Design of a Traffic Lane Congestion Monitoring and Control System using YOLO Neural Network Approach

Timothy Oluwapelumi Adeyemi, Taye Hassan Salami

Abstract


Considering the most essential component of the traffic system, the traffic signal; this study presents a traffic lane congestion monitoring and control system with the assistance of image processing. The research uses YOLO (You Only Look Once) algorithm to detect the presence of different classes of vehicles, and then determine the response of the different classes of vehicles to a defined traffic condition. The algorithm achieves this by using a dedicated signal switching algorithm to have a considerable high accuracy even at varying resolutions. The trained model had an entire system accuracy of 86%. The algorithm also has a system sensitivity of 93% and a precision value of 84%. The trained YOLO model had an accuracy of up to 99% for close up high-definition images. The result acquired help in regulating green signal time in traffic intersections by providing a more efficient and accurate signal with response to density.


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S. B. Raheem, W. A. Olawoore, D. P. Olagunju, and E. M. Adeokun, “The Cause , Effect and Possible Solution to Traffic Congestion on Nigeria Road ( A Case Study of Basorun-Akobo Road , Oyo State ),” Int. J. Eng. Sci. Invent., vol. 4, no. October, pp. 0–5, 2015.

J. D. Sanghavi, A. M. Shah, S. S. Rane, and V. Venkataramanan, Smart Traf fi c Density Management System Using Image Processing Á Density Á Image processing Á Ambulance Á MATLAB. Springer Singapore.

S. Sarath and L. R. Deepthi, “Priority Based Real Time Smart Traffic Control System Using Dynamic Background,” Proc. 2018 IEEE Int. Conf. Commun. Signal Process. ICCSP 2018, pp. 620–622, 2018, doi: 10.1109/ICCSP.2018.8524501.

U. E. Prakash, A. Thankappan, K. T. Vishnupriya, and A. A. Balakrishnan, “Density based traffic control system using image processing,” 2018 Int. Conf. Emerg. Trends Innov. Eng. Technol. Res. ICETIETR 2018, pp. 1–4, 2018, doi: 10.1109/ICETIETR.2018.8529111.

S. Informatic and O. Otasowie, "Simulation of an Intelligent Traffic Lights System Embedded Technique", vol. XVI, 2018.

W. Hu, H. Wang, L. Yan, and B. Du, “A swarm intelligent method for traffic light scheduling: application to real urban traffic networks,” Appl. Intell., vol. 44, no. 1, pp. 208–231, 2016, doi: 10.1007/s10489-015-0701-y.

S. G. Pashupatimath, “Different Techniques Used in Traffic Control System : An Introduction,” Int. J. Eng. Res. Technol., vol. 6, no. 03, pp. 1–4, 2018.

B. Zachariah, P. Ayuba, and L. P. Damuut, “Optimization of Traffic Light Control System of an Intersection Using Fuzzy Inference System,” Sci. World J., vol. 12, no. 4, pp. 27–33, 2017.

A. Kaur, M. Garag, and H. Kaur, “International Journal of Advanced Research in Computer Science and Software Engineering Review of Traffic Management Control Techniques,” vol. 7, no. 4, pp. 205–208, 2017.

M. Salehi, I. Sepahvand, and M. Yarahmadi, “TLCSBFL: A Traffic Lights Control System Based on Fuzzy Logic,” Int. J. u- e-Service, Sci. Technol., vol. 8, no. 3, pp. 27–34, 2014, doi: 10.14257/ijunesst.2014.7.3.03.

B. Ghazal, K. Elkhatib, K. Chahine, and M. Kherfan, “Smart traffic light control system,” 2016 3rd Int. Conf. Electr. Electron. Comput. Eng. their Appl. EECEA 2016, no. October 2017, pp. 140–145, 2016, doi: 10.1109/EECEA.2016.7470780.

L. Cruz-Piris, D. Rivera, S. Fernandez, and I. Marsa-Maestre, “Optimized sensor network and multi-agent decision support for smart traffic light management,” Sensors (Switzerland), vol. 18, no. 2, 2018, doi: 10.3390/s18020435.

K. Zaatouri, M. H. Jeridi, and T. Ezzedine, “Adaptive Traffic Light Control System Based on WSN: Algorithm Optimization and Hardware Design,” 2018 26th Int. Conf. Software, Telecommun. Comput. Networks, SoftCOM 2018, pp. 241–245, 2018, doi: 10.23919/SOFTCOM.2018.8555802.

