Enhancing traffic control systems with live video analytics: Issues, challenges, opportunities, and recent problems

Dheeraj Kumar Singh; Prashant Sahatiya; Amit Ganatra

doi:10.59400/issc.v3i1.530

Dheeraj Kumar Singh Department of Information Technology, Parul University, Vadodara 391760, India http://orcid.org/0000-0001-5071-3306
Prashant Sahatiya Centre for Distance and Online Education, Parul University, Vadodara 391760, India
Amit Ganatra Department of Computer Science & Engineering, Parul University, Vadodara 391760, India

Article ID: 530

132 Views, 142 PDF Downloads

DOI: https://doi.org/10.59400/issc.v3i1.530

Keywords: smart transport management; real time traffic monitoring; intelligent traffic analysis; object detection in live camera feed; adaptive traffic control system

Abstract

With the rapid urbanization and increasing number of vehicles on the roads, it has become imperative to develop innovative solutions that can monitor and manage traffic congestion automatically. Traffic congestion harms the economy, environment, and overall quality of life. To address these challenges, smart traffic management systems employ cutting-edge technologies such as live video analytics and sensor-based adaptive traffic control systems. These systems can predict traffic patterns, locate congestion hotspots, and uncover abnormalities contributing to road accidents in real time. However, adopting these technologies for traffic control systems raises important concerns such as robustness and sustainability across different traffic junctions, data integration from multiple sources, and computational feasibility for real-time computation. Therefore, this paper aims to present an overview of the potential benefits and challenges in adapting the latest technologies, including the Internet of Things and machine learning, for sustainable traffic management. Additionally, a case study of a smart city is presented to evaluate an adaptive traffic control system based on live camera feed analytics by leveraging computer vision techniques. The adaptive traffic control system is accurate in vehicle detection and counting. This system is very useful for smart cities where traffic signals need to be automated according to the density of vehicles.

References

[1]Afrin T, Yodo N. A Survey of Road Traffic Congestion Measures towards a Sustainable and Resilient Transportation System. Sustainability. 2020; 12(11): 4660. doi: 10.3390/su12114660

[2]Aleko DR, Djahel S. An Efficient Adaptive Traffic Light Control System for Urban Road Traffic Congestion Reduction in Smart Cities. Information. 2020; 11(2): 119. doi: 10.3390/info11020119

[3]Fattah MdA, Morshed SR, Kafy AA. Insights into the socio-economic impacts of traffic congestion in the port and industrial areas of Chittagong city, Bangladesh. Transportation Engineering. 2022; 9: 100122. doi: 10.1016/j.treng.2022.100122

[4]Chakraborty P, Adu-Gyamfi YO, Poddar S, et al. Traffic Congestion Detection from Camera Images using Deep Convolution Neural Networks. Transportation Research Record: Journal of the Transportation Research Board. 2018; 2672(45): 222-231. doi: 10.1177/0361198118777631

[5]Chin J, Callaghan V, Lam I. Understanding and personalising smart city services using machine learning, The Internet-of-Things and Big Data. In: Proceedings of the 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE); 19-21 June 2017; Edinburgh, UK. pp. 2050-2055. doi: 10.1109/isie.2017.8001570

[6]Kleine Deters J, Zalakeviciute R, Gonzalez M, et al. Modeling PM2.5 Urban Pollution Using Machine Learning and Selected Meteorological Parameters. Journal of Electrical and Computer Engineering. 2017; 2017: 1-14. doi: 10.1155/2017/5106045

[7]Ameer S, Shah MA, Khan A, et al. Comparative Analysis of Machine Learning Techniques for Predicting Air Quality in Smart Cities. IEEE Access. 2019; 7: 128325-128338. doi: 10.1109/access.2019.2925082

[8]Srivastava S, Divekar AV, Anilkumar C, et al. Comparative analysis of deep learning image detection algorithms. Journal of Big Data. 2021; 8(1). doi: 10.1186/s40537-021-00434-w

[9]Bahiru TK, Kumar Singh D, Tessfaw EA. Comparative Study on Data Mining Classification Algorithms for Predicting Road Traffic Accident Severity. In: Proceedings of the 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT); 20-21 April 2018; Coimbatore, India. pp. 1655-1660. doi: 10.1109/icicct.2018.8473265

