Journal: Journal of Machine Learning and Deep Learning (JMLDL), Volume:2, Issue:1, Pages: 1-10 Download pdf
Authors: Adama Coulibaly, Ibrahima Ngom , Jean Marie Dembélé , Kevin Leahy , Ousmane Sadio, Marc Momar Tall, Ibrahima Diagne, Moustapha Ndiaye
Date: 05-2025
Abstract: Urban transport infrastructures are continuously faced with challenges such as traffic congestion, accidents, pollution, wear and tear. Traditional traffic management technologies (such as traffic lights and static cameras) have shown their limitations, whereas Intelligent Transportation Systems (ITS) integrating the Internet of Things, drones, and Artificial Intelligence offer powerful tools to proactively and efficiently address these issues. ITS aim to enhance the efficiency, safety, and sustainability of transportation networks by leveraging advanced technologies. In this context, drones and AI play a central role in tackling the growing challenges of urbanization, increasing traffic, and the need for smarter infrastructures. This work reviews the contributions of Unmanned Aerial Vehicles and Artificial Intelligence in Intelligent Transportation Systems. A literature review is conducted to identify similar studies and assess the state of research on the topic. An analysis of the various applications of IoT, drones, and AI provides an evaluation of their contributions to ITS. Despite significant challenges, drones and AI have revolutionized the field of intelligent transportation.
Keywords: Artificial Intelligence, Contribution, Intelligent Transportation Systems, Review, UAV.
References:
[1] L. Jian, Z. Li, W. Wu, A. Ahmad, and G. Jeon, “Combining Unmanned Aerial Vehicles With Artificial Intelligence Technology for Traffic- Congestion Recognition,” IEEE Consumer Electronics Magazine, vol. 19, no. 2162-2248, pp. 81–86, 2019. DOI: 10.1109/MCE. 2019.2892286.
[2] H. Wang and Y. Yu, “A Comparative Study of State-of-the-Art Deep Learning Algorithms for Vehicle Detection,” IEEE Intelligent transportation systems magazine, vol. 19, no. 1939-1390, pp. 1–14, 2019. DOI: 10.1109/MITS.2019.2903518.
[3] Leduc, G.: Road traffic data: Collection methods and applications. Working Papers on Energy, Transport and Climate Change, vol. 1, no. 55 (2008),”
[4] K. Nellore and G. P. Hancke, “A survey on urban traffic management system using wireless sensor networks,” Sensors, vol. 16, no. 157, 2016, ISSN: 1424-8220. DOI: 10.3390/s16020157.
[5] D. Oladimeji, K. Gupta, N. A. Kose, K. Gundogan Kose, L. Ge, and F. Liang, “Smart transportation: An overview of technologies and applications,” Sensors, vol. 23, p. 3880, Apr. 2023. DOI: 10.3390/s23083880.
[6] V. Chand and J. Karthikeyan, “Survey on the role of IoT in intelligent transportation system,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 11, pp. 936–941, Sep. 2018. DOI: 10.11591/ijeecs. v11.i3. pp 936-941.
[7] K. Jurczenia and J. Rak, “A survey of vehicular network systems for road traffic management,” IEEE access: practical innovations, open solutions, vol. 10, pp. 42 365–42 385, 2022, ISSN: 2169-3536. DOI: 10. 1109/ACCESS.2022.3168354.
[8] BISIO, HALEEM, and SCIARRON E, “A Systematic Review of Drone Based Road Traffic Monitoring System,” IEEE, vol. 10, pp. 101 537–101 555, 2022. DOI: 10.1109/ACCESS.2022.3207282.
[9] E. Butila and R. Boboc, “Urban traffic monitoring and analysis using unmanned aerial vehicles (uavs): A systematic literature review,” Remote Sensing, vol. 14, Jan. 2022. DOI: 10.3390/rs14030620.
[10] R. Santhiya and C. GeethaPriya, “Machine learning techniques for intelligent transportation systemsan overview,” Jul. 2021, pp. 1–7. DOI: 10. 1109 /ICCCNT51525.2021.9579970.
