult R., Wymeersch H. and J. Sjöberg, 2017. Traffic coordination at road intersections: Autonomous decision-making algorithms using model-based heuristics. IEEE Intelligent Transportation Systems Magazine, 9(1), pp. 8-21.
• [18] Erdoğmuş P., 2018. A New Solution Approach for Non-Linear Equation Systems with Grey Wolf Optimizer. Sakarya University Journal of Computer and Information Sciences, 1(3), pp. 1-11.
• [19] Yüzgeç U. and İnaç T., 2016. Adaptive Spiral Optimization Algorithm for Benchmark Problems. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, 3(1), pp. 8-15.
• [20] Akgüngör A., Yılmaz Ö., Korkmaz E., Doğan E., 2019. Meta-Sezgisel Yöntemlerle Sabit Zamanlı Sinyalize Kavşaklar için Optimum Devre Süresi Modeli. El-Cezeri Journal of Science and Engineering, 6(2), pp. 259-269.
• [21] Bautista P.B., Aguiar L.U., Igartua M.A., 2022. How does the traffic behavior change by using SUMO traffic generation tools. Computer Communications, 181, pp. 1-13.
• [22] Krajzewicz D., 2010. Traffic simulation with SUMO–simulation of urban mobility. In: Barceló, J. (eds) Fundamentals of Traffic Simulation. International Series in Operations Research & Management Science, 145, pp. 269-293.
• [23] Topal E., Karakuzu C., Bozkurt H., 2021. Denektaşı bir yol-kavşak ağı için basit bir sezgisel yaklaşım ile trafik ışık sürelerinin eniyilenmesi. Cukurova 7th International Scientific Researches Conference, Adana, Türkiye, 7- 8 September, pp. 1149-1164.