[10]Athiappan K, Karthik C, Rajalaskshmi M, et al. Identifying Influencing Factors of Road Accidents in Emerging Road Accident Blackspots. Advances in Civil Engineering. 2022; 2022: 1-10. doi: 10.1155/2022/9474323

[11]Katuk N, Wan Abdullah WAN, Sugiharto T, et al. Smart technology: Ecosystem, impacts, challenges and the path forward. Information System and Smart City. 2023; 3(1). doi: 10.59400/issc.v3i1.63

[12]Saleem M, Abbas S, Ghazal TM, et al. Smart cities: Fusion-based intelligent traffic congestion control system for vehicular networks using machine learning techniques. Egyptian Informatics Journal. 2022; 23(3): 417-426. doi: 10.1016/j.eij.2022.03.003

[13]Anwar AHMM, Oakil AT. Smart Transportation Systems in Smart Cities: Practices, Challenges, and Opportunities for Saudi Cities. In: Belaïd F, Arora A (editors). Smart Cities: Social and Environmental Challenges and Opportunities for Local Authorities. Cham: Springer International Publishing; 2024. pp. 315-337.

[14]Oladimeji D, Gupta K, Kose NA, et al. Smart Transportation: An Overview of Technologies and Applications. Sensors. 2023; 23(8): 3880. doi: 10.3390/s23083880

[15]Mahdavinejad MS, Rezvan M, Barekatain M, et al. Machine learning for internet of things data analysis: a survey. Digital Communications and Networks. 2018; 4(3): 161-175. doi: 10.1016/j.dcan.2017.10.002

[16]Sharma P, Raglin A. Iot in smart cities: Exploring information theoretic and deep learning models to improve parking solutions. In: Proceedings of the 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI); 19-23 August 2019; Leicester, UK. pp. 1860-1865.

[17]Aboualmal A. Artificial Intelligence for IoT & Smart Cities. Available online: https://www.itu.int/en/ITU-T/Workshops-and-Seminars/201812/Documents/S3-Pres2-Abdulahhi%20AI%20for%20IoT%20and%20Smart%20City_v3.pdf (accessed on 12 December 2023).

[18]Jan MA, He X, Song H, et al. Editorial: Machine Learning and Big Data Analytics for IoT-Enabled Smart Cities. Mobile Networks and Applications. 2021; 26(1): 156-158. doi: 10.1007/s11036-020-01702-4

[19]Prasad MVD, Kumar EK, Krishna SVNPV, et al. Detection of two wheelers helmet using machine learning. Turkish Journal of Physiotherapy and Rehabilitation. 2021; 32(2): 1-8.

[20]Hilmani A, Maizate A, Hassouni L. Automated Real-Time Intelligent Traffic Control System for Smart Cities Using Wireless Sensor Networks. Wireless Communications and Mobile Computing. 2020; 2020: 1-28. doi: 10.1155/2020/8841893

[21]Rachmadi MF, Al Afif F, Jatmiko W, et al. Adaptive traffic signal control system using camera sensor and embedded system. In: Proceedings of the TENCON 2011 - 2011 IEEE Region 10 Conference; 21-24 November 2011; Bali, Indonesia. pp. 1261-1265. doi: 10.1109/tencon.2011.6129009

[22]Fedorov A, Nikolskaia K, Ivanov S, et al. Traffic flow estimation with data from a video surveillance camera. Journal of Big Data. 2019; 6(1). doi: 10.1186/s40537-019-0234-z

[23]Asha CS, Narasimhadhan AV. Vehicle Counting for Traffic Management System using YOLO and Correlation Filter. In: Proceedings of the 2018 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT); 16-17 March 2018; Bangalore, India. pp. 1-6. doi: 10.1109/conecct.2018.8482380

[24]Iwasaki Y, Misumi M, Nakamiya T. Robust Vehicle Detection under Various Environments to Realize Road Traffic Flow Surveillance Using an Infrared Thermal Camera. The Scientific World Journal. 2015; 2015: 1-11. doi: 10.1155/2015/947272

[25]Terven J, Cordova-Esparza D. A Comprehensive Review of YOLO: From YOLOv1 and Beyond. Available online: https://www.researchgate.net/publication/369760111_A_Comprehensive_Review_of_YOLO_From_YOLOv1_to_YOLOv8_and_Beyond (accessed on 12 December 2023).