[11] Azad, T. Atkison, and A. F. M. S. Shah, “A Review on Machine Learning in Intelligent Transportation Systems Applications,” The Open Transportation Journal, vol. 18, Sep. 2024. DOI: 10. 2174 /0126671212330496240821114216.
[12] S. Srivastava, S. Narayan, and S. Mittal, “A survey of deep learning techniques for vehicle detection from UAV images,” Journal of Systems Architecture, vol. 117, p. 102 152, 2021, ISSN: 1383-7621. DOI: 10.1016 / j. sysarc.2021.102152.
[13] B. Coifman, M. McCord, R. G. Mishalani, and K. Redmill, “Surface transportation surveillance from unmanned aerial vehicles,” in Proc. of the 83rd Annual Meeting of the Transportation Research Board, vol. 28, 2004.
[14] S. Sharma and S. Sebastian, “IoT based car accident detection and notification algorithm for general road accidents,” International Journal of Electrical and Computer Engineering (IJECE), vol. 9, no. 5, pp. 4020–4026, Oct. 2019, ISSN: 2722-2578. DOI: 10.11591/ijece. v9i5.pp4020-4026.
[15] E. Hache, C. Ternel, and L. Aissaoui, Système de transport intelligent et mobilité 3.0 : définition, enjeux et acteurs, Oct. 2017. (Visited on 01/01/2025).
[16] G. Nasserddine, A. Arid, and M. Nassereddine, “Internet of Things in Intelligent Transportation Systems,” in Jun. 2024, pp. 291–314, ISBN: 978-3-031-58387-2. DOI: 10.1007/978-3-031-58388-9 10.
[17] B. Zakaria, B. Fateh, and B. khelifa, “Les systèmes de transport intelligent (STI),” in Journées Portes Ouvertes Sur La Faculté Des Sciences Exactes JFSE 2017, Bechar, Algérie, 2017.
[18] D. Sharma, S. Sharma, A. Sahu, and A. Sharma, “Internet of things (IoT) and smart cities,” International Research Journal on Advanced Engineering and Management (IRJAEM), vol. 2, pp. 2526–2531, Aug. 2024. DOI: 10.47392/IRJAEM.2024.0365.
[19] A. Coulibaly, I. Ngom, J. M. Dembélé, et al., “Real-time Road Accident Scene Recognition Using Computer Vision Applied to Drone Imagery,” in 2024 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES), 2024, pp. 1–6. DOI: 10.1109/ICARES64249.2024.10768100.
[20] A. Haoui, R. Kavaler, and P. Varaiya, “Wireless magnetic sensors for traffic surveillance,” Transportation Research Part C: Emerging Technologies, vol. 16, no. 3, pp. 294–306, 2008, ISSN: 0968090X. DOI: 10.1016/j. trc.2007.10.004. (Visited on 08/13/2022).
[21] S. Joshi, Smart traffic management system, Sep. 2024. DOI: 10.13140/RG.2.2.19806.86080.
[22] N. Allali, Z. Chaouch, and M. Tamali, “Dashboard of intelligent transportation system (ITS) using mobile agents’ strategy on notification authentication process,” International Journal of Electrical and Computer Engineering (IJECE), vol. 9, no. 1, pp. 621–628, 2019, ISSN: 2722-2578. DOI: 10.11591/ijece. v9i1. pp 621-628.
[23] M. Fogue, P. Garrido, F. J. Martinez, J. C. Cano, C. T. Calafate, and P. Manzoni, “Automatic accident detection: Assistance through communication technologies and vehicles,” IEEE Vehicular Technology Magazine, vol. 7, no. 3, pp. 90–100, 2012, ISSN: 15566072. DOI : 10.1109/MVT.2012.2203877.
[24] F. Filali, Les réseaux VANET Vehicular Ad Hoc Networks, 2006. (Visited on 01/03/2025).
