Smart traffic management system: A data-driven approach to urban mobility

Authors

https://doi.org/10.22105/sci.v2i3.42

Abstract

Increasing congestion and complexity in urban transportation networks have reduced the effectiveness of static control strategies, highlighting the need for data-driven smart traffic management to mitigate delays, improve safety, and reduce environmental impacts. This paper adopts a review-and-design perspective to articulate a conceptual Smart Traffic Management System (STMS) that aggregates and fuses real-time, multi-source data (e.g., roadside sensors, cameras, and connectivity-based signals). The framework leverages computer vision to extract traffic states and detect incidents, and employs machine-learning analytics for forecasting and decision support, enabling adaptive signal control, incident management, and emergency-vehicle prioritization through an end-to-end pipeline from data ingestion and preprocessing to feature extraction, inference, and control action. The main contribution is a structured STMS architecture and operational workflow that clarifies the required components and decision pathways, and qualitatively demonstrates the system’s potential to outperform static timing by reducing queue build-up and waiting time, stabilizing flow, and improving responsiveness under non-recurrent events. The study is primarily conceptual and does not report quantitative field or simulation-based results; standard ITS performance indicators (e.g., average delay, travel time, queue length, throughput, emissions) and detection metrics (e.g., precision/recall/F1) are not empirically evaluated. Practical effectiveness is also contingent on data quality, sensor coverage, integration with legacy controllers, and security/privacy constraints. Integrating real-time sensing, computer vision, and machine-learning–driven decision support provides a viable foundation for smart-city traffic operations. Future work should prioritize pilot deployments, rigorous quantitative evaluation using established ITS metrics, and robust designs that address noisy data, operational constraints, and governance requirements.

Keywords:

Traffic congestion, Urban mobility, Smart traffic management system, Intelligent transportation system, Real-time data, Sensors, Image processing