Z. Liu, S. Jiang, P. Zhou, and M. Li, “A Participatory Urban Traffic Monitoring System: The Power of Bus Riders,” IEEE Trans. Intell. Transp. Syst., vol. 18, no. 10, pp. 2851–2864, 2017, doi: 10.1109/TITS.2017.2650215.

L. Cruz-Piris, D. Rivera, I. Marsa-Maestre, E. De la Hoz, and S. Fernandez, “Intelligent Traffic Light Management using Multi-Behavioral Agents,” no. September, pp. 110–117, 2017, doi: 10.4995/jitel2017.2017.6494.

A. Atta, S. Abbas, M. A. Khan, G. Ahmed, and U. Farooq, “An adaptive approach: Smart traffic congestion control system,” J. King Saud Univ. - Comput. Inf. Sci., vol. 32, no. 9, pp. 1012–1019, 2020, doi: 10.1016/j.jksuci.2018.10.011.

Z. Cao, S. Jiang, J. Zhang, and H. Guo, “A Unified Framework for Vehicle Rerouting and Traffic Light Control to Reduce Traffic Congestion,” IEEE Trans. Intell. Transp. Syst., vol. 18, no. 7, pp. 1958–1973, 2017, doi: 10.1109/TITS.2016.2613997.

L. Sumi and V. Ranga, “An IoT-VANET-based traffic management system for emergency vehicles in a smart city,” Adv. Intell. Syst. Comput., vol. 708, no. 2, pp. 23–31, 2018, doi: 10.1007/978-981-10-8636-6_3.

S. S. Kulkarni and D. R. Ade, “Intelligent Traffic Control System Implementation for Traffic Violation Control, Congestion Control and Stolen Vehicle Detection,” Int. J. Recent Contrib. from Eng. Sci. IT, vol. 5, no. 2, p. 57, 2017, doi: 10.3991/ijes.v5i2.7230.

K. Nellore and G. P. Hancke, “Traffic management for emergency vehicle priority based on visual sensing,” Sensors (Switzerland), vol. 16, no. 11, 2016, doi: 10.3390/s16111892.

S. Amir, M. S. Kamal, S. S. Khan, and K. M. A. Salam, “PLC based traffic control system with emergency vehicle detection and management,” 2017 Int. Conf. Intell. Comput. Instrum. Control Technol. ICICICT 2017, vol. 2018-Janua, pp. 1467–1472, 2018, doi: 10.1109/ICICICT1.2017.8342786.

A. Rego et al., “Software Defined Network-based Control System for an Efficient Traffic Management for Emergency Situations in Smart Cities,” Futur. Gener. Comput. Syst., 2018, doi: 10.1016/j.future.2018.05.054.

B. J. Saradha, G. Vijayshri, and T. Subha, “Intelligent traffic signal control system for ambulance using RFID and cloud,” Proc. 2017 2nd Int. Conf. Comput. Commun. Technol. ICCCT 2017, pp. 90–96, 2017, doi: 10.1109/ICCCT2.2017.7972255.

T. Giuffrè, T. Campisi, and G. Tesoriere, “Implications of Adaptive Traffic Light Operations on Pedestrian Safety Implications of Adaptive Traffic Light Operations on Pedestrian Safety,” no. November 2016, 2017, doi: 10.9790/1684-1306045863.

G. Pau, T. Campisi, A. Canale, A. Severino, M. Collotta, and G. Tesoriere, “Smart pedestrian crossing management at traffic light junctions through a fuzzy-based approach,” Futur. Internet, vol. 10, no. 2, 2018, doi: 10.3390/fi10020015.

R. Andronov and E. Leverents, “Calculation of vehicle delay at signal-controlled intersections with adaptive traffic control algorithm,” MATEC Web Conf., vol. 143, 2018, doi: 10.1051/matecconf/201714304008.

K. An, Y. J. Jeong, S. Lee, and D. Seo, “Smart Crossing system using IoT,” 2017 IEEE Int. Conf. Consum. Electron. ICCE 2017, pp. 392–393, 2017, doi: 10.1109/ICCE.2017.7889366.

H. J. Lee, R. Y. C. Kim, and H. S. Son, “Evaluation of a smart traffic light system with an IOT-based connective mechanism,” Information (Japan), vol. 20, no. 2. pp. 953–961, 2017.


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