[25] G. Vadivel, M. J. M. Hussain, and S. V. Tresa Sangeetha, “SMART TRANSPORTATION SYSTEMS: IOT-CONNECTED WIRELESS SENSOR NETWORKS FOR TRAFFIC CONGESTION MANAGEMENT,” IJASIS, vol. 9, no. 1, pp. 40–49, Jun. 2023. DOI: 10.29284/ijasis.9.1.2023.40-49.
[26] V. Astarita, D. Festa, and V. Giofr´e, “Mobile systems applied to traffic management and safety: A state of the art,” Procedia Computer Science, vol. 134, pp. 407–414, Jul. 2018. DOI: 10.1016/j.procs.2018.07.191.
[27] A. More, M. Mahajan, and H. Gholap, “AI and IOT Based Road Accident Detection and Reporting System,” ijraset, DOI: 10.22214/ijraset.2023.48904.
[28] E. Nasr, E. Kfoury, and D. Khoury, “An IoT approach to vehicle accident detection, reporting, and navigation,” in 2016 IEEE International Multidisciplinary Conference on Engineering Technology (IMCET), Nov. 2016, pp. 231–236. DOI: 10.1109/IMCET.2016.7777457.
[29] A. Rezowana, M. J. H. Khandaker, and A. Shakil, “RFID based Smart Transportation System with Android Application,” in Proceedings of the Second International Conference on Innovative Mechanisms for Industry Applications, ICIMIA, 2020, pp. 614–619, ISBN: 978-1-7281-4167-1.
[30] K. Micko, P. Papcun, and I. Zolotov´a, “Review of IoT sensor systems used for monitoring the road infrastructure,” Sensors, vol. 23, p. 4469, May 2023. DOI: 10.3390/s23094469.
[31] H. A. Najada and I. Mahgoub, “Anticipation and Alert System of Congestion and Accidents in VANET Using Big Data Analysis for Intelligent Transportation Systems,” IEEE, pp. 1–8, 2016.
[32] D. P. Chowdhury, P. Bala, D. Addy, S. Giri, and A. R. Chaudhuri, “RFID and Android Based Smart Ticketing and Destination Announcement System,” IEEE, no. 978-1-5090-2029-4/16, 2016.
[33] H. D. Chon, S. Jun, H. Jung, and S. Won, “Using RFID for Accurate Positioning,” Journal of Global Positioning Systems, vol. 3, no. 1-2: 32-39, pp. 32–39, Feb. 2005.
[34] A. Shaik, N. Bowen, J. Bole, et al., “Smart car: An IoT based accident detection system,” in 2018 IEEE Global Conference on Internet of Things (GCIoT), Dec. 2018, pp. 1–5. DOI: 10.1109/GCIoT.2018.8620131.
[35] W. Etaiwi and S. Idwan, “Traffic management systems: A survey of current solutions and emerging technologies,” Journal of Computational Social Science, vol. 8, Nov. 2024. DOI: 10.1007/s42001-024-00340-0.
[36] X. Xiao, Z. Peng, Y. Lin, et al., “Parking prediction in smart cities: A survey,” IEEE Transactions on Intelligent Transportation Systems, vol. PP, pp. 1–25, Oct. 2023. DOI: 10.1109/TITS.2023.3279024.
[37] M. Heimberger, J. Horgan, C. Hughes, J. McDonald, and S. Yogamani, “Computer vision in automated parking systems: Design, implementation and challenges,” Image and Vision Computing, vol. 68, pp. 88–101, Dec. 2017, ISSN: 02628856. DOI: 10.1016/j.imavis.2017.07. 002. arXiv: 2104.12537. (Visited on 08/13/2022).
[38] F. I. R. Edward Sehar, B. Mahadevan, L. Pushparaj, and D. Thiyaharajan, “Traffic management in smart cities using unmanned aerial vehicles,” in May 2024, pp. 218–247, ISBN: 979-8-3693-2093-8. DOI : 10.4018/979- 8-3693-2093-8.ch013.
[39] A. STRAUSS, V´ehicule Autonome, Apr. 2019. (Visited on 01/04/2025).
[40] N. Monot, “Des systèmes d’aide à la conduite au véhicule autonome connecté,” Ph.D. dissertation, Jul. 2019.