References

  1. [1] Goswami, S. A., Padhya, B. P., & Patel, K. D. (2019). Internet of things: Applications, challenges and research issues. 2019 third international conference on I-SMAC (IoT in social, mobile, analytics and cloud)(I-SMAC) (pp. 47-50). IEEE. https://doi.org/10.1109/I-SMAC47947.2019.9032474
  2. [2] Pawar, L., Bajaj, R., Singh, J., & Yadav, V. (2019). Smart city IoT: Smart architectural solution for networking, congestion and heterogeneity. 2019 international conference on intelligent computing and control systems, ICCS 2019 (pp. 124–129). IEEE. https://doi.org/10.1109/ICCS45141.2019.9065688
  3. [3] Melibari, W., Baodhah, H., & Akkari, N. (2023). IoT-based smart cities beyond 2030: Enabling technologies, challenges, and solutions. 2023 1st international conference on advanced innovations in smart cities (ICAISC) (pp. 1–6). IEEE. https://doi.org/10.1109/ICAISC56366.2023.10085126
  4. [4] Szum, K. (2021). IoT-based smart cities: A bibliometric analysis and literature review. Engineering management in production and services, 13(2), 115–136. https://doi.org/10.2478/emj-2021-0017
  5. [5] Boogaard, H., Patton, A. P., Atkinson, R. W., Brook, J. R., Chang, H. H., Crouse, D. L., ... & Forastiere, F. (2022). Long-term exposure to traffic-related air pollution and selected health outcomes: A systematic review and meta-analysis. Environment international, 164, 107262. https://doi.org/10.1016/j.envint.2022.107262
  6. [6] Mohapatra, H., & Rath, A. K. (2020). Fault-tolerant mechanism for wireless sensor network. IET wireless sensor systems, 10(1), 23–30. https://doi.org/10.1049/iet-wss.2019.0106
  7. [7] Lazaridou, D., Michailidis, A., & Mattas, K. (2019). Evaluating the willingness to pay for using recycled water for irrigation. Sustainability, 11(19), 5220. https://doi.org/10.3390/su11195220
  8. [8] Belli, L., Cilfone, A., Davoli, L., Ferrari, G., Adorni, P., Di Nocera, F., … & Bertolotti, E. (2020). IoT-enabled smart sustainable cities: Challenges and approaches. Smart cities, 3(3), 1039–1071. https://doi.org/10.3390/smartcities3030052
  9. [9] Mohapatra, H., & Rath, A. K. (2020). Survey on fault tolerance-based clustering evolution in WSN. IET networks, 9(4), 145–155. https://doi.org/10.1049/iet-net.2019.0155
  10. [10] Robertson, D. I., & Bretherton, R. D. (1991). Optimizing networks of traffic signals in real time-the SCOOT method. IEEE transactions on vehicular technology, 40(1), 11–15. https://doi.org/10.1109/25.69966
  11. [11] Whaiduzzaman, M., Barros, A., Chanda, M., Barman, S., Sultana, T., Rahman, M. S., … & Fidge, C. (2022). A review of emerging technologies for IoT-based smart cities. Sensors, 22(23), 9271. https://doi.org/10.3390/s22239271
  12. [12] Rudenko, O., Liu, Y., Wang, C., & Rahardja, S. (2019). An extensive game-based resource allocation for securing D2D underlay communications. IEEE access, 7, 43052–43062. https://doi.org/10.1109/ACCESS.2019.2905581
  13. [13] Kamruzzaman, M. M., Hossin, M. A., Alruwaili, O., Alanazi, S., Alruwaili, M., Alshammari, N., … & Zaman, R. (2022). IoT-oriented 6G wireless network system for smart cities. Computational intelligence and neuroscience, 2022(1), 1874436. https://doi.org/10.1155/2022/1874436
  14. [14] Pu, W. (2011). Analytic relationships between travel time reliability measures. Transportation research record, 2254(1), 122–130. https://doi.org/10.3141/2254-13
  15. [15] Mohapatra, H., & Rath, A. K. (2018). Fault tolerance through energy balanced cluster formation (EBCF) in WSN. In Smart innovations in communication and computational sciences: proceedings of ICSICCS-2018 (pp. 313-321). Singapore: Springer Singapore. https://doi.org/10.1007/978-981-13-2414-7_29
  16. [16] Hammi, B., Khatoun, R., Zeadally, S., Fayad, A., & Khoukhi, L. (2017). IoT technologies for smart cities. IET networks, 7(1), 1–13. https://doi.org/10.1049/iet-net.2017.0163
  17. [17] Mehmood, Y., Ahmad, F., Yaqoob, I., Adnane, A., Imran, M., & Guizani, S. (2017). Internet-of-things-based smart cities: Recent advances and challenges. IEEE communications magazine, 55(9), 16–24. https://doi.org/10.1109/MCOM.2017.1600514
  18. [18] Ullah, A., Anwar, S. M., Li, J., Nadeem, L., Mahmood, T., Rehman, A., & Saba, T. (2024). Smart cities: The role of internet of things and machine learning in realizing a data-centric smart environment. Complex and intelligent systems, 10(1), 1607–1637. https://doi.org/10.1007/s40747-023-01175-4
  19. [19] Chen, X., Yin, M., Song, M., Zhang, L., & Li, M. (2014). Social welfare maximization of multimodal transportation: Theory, metamodel, and application to Tianjin Ecocity, China. Transportation research record, 2451(1), 36–49. https://doi.org/10.3141/2451-05
  20. [20] Achi, A., Salinesi, C., & Viscusi, G. (2016). Innovation capacity and the role of information systems: A qualitative study. Journal of management analytics, 3(4), 333–360. https://doi.org/10.1080/23270012.2016.1239228
