2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 3 , Issue 1 , PP: 43-50, 2021 | Cite this article as | XML | Html | PDF | Full Length Article
Mahmoud Ismail 1 * , Shereen Zaki 2
Doi: https://doi.org/10.54216/JISIoT.030104
rapid urbanization and the growing population in smart cities pose significant challenges to the management of urban traffic. In recent years, there has been an increasing interest in developing intelligent traffic management systems that leverage advanced machineries, such as the Internet of Things (IoT), and machine learning (ML), to enhance the efficiency and effectiveness of traffic management in smart cities. This paper proposes an intelligent traffic management (ITM) system for smart cities that integrates various computing paradigms to provide real-time traffic information, optimize traffic flow, and improve road safety. The suggested system utilizes an innovative system for the predicting the traffic flows with the goal of enhancing the current level of traffic management in smart cities. An enhanced convolutional autoencoder network is incorporated into the proposed system as a means of extracting the spatial representations contained in traffic flows. Additionally, by the utilization of a refined gated learning module, it possesses the capability of accurately recording temporal dynamics. Our system is evaluated using real-world traffic data, and the results demonstrate its effectiveness in improving traffic flow and reducing congestion in smart cities. Our system has the potential to significantly enhance the performance of traffic management systems in smart cities, decrease traffic crowding, and progress the safety of roads in smart cities.
Deep Learning , Intelligent system , internet of things (IoT) , smart cities
[1]. Tang, F., Mao, B., Fadlullah, Z. M., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2017). On removing routing protocol from future wireless networks: A real -time deep learning approach for intelligent traffic control. IEEE Wireless Communications, 25(1), 154-160.
[2]. Wei, H., Zheng, G., Yao, H., & Li, Z. (2018, July). Intellilight: A reinforcement learning approach for intelligent traffic light control. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 2496-2505).
[3]. Tang, F., Fadlullah, Z. M., Mao, B., & Kato, N. (2018). An intelligent traffic load prediction-based adaptive channel assignment algorithm in SDN-IoT: A deep learning approach. IEEE Internet of Things Journal, 5(6), 5141-5154.
[4]. Fadlullah, Z. M., Tang, F., Mao, B., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2017). State-of-the-art deep learning: Evolving machine intelligence toward tomorrow’s intelligent network traffic control systems. IEEE Communications Surveys & Tutorials, 19(4), 2432 -2455.
[5]. Zhao, Z., Chen, W., Wu, X., Chen, P. C., & Liu, J. (2017). LSTM network: a deep learning approach for short‐term traffic forecast. IET Intelligent Transport Systems, 11(2), 68 -75.
[6]. Rehena, Z., & Janssen, M. (2018, April). Towards a framework for context -aware intelligent traffic management system in smart cities. In Companion Proceedings of the The Web Conference 2018 (pp. 893-898).
[7]. Y. Liu, C. Yang and Q. Sun, "Thresholds Based Image Extraction Schemes in Big Data Environment in Intelligent Traffic Management," in IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 7, pp. 3952-3960, July 2021, doi: 10.1109/TITS.2020.2994386.
[8]. Menouar, H., Guvenc, I., Akkaya, K., Uluagac, A. S., Kadri, A., & Tuncer, A. (2017). UAV-enabled intelligent transportation systems for the smart city: Applications and challenges. IEEE Communications Magazine, 55(3), 22-28.
[9]. Rizwan, P., Suresh, K., & Babu, M. R. (2016, October). Real-time smart traffic management system for smart cities by using Internet of Things and big data. In 2016 international conference on emerging technological trends (ICETT) (pp. 1-7). IEEE.
[10]. Djahel, S., Doolan, R., Muntean, G. M., & Murphy, J. (2014). A communications-oriented perspective on traffic management systems for smart cities: Challenges and innovative approaches. IEEE Communications Surveys & Tutorials, 17(1), 125-151.
[11]. Rego, A., Garcia, L., Sendra, S., & Lloret, J. (2018). Software Defined Network-based control system for an efficient traffic management for emergency situations in smart cities. Future Generation Computer Systems, 88, 243-253.
[12]. Kumar, P. M., Manogaran, G., Sundarasekar, R., Chilamkurti, N., & Varatharajan, R. (2018). Ant colony optimization algorithm with internet of vehicles for intelligent traffic control system. Computer Networks, 144, 154-162.
[13]. Mohammed, F., Idries, A., Mohamed, N., Al-Jaroodi, J., & Jawhar, I. (2014, May). UAVs for smart cities: Opportunities and challenges. In 2014 International Conference on Unmanned Aircraft Systems
(ICUAS) (pp. 267-273). IEEE.
[14]. Sharma, P. K., Moon, S. Y., & Park, J. H. (2017). Block-VN: A distributed blockchain based vehicular network architecture in smart city. Journal of information processing systems, 13(1), 184-195.
[15]. Lin, Y., Wang, P., & Ma, M. (2017, May). Intelligent transportation system (ITS): Concept, challenge and opportunity. In 2017 ieee 3rd international conference on big data security on cloud (bigdatasecurity), ieee international conference on high performan ce and smart computing (hpsc), and ieee international conference on intelligent data and security (ids) (pp. 167-172). IEEE.
[16]. A. Salamanis, D. D. Kehagias, C. K. Filelis-Papadopoulos, D. Tzovaras and G. A. Gravvanis, "Managing Spatial Graph Dependencies in Large Volumes of Traffic Data for Travel-Time Prediction," in IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 6, pp. 1678 -1687, June 2016, doi: 10.1109/TITS.2015.2488593.
[17]. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł. and Polosukhin, I., 2017. Attention is all you need. Advances in neural information processing systems, 30.