[41] O. Aouedi, H. Vu, A. Sacco, et al., A survey on intelligent internet of things: Applications, security, privacy, and future directions, Jun. 2024. DOI: 10.48550/arXiv. 2406.03820.
[42] B. Coifman, M. McCord, R. G. Mishalani, M. Iswalt, and Y. Ji, “Roadway traffic monitoring from an unmanned aerial vehicle,” in IEE Proceedings-Intelligent Transport Systems, vol. 153, IET, 2006, pp. 11–20.
[43] L. Wang, F. Chen, and H. Yin, “Detecting and tracking vehicles in traffic by unmanned aerial vehicles,” Automation in construction, vol. 72, pp. 294–308, 2016.
[44] N. V. Kim and M. A. Chervonenkis, “Situation control of unmanned aerial vehicles for road traffic monitoring,” Modern Appl. Sci., vol. 9, 2015.
[45] T. Yuan, W. Neto, C. Esteve Rothenberg, K. Obraczka, C. Barakat, and T. Turletti, “Machine learning for next-generation intelligent transportation systems: A survey,” Transactions on Emerging Telecommunications Technologies, vol. 33, Apr. 2022. DOI: 10.1002/ett.4427.
[46] B. K. M, A. Basit, K. MB, G. R, and K. SM, “Road accident detection using machine learning,” in 2021 International Conference on System, Computation, Automation and Networking (ICSCAN), Jul. 2021, pp. 1–5. DOI: 10.1109/ICSCAN53069.2021.9526546.
[47] A. Coulibaly, I. Ngom, J. M. Dembélé, O. Sadio, M. M. Tall, and I. Diagne, “Detection of asphalt roads degradation using Deep Learning applied to Unmanned Aerial Vehicle imagery,” in 2023 International Conference on Intelligent Computing and next Generation networks (ICNGN), Nov. 2023, pp. 1–5. DOI: 10.1109/ICNGN59831.2023.10396763.
[48] A. Azhar, S. Rubab, M. M. Khan, et al., “Detection and prediction of traffic accidents using deep learning techniques,” Cluster Computing, vol. 26, no. 1, pp. 477–493, Feb. 2023, ISSN: 1573-7543. DOI: 10.1007/s10586-021-03502-1.
[49] M. R. Septian, A. Masitoh, and I. Sari, “Implementation of deep learning algorithm for vehicle count monitoring system,” TEPIAN, vol. 5, pp. 143–149, Dec. 2024. DOI: 10.51967/tepian.v5i4.3213.
[50] F. Zantalis, G. Koulouras, S. Karabetsos, and D. Kandris, “Future internet a review of machine learning and IoT in smart transportation,” Future Internet, vol. 11, Apr. 2019. DOI: 10.3390/fi11040094.
[51] A. Sakhuja, “Intelligent Traffic Management System using Computer Vision and Machine Learning,” Innovative Research Thoughts, vol. 9, pp. 1–10, Dec. 2023. DOI: 10.36676/irt.2023-v9i5-001.
[52] S. Sutikno, A. Sugiharto, R. Kusumaningrum, and H. Wibawa, “Improved car detection performance on highways based on YOLOv8,” Bulletin of Electrical Engineering and Informatics, vol. 13, pp. 3526–3533, Oct. 2024. DOI: 10.11591/eei.v13i5.8031.
[53] G. ¨Ozt¨urk, O. Eldogan, and R. Koker, “Computer Vision-Based Lane Detection and Detection of Vehicle, Traffic Sign, Pedestrian Using YOLOv5,” Sakarya University Journal of Science, vol. 28, Jan. 2024. DOI: 10.16984/saufenbilder.1393307.
[54] B. Medina-Salgado, E. S´anchez-DelaCruz, P. Pozos-Parra, and J. Sierra, “Urban traffic flow prediction techniques: A review,” Sustainable Computing: Informatics and Systems, vol. 35, p. 100 739, Apr. 2022. DOI: 10.1016/j.suscom.2022.100739.
[55] L. Ciampi, G. Amato, F. Falchi, C. Gennaro, and F. Rabitti, “Counting vehicles with cameras,” Jun. 2018.