  21. [21] Abdel-Rahman, M., Zia, M., & Alduraibi, M. (2019). Temperature-dependent resistive properties of vanadium pentoxide/vanadium multi-layer thin films for microbolometer & antenna-coupled microbolometer applications. Sensors, 19(6), 1320. https://doi.org/10.3390/s19061320
  22. [22] Ahlmeyer, M., & Chircu, A. M. (2016). Securing the internet of things: A review. Issues in information systems, 17(4), 21–28. https://iacis.org/iis/2016/4_iis_2016_21-28.pdf
  23. [23] Mohapatra, H., & Rath, A. K. (2019). Detection and avoidance of water loss through municipality taps in India by using smart taps and ICT. IET wireless sensor systems, 9(6), 447–457. https://doi.org/10.1049/iet-wss.2019.0081
  24. [24] Mohapatra, H. (2021). Socio-technical challenges in the implementation of smart city. 2021 international conference on innovation and intelligence for informatics, computing, and technologies, 3ict 2021 (pp. 57–62). IEEE. https://doi.org/10.1109/3ICT53449.2021.9581905
  25. [25] Mohapatra, H., & Rath, A. K. (2020). Nub less sensor based smart water tap for preventing water loss at public stand posts. Proceedings of 2020 IEEE workshop on microwave theory and techniques in wireless communications, mttw 2020 (Vol. 1, pp. 145–150). IEEE. https://doi.org/10.1109/MTTW51045.2020.9244926
  26. [26] Tasken, K., & Ruppelt, A. (2006). Table contents. Frontiers in bioscience, 11, 2929–2939. file:///C:/Users/Administrator/Desktop/Landmark2022.pdf
  27. [27] Islam, M. S., H-Ng, B. W., & Abbott, D. (2020). Terahertz ultrahigh-q metasurface enabled by out-of-plane asymmetry. 2020 45th international conference on infrared, millimeter, and terahertz waves (IRMMW-THZ) (pp. 1–2). IEEE. https://doi.org/10.1109/IRMMW-THz46771.2020.9370414
  28. [28] Zhong, W., & Xu, Y. (2010). Distributed energy efficient spectrum sharing strategy selection with limited feedback in mimo interference channels. 2010 IEEE global telecommunications conference globecom 2010 (pp. 1–5). IEEE. https://doi.org/10.1109/GLOCOM.2010.5683868
  29. [29] Zong, F., & Yue, S. (2023). Carbon emission impacts of longitudinal disturbance on low-penetration connected automated vehicle environments. Transportation research part d: transport and environment, 123, 103911. https://doi.org/10.1016/j.trd.2023.103911
  30. [30] Guerra, E., & Cervero, R. (2011). Cost of a ride: The effects of densities on fixed-guideway transit ridership and costs. Journal of the american planning association, 77(3), 267–290. https://doi.org/10.1080/01944363.2011.589767
  31. [31] Geurs, K. T., & Van Wee, B. (2004). Accessibility evaluation of land-use and transport strategies: Review and research directions. Journal of transport geography, 12(2), 127–140. https://doi.org/10.1016/j.jtrangeo.2003.10.005
  32. [32] Lv, Y., Duan, Y., Kang, W., Li, Z., & Wang, F. Y. (2015). Traffic flow prediction with big data: A deep learning approach. IEEE transactions on intelligent transportation systems, 16(2), 865–873. https://doi.org/10.1109/TITS.2014.2345663
  33. [33] Ahmed, M. M., & Ghasemzadeh, A. (2018). The impacts of heavy rain on speed and headway Behaviors: An investigation using the SHRP2 naturalistic driving study data. Transportation research part c: emerging technologies, 91, 371–384. https://doi.org/10.1016/j.trc.2018.04.012
  34. [34] Khaksari, A., Tsaousoglou, G., Makris, P., Steriotis, K., Efthymiopoulos, N., & Varvarigos, E. (2021). Sizing of electric vehicle charging stations with smart charging capabilities and quality of service requirements. Sustainable cities and society, 70, 102872. https://doi.org/10.1016/j.scs.2021.102872
  35. [35] Stone, A. A., & Schneider, S. (2016). Commuting episodes in the United States: Their correlates with experiential wellbeing from the American Time Use Survey. Transportation research part f: traffic psychology and behaviour, 42, 117–124. https://doi.org/10.1016/j.trf.2016.07.004
  36. [36] Wang, Y. P. E., Lin, X., Adhikary, A., Grovlen, A., Sui, Y., Blankenship, Y., … & Razaghi, H. S. (2017). A primer on 3GPP narrowband Internet of Things. IEEE communications magazine, 55(3), 117–123. https://doi.org/10.1109/MCOM.2017.1600510CM
  37. [37] Hernandez, W., Mendez, A., Zalakeviciute, R., & Diaz-Marquez, A. M. (2020). Robust confidence intervals for PM2. 5 concentration measurements in the ecuadorian park la carolina. Sensors, 20(3), 654. https://doi.org/10.3390/s20030654

Downloads

Published

2025-09-20

Issue

Vol. 2 No. 3 (2025): Smart City Insights

Section

Articles

Categories

How to Cite

Edalatpanah, S. A., & Hess, M. (2025). Smart traffic management system: A data-driven approach to urban mobility. Smart City Insights, 2(3), 136-144. https://doi.org/10.22105/sci.v2i3.42

Similar Articles

1-10 of 29

You may also start an advanced similarity search for this article.