Abstract
Traffic congestion is one of the major modern-day crisis in the world. There are many reasons behind this problem, among which the common reasons are poor traffic management, cars changing lanes, unplanned stoppage, dysfunctional traffic lights, drivers not following rules, emergency vehicle priorities not met etc. To overcome such situations traffic police is placed and the traffic congestion is handled by them manually. But in congested cities, it is very tough to handle huge traffic by a traffic police manually. As more and more vehicles are being commissioned in an already congested traffic system, there is an urgent need for a whole new traffic control system using advanced technologies to utilize the already existent infrastructures to its fullest extent. In this work, we create a fully automated system for traffic control based on traffic density with the help of a machine learning algorithm. We used foreground background subtraction to identify the vehicles in each lane. Using K-nearest neighbour algorithm we computed the density of each lane. Using KNN algorithm we found the accuracy as 99.04% and recall as 73.18%. We then create a database with the density values of each lane using phpmyadmin. The density values are fetched by NodeMCU from the cloud and traffic signals are activated based on the largest density in a round robin fashion. We further improvise the system for prioritizing emergency vehicles in the congestion. We use the Yolo object detection algorithm to detect emergency vehicles like ambulances so that traffic can be cleared up for them.
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Smart cities.: Smart cities. https://smartcities.gov.in/about-the-mission 23rd May 2023. Accessed 30 May 2023
Wei, L., Li, J.-H., Xu, L.-W., Gao, L., Yang, J.: Queue length estimation for signalized intersections under partially connected vehicle environment. J. Adv. Transp. 2022, 9568723 (2022)
Appiah, O., Quayson, E., Opoku, E.: Ultrasonic sensor based traffic information acquisition system; a cheaper alternative for its application in developing countries. Sci. Afr. 9, e00487 (2020)
Kuhn, J.P., Bui, B.C., Pieper, G.J.: Acoustic sensor system for vehicle detection and multi-lane highway monitoring. (1997). US Patent US5798983A
Tau vehicle type recognition dataset from kaggle. https://www.kaggle.com/c/vehicle
Hilmani, A., Maizate, A., Hassouni, L.: Automated real-time intelligent traffic control system for smart cities using wireless sensor networks. Wirel. Commun. Mob. Comput. 2020, 8841893 (2020)
Ransubhe, S., Mughni, M.A., Shiralkar, C.R., Ratnaparkhi, B.: Smart traffic light switching and traffic density calculation model using computer vision. In: 2023 IEEE 8th International Conference for Convergence in Technology (I2CT), pp. 1–5, (2023)
Mohandass, M.P., Kaliraj, I., Maareeswari, R., Vimalraj, R.: Iot based traffic management system for emergency vehicles. In: t2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS), vol. 1, pp. 1755–1759 (2023)
Humayun, M., Afsar, S., Almufareh, M.F., Jhanjhi, N.Z., AlSuwailem, M.: Smart traffic management system for metropolitan cities of kingdom using cutting edge technologies. J. Adv. Transp. 2022, 4687319 (2022)
Lee, W.-H., Chiu, C-Y.: Design and implementation of a smart traffic signal control system for smart city applications. Sensors 20(2) (2020)
Moumen, N.R.I., Abouchabaka, J.: Adaptive traffic lights based on traffic flow prediction using machine learning models. Int. J. Electr. Comput. Eng. (IJECE) 13(5), 5813–5823 (2013)
George, S., Santra, A.K.: Traffic prediction using multifaceted techniques: A survey. Wirel. Pers. Commun. 115(2), 1047–1106 (2020)
Navarro-Espinoza, A., López-Bonilla, O.R., García-Guerrero, E.E., Tlelo-Cuautle, E., López-Mancilla, D., Hernández-Mejía, C., Inzunza-González, E.: Traffic flow prediction for smart traffic lights using machine learning algorithms. Technologies 10(1) (2022)
Javaid, S., Sufian, A., Pervaiz, S., Tanveer, M.: Smart traffic management system using internet of things. In: 2018 20th International Conference on Advanced Communication Technology (ICACT), pp. 1–1, (2018)
Bhate, S.V., Kulkarni, P.V., Lagad, S.D., Shinde, M.D., Patil, S.: Iot based intelligent traffic signal system for emergency vehicles. In: 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), pp. 788–793 (2018)
Trnovszky, T., Sykora, P., Hudec, R.: Comparison of background subtraction methods on near infra-red spectrum video sequences. Procedia Eng. 192, 887–892 (2017)
Maheria, U., Fancy, C., Anand, M.: Iot-based traffic congestion and safety management with street light control system. In: Hemanth, D.J., Vadivu, G., Sangeetha, M., Balas, V.E. (eds.) Artificial Intelligence Techniques for Advanced Computing Applications, pp. 495–501. Springer, Singapore (2021)
Younes, M.B., Boukerche, A.: An efficient dynamic traffic light scheduling algorithm considering emergency vehicles for intelligent transportation systems. Wirel. Netw. 24(7), 2451–2463 (2018)
Zuo, J., Jia, Z., Yang, J., Kasabov, N.: Moving target detection based on improved gaussian mixture background subtraction in video images. IEEE Access 7, 1–1 (2019)
Song, Z.: Background subtraction using infinite asymmetric gaussian mixture models with simultaneous feature selection. IET Image Process. 14(11), 2321–2332 (2020)
KNN algorithm. https://towardsdatascience.com/machine-learning-basics-with-the-k-nearest-neighbors-algorithm
Tzutalin. labelimg. git code (2015). https://github.com/tzutalin/labelImg
Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. https://pjreddie.com/media/files/papers/YOLOv3.pdf
Redmon, J., Farhadi, A.: Yolo9000: Better, faster, stronger. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6517–6525 (2016)
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R.G, S., C, H., Dipesh, R. et al. Density Based Real-time Smart Traffic Management System along with Emergency Vehicle Detection for Smart Cities. Int. J. ITS Res. 22, 328–338 (2024). https://doi.org/10.1007/s13177-024-00400-9
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DOI: https://doi.org/10.1007/s13177-024-00400-9