[56] A. Akoushideh, S. Sadat, and A. Shahbahrami, “Counting vehicles types using deep learning algorithm in video surveillance systems,” Multimedia Tools and Applications, pp. 1–24, Dec. 2024. DOI: 10.1007/s11042-024-20530-2.
[57] H. Zhang, M. Liptrott, N. Bessis, and J. Cheng, “Real-time Traffic Analysis Using Deep Learning Techniques And UAV Based Video,” IEEE, pp. 1–5
[58] R. Ke, “Real-Time Bidirectional Traffic Flow Parameter Estimation from Aerial Videos,” IEEE Transactions on Intelligent Transportation Systems, pp. 1–12, 2016.
[59] G. Zheng, “Deep learning models for traffic prediction in urban transport networks,” Ph.D. dissertation, Jun. 2022.
[60] L. Li, X. Qu, J. Zhang, Y. Wang, and B. Ran, “Traffic speed prediction for intelligent transportation system based on a deep feature fusion model,” Journal of Intelligent Transportation Systems, vol. 23, pp. 1–12, Mar. 2019. DOI: 10.1080/15472450.2019.1583965.
[61] A. Essien, I. Petrounias, P. Sampaio, and S. Sampaio, “Improving urban traffic speed prediction using data source fusion and deep learning,” Feb. 2019, pp. 1–8. DOI: 10.1109/BIGCOMP.2019.8679231.
[62] W. Utomo, P. W. Bhaskara, A. Kurniawan, S. Juniastuti, and E. M. Yuniarno, “Traffic Congestion Detection Using Fixed-Wing Unmanned Aerial Vehicle (UAV) Video Streaming Based on Deep Learning,” 2020 International Conference on Computer Engineering, Network and Intelligent Multimedia (CENIM 2020), pp. 234–238, 2021.
[63] J. Kurniawan, S. G. Syahra, C. K. Dewa, and Afiahayati, “Traffic Congestion Detection: Learning from CCTV Monitoring Images using Convolutional Neural Network,” vol. 144, pp. 291–297, 2018.
[64] A. Dogra and J. Kaur, “Moving towards smart transportation with machine learning and Internet of Things (IoT): A review,” Journal of Smart Environments and Green Computing, vol. 2, Feb. 2022. DOI: 10.20517/jsegc.2021.09.
[65] N. Chanduja, “IOT based smart parking and traffic management system for modern cities,” International Journal for Research in Applied Science and Engineering Technology, vol. 12, pp. 492–498, Dec. 2024. DOI: 10.22214/ijraset.2024.65829.
[66] C. Ruseruka, J. Mwakalonge, G. Comert, S. Siuhi, and J. Perkins, “Road Condition Monitoring Using Vehicle Built-in Cameras and GPS Sensors: A Deep Learning Approach,” Vehicles MPDI, vol. 5, pp. 931–948, 2023. DOI: 10.3390/vehicles5030051.
[67] Z. hui, X. yaohua, M. lu, and F. Jiansheng, “Vision-based real-time traffic accident detection,” in Proceeding of the 11th World Congress on Intelligent Control and Automation, Jun. 2014, pp. 1035–1038. DOI: 10.1109/WCICA.2014.7052859.
[68] H. Ghahremannezhad, H. Shi, and C. Liu, “Real-time accident detection in traffic surveillance using deep learning,” in 2022 IEEE International Conference on Imaging Systems and Techniques (IST), Jun. 2022, pp. 1–6. DOI : 10.1109/IST55454.2022.9827736.
[69] A. B. Parsa, H. Taghipour, S. Derrible, and A. (Mohammadian, “Real-time accident detection: Coping with imbalanced data,” Accident Analysis & Prevention, vol. 129, pp. 202–210, 2019, ISSN: 0001-4575. DOI: 10.1016/j.aap.2019.05.014.
