Intelligent Traffic Management Systems for Smart Cities

  • Authors

    • Dr. Grace Ndlovu Associate Professor, University of Pretoria, South Africa Author

    DOI:

    https://doi.org/10.67228/30716357/IJMRSE-2021PI6T3Q

    Published 04-04-2021

  • Intelligent Traffic Management, Smart Cities, Internet Of Things (Iot), Artificial Intelligence, Machine Learning, Adaptive Traffic Control, Urban Mobility, Big Data Analytics

    Issue

    Vol. 4 No. 1 (2021)

    Section

    Articles

    How to Cite

    Intelligent Traffic Management Systems for Smart Cities. (2021). International Journal of Modern Research in Science & Engineering, 4(1), 01-16. https://doi.org/10.67228/30716357/IJMRSE-2021PI6T3Q

  • Abstract

    The high rate of urbanization has caused a high growth in the number of vehicles, which has produced a congestion, wastage on time, and fuel, as well as pollution to the environment. No longer applicable because of the dynamic character of modern urban traffic, the traditional traffic management systems based on the use of the non-informative control mechanisms and low real-time flexibility. The Intelligent Traffic Management Systems (ITMS) have become a very important part of an intelligent city system, as it intends to use the latest technologies that include Artificial Intelligence (AI), Internet of Things (IoT), machine learning, cloud computing and big data analysis to make traffic flow in the city more efficient and safer. In this paper, complete research on Intelligent Traffic Management Systems in smart cities has been made. It dwells upon the development of traffic management, the enabling technologies, system architecture, and methodologies. An elaborate literature review indicates the latest developments and outlines the gaps in research. The proposed approach will combine real-time data collection, predictive analysis, and responsive signal modulation to improve the traffic flow. The mathematical models and performance evaluation measures have been addressed to measure the system effectiveness. The findings indicate that intelligent systems are very effective in minimizing congestion, travelling time as well as emissions over traditional methods. Lastly, issues, constraints, and research prospects are given to facilitate long-term and viable implementation of ITMS in intelligent city setups.

  • References

    [1] Papageorgiou, M., Diakaki, C., Dinopoulou, V., Kotsialos, A., & Wang, Y. (2003). Review of road traffic control strategies. Proceedings of the IEEE, 91(12), 2043–2067.

    [2] Gartner, N. H., Messer, C. J., & Rathi, A. K. (2002). Traffic flow theory: A state-of-the-art report. Transportation Research Board.

    [3] Coifman, B., Beymer, D., McLauchlan, P., & Malik, J. (1998). A real-time computer vision system for vehicle tracking and traffic surveillance. Transportation Research Part C, 6(4), 271–288.

    [4] Ma, X., Tao, Z., Wang, Y., Yu, H., & Wang, Y. (2015). Long short-term memory neural network for traffic speed prediction. Transportation Research Part C, 54, 187–197.

    [5] Krajzewicz, D., Erdmann, J., Behrisch, M., & Bieker, L. (2012). Recent development and applications of SUMO – Simulation of Urban MObility. International Journal On Advances in Systems and Measurements, 5(3–4), 128–138.

    [6] Zanella, A., Bui, N., Castellani, A., Vangelista, L., & Zorzi, M. (2014). Internet of Things for smart cities. IEEE Internet of Things Journal, 1(1), 22–32.

    [7] Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645–1660.

    [8] Chen, C., Liu, Y., Qian, Z., & Ma, J. (2016). Traffic flow prediction using deep learning. Transportation Research Part C, 65, 1–14.

    [9] Abdulhai, B., Pringle, R., & Karakoulas, G. J. (2003). Reinforcement learning for true adaptive traffic signal control. Journal of Transportation Engineering, 129(3), 278–285.

    [10] Wei, H., Zheng, G., Yao, H., & Li, Z. (2018). IntelliLight: A reinforcement learning approach for intelligent traffic light control. Proceedings of the 24th ACM SIGKDD Conference, 2496–2505.

    [11] Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637–646.

    [12] Satyanarayanan, M. (2017). The emergence of edge computing. Computer, 50(1), 30–39.

    [13] Zhang, Y., & Li, L. (2018). Security and privacy in intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems, 19(7), 2381–2395.

    [14] Petit, J., & Shladover, S. E. (2015). Potential cyberattacks on automated vehicles. IEEE Transactions on Intelligent Transportation Systems, 16(2), 546–556.

    [15] Vlahogianni, E. I., Karlaftis, M. G., & Golias, J. C. (2014). Short-term traffic forecasting: Where we are and where we’re going. Transportation Research Part C, 43, 3–19

  • Downloads