Articles Open Access
Artificial Intelligence Techniques for Smart Urban Traffic Monitoring and Management Systems
Department of Computer Science and Engineering, SRM Institute of Science and Technology (SRMIST), Chennai, India
Abstract
Artificial Intelligence (AI) has emerged as a transformative technology in the development of smart urban traffic monitoring and management systems. Rapid urbanization and increasing vehicle density have created significant challenges such as traffic congestion, road accidents, fuel wastage, and environmental pollution. Traditional traffic management approaches are often unable to respond effectively to dynamic traffic conditions. AI-based systems utilize technologies such as machine learning, deep learning, computer vision, neural networks, and the Internet of Things (IoT) to analyze real-time traffic data and optimize transportation operations. These intelligent systems can monitor traffic flow, detect incidents, predict congestion patterns, control traffic signals adaptively, and support autonomous transportation infrastructure. AI-driven traffic management enhances road safety, reduces travel time, minimizes fuel consumption, and improves urban mobility efficiency. Furthermore, smart traffic systems facilitate data-driven decision-making for city administrators and urban planners. Despite these advantages, challenges including high implementation costs, cybersecurity risks, data privacy concerns, and infrastructure limitations remain significant barriers. This study explores various AI techniques applied in smart urban traffic monitoring and management systems, reviews existing literature, examines research methodologies, and highlights the advantages and disadvantages of AI-based traffic solutions for sustainable and intelligent urban transportation development.
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Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 2 |
| Number | 3 |
| Pages | 1811-1818 |
| Published | 2018-05-05 |
| Issue | Vol. 2 No. 3 (2018): International Research Journal of Innovative Engineering (IRJIE) |
References
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35. Mathew, A. R. (2017). Cloud Technology and the Challenges for Forensics Investigators. DEStech Transactions on Computer Science and Engineering.
36. Begum, R. S., & Sugumar, R. (2015). A Conceptual Comparison of Artificial Bee Colony and Particle Swarm Optimization.
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38. Singh, T. J., & Sugumar, R. (2012, December). An extensibility of THEMIS billing system for the cloud computing environment. In 2012 Fourth International Conference on Advanced Computing (ICoAC) (pp. 1-6). IEEE.
39. Sugumar, R., Rengarajan, A., & Jayakumar, C. (2018). Trust based authentication technique for cluster based vehicular ad hoc networks (VANET). Wireless Networks, 24(2), 373-382.
40. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian Journal of Science and Technology, 8(35), 1-5.
41. Priya, P. S., & Sugumar, R. (2014). Multi Keyword Searching Techniques over Encrypted Cloud Data. In IJSR.
42. Chiranjeevi, K. G., Latha, R., & Kumar, S. S. (2016). Enlarge Storing Concept in an Efficient Handoff Allocation during Travel by Time Based Algorithm. Indian Journal of Science and Technology, 9, 40.
43. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
44. Sugumar, R. (2014). A technique to stock market prediction using fuzzy clustering and artificial neural networks.
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2. Chan, Y. (2017). Telecommunications- and information technology–inspired analyses: Review of an intelligent transportation systems experience. Transportation Research Record, 2658(1), 52–60.
3. Djahel, S., Doolan, R., Muntean, G. M., & Murphy, J. (2015). A communications-oriented perspective on traffic management systems for smart cities. IEEE Communications Surveys & Tutorials, 17(1), 125–151.
4. Goel, D., Pahal, N., Jain, P., & Chaudhury, S. (2017). An ontology-driven context aware framework for smart traffic monitoring. In IEEE International Symposium on Technologies for Smart Cities (pp. 1–6).
5. Karagiannis, G., Altintas, O., Ekici, E., et al. (2011). Vehicular networking: A survey and tutorial on requirements, architectures, challenges, standards and solutions. IEEE Communications Surveys & Tutorials, 13(4), 584–616.
6. Lee, U., & Gerla, M. (2010). A survey of urban vehicular sensing platforms. Computer Networks, 54(4), 527–544.
7. Mehboob, F., Abbas, M., Rauf, A., Khan, S. A., & Jiang, R. (2017). Video surveillance-based intelligent traffic management in smart cities. IntechOpen.
8. Nellore, K., & Hancke, G. P. (2016). A survey on urban traffic management system using wireless sensor networks. Sensors, 16(2), 157.
9. Sharma, A., Chaki, R., & Bhattacharya, U. (2011). Applications of wireless sensor networks in intelligent transportation systems: A review. International Conference on Electronics Computer Technology, 1–5.
10. Souza, A. M., Brennand, C. A. R. L., Yokoyama, R. S., Donato, E. A., Madeira, E. R. M., & Villas, L. A. (2017). Traffic management systems: A classification, review, challenges, and future perspectives. International Journal of Distributed Sensor Networks, 13(4), 1–25.
11. Vasirani, M., & Ossowski, S. (2014). A market-inspired approach for intersection management in urban road traffic networks. Journal of Artificial Intelligence Research, 51, 621–659.
12. Younis, O. M., & Moayeri, N. (2017). Dynamic traffic light control at road intersections using cyber-physical systems. IEEE Internet of Things Journal, 4(6), 1–10.
13. Kafi, M. A., Challal, Y., Djenouri, D., et al. (2012). A study of wireless sensor network architectures for traffic light monitoring. Procedia Computer Science, 10, 1074–1080.
14. Khanafer, M., Guennoun, M., & Mouftah, H. T. (2009). Wireless sensor network architectures for intelligent transportation systems. NTMS Conference Proceedings, 1–8.