[70] U. K. Singh, S. Yadav, S. Joshi, S. Singh, and K. Jayavel, “RescueAlert-an accident detection and rescue mechanism,” International Journal of Electrical and Computer Engineering (IJECE), vol. 11, no. 4, pp. 3356–3364, Aug. 2021, ISSN: 2722-2578. DOI: 10.11591/ijece. v11i4. pp 3356-3364.
[71] Gomathy, K. Rohan, B. M. K. Reddy, and V. Geetha, “Accident detection and alert system,” Journal of Engineering, Computing & Architecture, vol. 12, no. 3, pp. 32–43, 2022.
[72] S. Ghosh, S. J. Sunny, and R. Roney, “Accident detection using convolutional neural networks,” in 2019 International Conference on Data Science and Communication (IconDSC), Mar. 2019, pp. 1–6. DOI : 10.1109/IconDSC.2019.8816881.
[73] S. K Santhi, “Accident detection using convolutional neural networks,” Journal of Emerging Technologies and Innovative Research, vol. 10, no. 4, pp. 646–649, 2023.
[74] D. K. Yadav, Renu, Ankita, and I. Anjum, “Accident detection using deep learning,” ser. Proceedings – IEEE 2020 2nd International Conference on Advances in Computing, Communication Control and Networking, ICACCCN 2020, Institute of Electrical and Electronics Engineers Inc., Dec. 2020, pp. 232–235, ISBN: 978-1-7281-8337-4. DOI: 10.1109/ICACCCN51052.2020.9362808.
[75] R. Desai, A. Jadhav, S. Sawant, and N. Thakur, “Accident detection using ml and ai techniques,” Eng paper Journal, 2021.
[76] Z. Y. Chan and S. A. Suandi, “City Tracker: Multiple Object Tracking in Urban Mixed Traffic Scenes,” Proceedings of the 2019 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2019, pp. 335–339, Sep. 2019. DOI : 10.1109/ICSIPA45851.2019.8977783. (Visited on 08/13/2022).
[77] L. Wang, F. Chen, and H. Yin, “Detecting and tracking vehicles in traffic by unmanned aerial vehicles,” Automation in Construction, vol. 72, pp. 294–308, 2016. DOI: 10.1016/j.autcon.2016.05.008.
[78] L. M. Fuentes and S. A. Velastin, “People tracking in surveillance applications,” J Image Vision Computer, vol. 24, 2006. DOI: 10.1016/j.imavis.2005.06.006.
[79] U. Khalil, T. Javid, and A. Nasir, “Automatic Road accident detection techniques: A brief survey,” in 2017 International Symposium on Wireless Systems and Networks (ISWSN), Nov. 2017, pp. 1–6. DOI: 10. 1109 /ISWSN.2017.8250025.
[80] G. Leduc et al., “Road traffic data: Collection methods and applications,” Working Papers on Energy, Transport and Climate Change, vol. 1, no. 55, pp. 1–55, 2008.
[81] V. Ha and C. Thuy, “Model predictive control combined reinforcement learning for automatic vehicles applied in intelligent transportation system,” TELKOMNIKA (Telecommunication Computing Electronics and Control), vol. 22, p. 302, Apr. 2024. DOI: 10. 12928 /telkomnika.v22i2.25274.
[82] R. A. Khalil, Z. Safelnasr, N. Yemane, M. Kedir, A. Shafiqurrahman, et N. Saeed, « Advanced learning technologies for intelligent transportation systems: Prospects and challenges », IEEE Open Journal of Vehicular Technology, 2024.
[83] M. Elassy, M. Al-Hattab, M. Takruri, et S. Badawi, « Intelligent transportation systems for sustainable smart cities », Transportation Engineering, p. 100252, 2024.
[84] M. D. Tezerjani, M. Khoshnazar, M. Tangestanizadeh, A. Kiani, et Q. Yang, « A Survey on Reinforcement Learning Applications in SLAM », Journal of Machine Learning and Deep Learning (JMLDL), p. 20‑31, décembre 2024.
[85] Y. Shereen, « Optimization of Neural Network Weights with Nature Inspired Algorithm », Journal of Machine Learning and Deep Learning (JMLDL), p. 1‑9, août 2024.