Abstract
Now-a-days, traffic congestion is one of the troubles in metro cities. It is mainly due to escalate in vehicle, population etc. Due to the raise in vehicle, the road is incapable of handling the traffic. Consequently it is very important to revamp the traffic management system. The advanced traffic management system intention is to ameliorate the safety and efficiency of the transportation system. Advanced traffic management system permit opportunities for new methods of evaluations and continuing assessment is provided. A few scenarios were analysed in the traffic issues and better performance is acquired in this research. The traffic map in New York City is taken as a database model and to discover the shortest path an advanced Dijkstra’s algorithm is applied. The advanced form of Hadoop known as Apache spark is used. Spark is the open standard and it is easy to program as it has tons of high level operators which can be flexible in-memory data processing that enables batch, real-time, and advanced analytics on the Apache Hadoop platform. R-tool is an open source programming language and software environment for statistical computing is also employed. By using time forecasting algorithm, for each scenarios the vehicle speed, count, collusion, time, etc. is calculated and the Map Analysis is done with good performance by using R-tool. The overall performance of the system that enhances the traffic control efficiency is 96.23%
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Change history
06 June 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s12652-022-04064-9
References
Brand A, Vengerov A, Mostovaya N, Berg V (2017) Development of self-contained power supply systems for traffic management equipment. Transp Res Procedia 20:53–59. https://doi.org/10.1016/j.trpro.2017.01.012
Dey K, Islam SS, Alam MK, Islam MR (2012) An infrared based intelligent traffic system. In: 2012 International conference on informatics, electronics and vision (ICIEV), pp 57–60. IEEE, https://doi.org/10.1109/ICIEV.2012.6317334
González CL, Zapotecatl JL, Gershenson C, Alberola JM, Julian V (2019) A robustness approach to the distributed management of traffic intersections. J Ambient Intell Humaniz Comput 1–12
Jagtap PS, Shinde S (2016) Big data solution for improving traffic management system effectively for vehicular networks. Int J Adv Res Comput Commun Eng 5(6):800–805
Jothilakshmi S, Natarajan VA, Gudivada VN (2015) Scalable traffic video analytics using hadoop MapReduce. ALLDATA 2015:18
Mokhtar B, Eltoweissy M (2017) Big data and semantics management system for computer networks. Ad Hoc Netw 57:32–51. https://doi.org/10.1016/j.adhoc.2016.06.013
Ropaite G, Koseleva N (2017) Big data in building energy efficiency: understanding of big data and main challenges. Procedia Eng 172(2017):544–549. https://doi.org/10.1016/j.proeng.2017.02.064
Ruan G, Zhang H (2017) Closed-loop big data analysis with visualization and scalable computing. Big Data Res 8:12–26. https://doi.org/10.1016/j.bdr.2017.01.002
Saeed F, Javed O, Aslam MH, Malhi MH, Fraz M (2011) Vision based intelligent traffic management system. 2011 Front Inform Technol
Sakhare AV, Khekare GS (2013) A smart city framework for intelligent traffic system using VANET. In: 2013 International mutli-conference on automation, computing, communication, control and compressed sensing (iMac4s), pp 302–305. IEEE. https://doi.org/10.1109/iMac4s.2013.6526427
Salama AS, Saleh BK, Eassa MM (2010) Intelligent cross road traffic management system (ICRTMS). In: 2010 2nd International conference on computer technology and development, pp 27–31. IEEE, https://doi.org/10.1109/ICCTD.2010.5646059
Schmidt D, Ostrouchov G, Matheson MA, Chen W-C (2017) Programming with BIG data in R: scaling analytics from one to thousands of nodes. Big Data Research 8:1–11. https://doi.org/10.1016/j.bdr.2016.10.002
Shelke M, Malhotra A, Mahalle PN (2019) Fuzzy priority based intelligent traffic congestion control and emergency vehicle management using congestion-aware routing algorithm. J Ambient Intell Humaniz Comput 1–18
Srinivasan A, Wang G, Hu J, Liu Q (2017) Preface: Security and privacy in big data clouds. 206–207. https://doi.org/10.1016/j.future.2017.03.033
Sun F, Yu X, Cheng X (2012) Intelligent urban traffic management system based on cloud computing and internet of things. In: 2012 International conference on computer science and service system, pp 2169–2172. IEEE, https://doi.org/10.1109/CSSS.2012.539
Thomas P, Stonier RJ, Hu Y (2007) Traffic signal control using fuzzy logic and evolutionary algorithms. In: 2007 IEEE congress on evolutionary computation, pp 1785–1792. IEEE, https://doi.org/10.1109/CEC.2007.4424689
Vu HQ, Gammack J, Miah SJ, McGrath M (2017) A big data analytics method for tourist behaviour analysis. Inform Manag 54(6):771–785. https://doi.org/10.1016/j.im.2016.11.011
Wang G, Guo L, Zheng L, Fang W (2010) Intelligent traffic management system base on WSN and RFID. In: 2010 International conference on computer and communication technologies in agriculture engineering, vol 2, pp 227–230. IEEE, https://doi.org/10.1109/CCTAE.2010.5544797
Wang A, Wang C, Nedjah N, Li X, Zhou X (2016) Soft computing in big data intelligent transportation systems. Appl Soft Comput 38:1099–1108. https://doi.org/10.1016/j.asoc.2015.06.006
Xu C, Wang C, Chen J, Xia Y, Lu X (2016) Big traffic data processing framework for intelligent monitoring and recording systems. Neurocomputing 181:139–146. https://doi.org/10.1016/j.neucom.2015.07.140
Zeng G (2015) Application of big data in intelligent traffic system. IOSR Journal of Computer Engineering 17(1):101–04. https://pdfs.semanticscholar.org/466b/1633d25fb77c0c817a1933e1c5848022927a.pdf DOI https://doi.org/10.9790/0661-17160104.
Zeyu J, Yi L, Shuiping Y, Mingduan Z (2017) Model study for intelligent transportation system with big data. Procedia Comput Sci 107:418–426. https://doi.org/10.1016/j.procs.2017.03.132
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article has been retracted. Please see the retraction notice for more detail:https://doi.org/10.1007/s12652-022-04064-9
About this article
Cite this article
Praveen, D.S., Raj, D.P. RETRACTED ARTICLE: Smart traffic management system in metropolitan cities. J Ambient Intell Human Comput 12, 7529–7541 (2021). https://doi.org/10.1007/s12652-020-02453-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-020-02453-6