15. Meneguette, R. I., Boukerche, A., Villas, L. A., & Ueyama, J. (2015). UCONDES: An urban congestion detection system based on neural networks. IEEE Globecom Workshops, 1–6.
16. Santhosh, K. K., Dogra, D. P., & Roy, P. P. (2017). Real-time moving object classification for road traffic management in smart cities. IEEE Symposium on Technologies for Smart Cities, 1–6.
17. Arbi, Z., Belkahla Driss, O., & Sbai, M. K. (2017). A multi-agent system for monitoring and regulating road traffic in a smart city. International Conference on Smart, Monitored and Controlled Cities, 17–22.
18. De Souza, A. M., Yokoyama, R., Boukerche, A., et al. (2016). ICARUS: Improvement of traffic condition through an alerting and rerouting system. Computer Networks, 110, 118–132.
19. US Department of Transportation. (2015). Traffic congestion and reliability: Trends and advanced strategies for congestion mitigation. Washington, DC: USDOT.
20. Zamora-Izquierdo, M. A., Santa, J., Martínez, J. A., et al. (2014). Smart traffic management systems for urban environments. IEEE Intelligent Transportation Systems Magazine, 6(3), 23–35.
21. Mathew, A., & Asari, V. K. (2014, March). Rotation-invariant histogram features for threat object detection on pipeline right-of-way. In Video Surveillance and Transportation Imaging Applications 2014 (Vol. 9026, pp. 19-26). SPIE.
22. Sugumar, R., Rengarajan, A., & Jayakumar, C. (2015). Design a Weight Based Sorting Distortion Algorithm for Privacy Preserving Data Mining. Middle-East Journal of Scientific Research, 23(3), 405-412.
23. Karthika, V., Brijet, Z., & Bharathi, N. (2012). Design of optimal controller for fluid catalytic cracking unit. Procedia Engineering, 38, 1150-1160.
24. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
25. Sugumar, R. B., & Anandharaj, K. (2016). Assessment of Bacterial Load in the Fresh Water Lake System of Tamil Nadu. Interl J Curr Microbiol App Sci, 5(6), 236-246.
26. Rengarajan, R. S. A. (2016). Secure verification technique for defending IP spoofing attacks.
27. Mathew, A. R., & Al Hajj, A. (2017). Secure communications on IoT and big data. Indian Journal of Science and Technology, 10(11).
28. Alex, A. T., Asari, V. K., & Mathew, A. (2012, October). Gradient feature matching for expression invariant face recognition using single reference image. In 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 851-856). IEEE.
29. Sasidevi, J., Sugumar, R., & Priya, P. S. (2015). New-Multi-Phase Distribution Network Intrusion Detection. International Journal, 5(3).
30. Mathew, A., & Asari, V. K. (2013, March). Tracking small targets in wide area motion imagery data. In Video Surveillance and Transportation Imaging Applications (Vol. 8663, pp. 69-78). SPIE.
31. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
32. Alex, A. T., Asari, V. K., & Mathew, A. (2013, March). Gradient feature matching for in-plane rotation invariant face sketch recognition. In Image Processing: Machine Vision Applications VI (Vol. 8661, pp. 46-58). SPIE.
33. Natarajan, R., & Sugumar, R. (2014). A survey on attacks in web usage mining. International Journal of Innovative Research in Computer and Communication Engineering, 2(5), 4470-5.
34. Satyanarayana, D., Mathew, A. R., & Sathyashree, S. (2016). An Architecture for Wireless Communication Systems using Li-Fi technology. In 8th International Conference on Latest Trends in Engineering and Technology (ICLTET’2016) (pp. 37-41).
35. Mathew, A. R. (2017). Cloud Technology and the Challenges for Forensics Investigators. DEStech Transactions on Computer Science and Engineering.
36. Begum, R. S., & Sugumar, R. (2015). A Conceptual Comparison of Artificial Bee Colony and Particle Swarm Optimization.
37. Soundappan, S. J., & Sugumar, R. (2016). Optimal knowledge extraction technique based on hybridisation of improved artificial bee colony algorithm and cuckoo search algorithm. International Journal of Business Intelligence and Data Mining, 11(4), 338-356.
38. Singh, T. J., & Sugumar, R. (2012, December). An extensibility of THEMIS billing system for the cloud computing environment. In 2012 Fourth International Conference on Advanced Computing (ICoAC) (pp. 1-6). IEEE.
39. Sugumar, R., Rengarajan, A., & Jayakumar, C. (2018). Trust based authentication technique for cluster based vehicular ad hoc networks (VANET). Wireless Networks, 24(2), 373-382.
40. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian Journal of Science and Technology, 8(35), 1-5.
41. Priya, P. S., & Sugumar, R. (2014). Multi Keyword Searching Techniques over Encrypted Cloud Data. In IJSR.
42. Chiranjeevi, K. G., Latha, R., & Kumar, S. S. (2016). Enlarge Storing Concept in an Efficient Handoff Allocation during Travel by Time Based Algorithm. Indian Journal of Science and Technology, 9, 40.
43. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
44. Sugumar, R. (2014). A technique to stock market prediction using fuzzy clustering and artificial neural networks.
45. Brijet, Z., Kumar, B. S., & Bharathi, N. (2017). Vehicle anti-theft system using fingerprint recognition technique. Open Academic Journal of Advanced Science and Technology, 1(1), 36-41.
46. Z. Brijet, N. Bharathi, G. Shanmugapriya. (2015). Design of Model Predictive Controller for Fluid Catalytic Cracking Unit. Fluid-IJCTA, 8(5), 1917-1926.
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