Abstract
In present-day times, the number of vehicles has increased drastically, but in contrast, the capabilities of our roads and transportation systems still remain underdeveloped and as a result, fail to cope with this upsurge in the number of vehicles. As a consequence, traffic jamming, road accidents, increase in pollution levels are some of the common traits that can be observed in our new age cities. With the emergence of the Internet of Things and its applicability in Smart Cities, creates a perfect platform for addressing traffic-related issues, thus leading to the establishment of Intelligent Traffic Management Systems (ITMS). The work presented in this paper talks about an intelligent traffic management system that lays its foundation on Cloud computing, Internet of Things and Data Analytics. Our proposed system helps to resolve the numerous challenges being faced by traffic management authorities, in terms of predicting an optimum route, reducing average waiting time, traffic congestion, travel cost and the extent of air pollution. The system aims at using machine learning algorithms for predicting optimum routes based upon traffic mobilization patterns, vehicle categorization, accident occurrences and levels of precipitation. Finally, the system comes up with the concept of a green corridor, wherein emergency services are allowed to travel without facing any kinds of traffic congestion.
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References
Miz, V., Hahanov, V.: Smart traffic light in terms of the cognitive road traffic management system (CTMS) based on the internet of things. In: 2014 East-West Design & Test Symposium (EWDTS), pp. 1–5. IEEE, September 2014
Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M.: Internet of things (IoT): a vision, architectural elements, and future directions. Future Gener. Comput. Syst. 29(7), 1645–1660 (2013)
Foschini, L., Taleb, T., Corradi, A., Bottazzi, D.: M2M-based metropolitan platform for IMS-enabled road traffic management in IoT. IEEE Commun. Mag. 49(11), 50–57 (2011)
Yu, M., Zhang, D., Cheng, Y., Wang, M.: An RFID electronic tag based automatic vehicle identification system for traffic IOT applications. In: 2011 Chinese Control and Decision Conference (CCDC), pp. 4192–4197. IEEE, May 2011
Zhou, H., Liu, B., Wang, D.: Design and research of urban intelligent transportation system based on the internet of things. In: Wang, Y., Zhang, X. (eds.) IOT 2012. CCIS, vol. 312, pp. 572–580. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32427-7_82
Khanna, A., Anand, R.: IoT based smart parking system. In: International Conference on Internet of Things and Applications (IOTA), pp. 266–270. IEEE, January 2016
Lingling, H., Haifeng, L., Xu, X., Jian, L.: An intelligent vehicle monitoring system based on internet of things. In: 2011 Seventh International Conference on Computational Intelligence and Security (CIS), pp. 231–233. IEEE, December 2011
Kyriazis, D., Varvarigou, T., White, D., Rossi, A., Cooper, J.: Sustainable smart city IoT applications: heat and electricity management & eco-conscious cruise control for public transportation. In: 2013 IEEE 14th International Symposium and Workshops on World of Wireless, Mobile and Multimedia Networks (WoWMoM), pp. 1–5. IEEE, June 2013
Khanna, A., Tomar, R.: IoT based interactive shopping ecosystem. In: 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), pp. 40–45. IEEE, October 2016
Tarapiah, S., Atalla, S., AbuHania, R.: Smart on-board transportation management system using GPS/GSM/GPRS technologies to reduce traffic violation in developing countries. Int. J. Digital Inf. Wirel. Commun. (IJDIWC) 3(4), 430–439 (2013)
Parwekar, P.: From internet of things towards cloud of things. In: 2011 2nd International Conference on Computer and Communication Technology (ICCCT), pp. 329–333. IEEE, September 2011
Zhou, J., et al.: CloudThings: a common architecture for integrating the internet of things with cloud computing. In: 2013 IEEE 17th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp. 651–657. IEEE, June 2013
Rajan, M.A., Balamuralidhar, P., Chethan, K.P., Swarnahpriyaah, M.: A self-reconfigurable sensor network management system for internet of things paradigm. In: 2011 International Conference on Devices and Communications (ICDeCom), pp. 1–5. IEEE, February 2011
Tomar, R., Khanna, A., Bansal, A., Fore, V.: An architectural view towards autonomic cloud computing. In: Satapathy, S.C., Bhateja, V., Raju, K.Srujan, Janakiramaiah, B. (eds.) Data Engineering and Intelligent Computing. AISC, vol. 542, pp. 573–582. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-3223-3_55
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newsl. 11(1), 10–18 (2009)
Fore, V., Khanna, A., Tomar, R., Mishra, A.: Intelligent supply chain management system. In: 2016 International Conference on Advances in Computing and Communication Engineering (ICACCE), pp. 296–302. IEEE, November 2016
Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: iFogSim: a toolkit for modeling and simulation of resource management techniques in the internet of things, Edge and Fog computing environments. Softw.: Pract. Exp. 47(9), 1275–1296 (2017)
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Khanna, A., Goyal, R., Verma, M., Joshi, D. (2019). Intelligent Traffic Management System for Smart Cities. In: Singh, P., Paprzycki, M., Bhargava, B., Chhabra, J., Kaushal, N., Kumar, Y. (eds) Futuristic Trends in Network and Communication Technologies. FTNCT 2018. Communications in Computer and Information Science, vol 958. Springer, Singapore. https://doi.org/10.1007/978-981-13-3804-5_12
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DOI: https://doi.org/10.1007/978-981-13-3804-5_12
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