Abstract
Traffic is always a big problem in cities especially in Asian countries including Vietnam, Philippines, and India, etc. Traffic in these places is characterized by vehicles such as motorbikes, bicycles, cars, and buses while traveling on roads often without dedicated lanes. They do not follow the traffic lane and occupy any lateral position over the width of roadway depending on the availability of road space at a given instant of time. Such a transport system is called heterogeneous traffic. The issue of intelligent traffic light control thus attracts much attention. These include solutions such as controlling traffic lights in a grid, in a straight line (green wave) or controlling at intersections. This paper uses Fuzzy Logic to optimize traffic lights at an isolated intersection and in heterogeneous traffic conditions. The system is simulated on SUMO simulation software. The results of the application of fuzzy control algorithms have been remarkably effective compared to the use of fixed traffic lights.
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
References
Razavi, M., Hamidkhani, M., Sadeghi, R.: Smart traffic light scheduling in smart city using image and video processing. In: 3rd International Conference on Internet of Things and Applications (IoT), pp. 1–4. Isfahan, Iran (2019). https://doi.org/10.1109/iicita.2019.8808836
Pandey, K., Jalan, P.: An approach for optimizing the average waiting time for vehicles at the traffic intersection. In: fifth International Conference on Parallel, Distributed and Grid Computing (PDGC), pp. 30–35. Solan Himachal Pradesh, India (2018). https://doi.org/10.1109/pdgc.2018.8745757
Luo, J., Huang, Y., Weng, Y.: Design of Variable Traffic Light Control Systems for Preventing Two-Way Grid Network Traffic Jams Using Timed Petri Nets. IEEE Trans. Intell. Transp. Syst. (2019). https://doi.org/10.1109/TITS.2019.2925824
Qi, L., Zhou, M., Luan, W.: An emergency traffic light strategy to prevent traffic congestion. In: IEEE 13th International Conference on Networking, Sensing, and Control (ICNSC), pp. 1–6. Mexico City (2016). https://doi.org/10.1109/icnsc.2016.7479013
Natafgi MB, Osman M, Haidar AS, Hamandi L (2018) Smart Traffic Light System Using Machine Learning. In: IEEE International Multidisciplinary Conference on Engineering Technology (IMCET), Beirut, 2018, pp. 1–6. https://doi.org/10.1109/imcet.2018.8603041
Rida, N., Ouadoud, M., Hasbi, A., Chebli, S.: Adaptive traffic light control system using wireless sensors networks. In: IEEE 5th International Congress on Information Science and Technology (CiSt), pp. 552–556. Marrakech (2018). https://doi.org/10.1109/cist.2018.8596620
Wu, Q., He, F., Fan, X.: The intelligent control system of traffic light based on fog computing. Chin. J. Electron. 27(6), 1265–1270 (2018). https://doi.org/10.1049/cje.2018.09.015
Mir, A., Hassan, A.: Fuzzy inference rule based neural traffic light controller. In: IEEE International Conference on Mechatronics and Automation (ICMA), pp. 816–820. Changchun (2018). https://doi.org/10.1109/icma.2018.8484382
Nurlayli, A., Alqodri, F., Sakkinah, I. S.: Design of fuzzy simulation for determining the duration of traffic light based on vehicle density level and carbon monoxide level. In: 4th International Conference on Science and Technology (ICST), pp. 1–6. Yogyakarta (2018). https://doi.org/10.1109/icstc.2018.8528671
Koilias, A., Mousas, C,, Rekabdar, B,, Anagnostopoulos, C., Passenger anxiety when seated in a virtual reality self-driving car. In: IEEE Conference on Virtual Reality and 3D User Interfaces (VR), pp. 1024–1025. Osaka, Japan, (2019). https://doi.org/10.1109/vr.2019.8798084
Ren, H., Song, Y., Wang, J., Hu, Y., Lei, J.: A deep learning approach to the citywide traffic accident risk prediction. In: 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 3346–3351. Maui, HI (2018). https://doi.org/10.1109/itsc.2018.8569437
Deshmukh, S.M., Savant, B.N.: Traffic congestion alerting system. In: International Conference on Computing Communication Control and Automation (ICCUBEA), pp. 1–5. Pune (2016). https://doi.org/10.1109/iccubea.2016.7860110
Taniguchi, Y., Hisamatsu, H.: A study on road surface condition monitoring system using bicycle-mounted grid laser light. In: 7th International Conference on Intelligent Systems, Modelling and Simulation (ISMS), pp. 356–359. Bangkok (2016). https://doi.org/10.1109/isms.2016.23
Webster, F.V.:. Traffic signal settings. Road Research Technical Paper No. 39. London: Great Britain Road Research Laboratory(1958)
Saidallah, M., Fergougui AE et al. A Comparative Study of Urban Road Traffic Simulators. In: MATEC Web of Conferences, vol. 81, p. 05002. (2016). https://doi.org/10.1051/matecconf/20168105002
Eclipse SUMO, https://www.dlr.de/ts/en. Accessed 14 Aug 2019
Design and simulate fuzzy logic systems, https://ch.mathworks.com/products/fuzzy-logic.html, Accessed 14 Aug 2019
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Hung, P.D., Giang, D.T. (2021). Traffic Light Control at Isolated Intersections in Case of Heterogeneous Traffic. In: Kreinovich, V., Hoang Phuong, N. (eds) Soft Computing for Biomedical Applications and Related Topics. Studies in Computational Intelligence, vol 899. Springer, Cham. https://doi.org/10.1007/978-3-030-49536-7_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-49536-7_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-49535-0
Online ISBN: 978-3-030-49536-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)