|
Iot Enabled Intelligent Traffic Congestion Handling System Empowered By Machine Learning
[Full Text]
AUTHOR(S)
Farhad Hassan, Amir Ijaz, Mubashir Ali, Zeshan Afzal, Farrukh Arslan
KEYWORDS
Internet of Things, Machine Learning, Traffic Management, Congestion Control, Smart Cities, Mathematical Modeling.
ABSTRACT
Internet of things is evolving technology which driving the world towards automation and smart systems. It is major factor of industry 4.0, smart cities and smart societies. Currently, the traffic is increasing exponentially in big cities; control and management of traffic in smart cities is well-known issue. Efficient congestion control and traffic management save many valuable resources. Various sensors are integrated in automated and smart systems to sense, collect and transfer data. Machine learning is another emerging technology that improves the intelligence and capabilities of smart systems. In this paper, we proposed an IoT-ITCHS-ML model to sense, analyze and control the traffic congestion in smart societies. The proposed system sense and notify the congested areas. The proposed systems performed significantly well in comparison with previous approaches and obtain 99.2% accuracy with only 1.2% missrate in training phase; 98.5% accuracy in validation phase. This research prosper the smart systems, IoT innovations and impact of machine learning in smart societies.
REFERENCES
[1] M. F. Rachmadi et al., “Adaptive traffic signal control system using camera sensor and embedded system,” in IEEE Region 10 Annual International Conference, Proceedings/TENCON, 2011, pp. 1261–1265, doi: 10.1109/TENCON.2011.6129009.
[2] S. N. Mahalank, K. B. Malagund, and R. M. Banakar, “Non Functional Requirement Analysis in IoT based smart traffic management system,” in 2016 International Conference on Computing Communication Control and automation (ICCUBEA), Aug. 2016, pp. 1–6, doi: 10.1109/ICCUBEA.2016.7860147.
[3] F. Zantalis, G. Koulouras, S. Karabetsos, and D. Kandris, “A review of machine learning and IoT in smart transportation,” Futur. Internet, vol. 11, no. 4, pp. 1–23, 2019, doi: 10.3390/FI11040094.
[4] M. Lewandowski, B. Płaczek, M. Bernas, and P. Szymała, “Road traffic monitoring system based on mobile devices and bluetooth low energy beacons,” Wirel. Commun. Mob. Comput., vol. 2018, 2018, doi: 10.1155/2018/3251598.
[5] S. Javaid, A. Sufian, S. Pervaiz, and M. Tanveer, “Smart traffic management system using Internet of Things,” Apr. 2018, pp. 1–1, doi: 10.23919/icact.2018.8323769.
[6] K. A. Shah, K. A. Shah, J. Jha, N. Khetra, and M. Zala, “Improvement of Traffic Monitoring System by Density and Flow Control For Indian Road System Using IoT,” Int. J. Sci. Res. Dev., vol. 3, no. 10, pp. 167–170, Jan. 2016.
[7] M. C. Narhe and D. M. S. Nagmode, “Vehicle Counting using Video Image Processing,” 2014.
[8] A. Dubey, M. Lakhani, S. Dave, and J. J. Patoliya, “Internet of Things based adaptive traffic management system as a part of Intelligent Transportation System (ITS),” in 2017 International Conference on Soft Computing and its Engineering Applications: Harnessing Soft Computing Techniques for Smart and Better World, icSoftComp 2017, Feb. 2018, vol. 2018-January, pp. 1–6, doi: 10.1109/ICSOFTCOMP.2017.8280081.
[9] P. kumar Sethy, P. Bidhar, J. Padhi, and S. Behera, “Realtime Implementation of Odd-Even Traffic Control Formula Using Arduino,” Accessed: Apr. 24, 2021. [Online]. Available: https://www.researchgate.net/publication/303487867_Realtime_Implementation_of_Odd-Even_Traffic_Control_Formula_Using_Arduino.
[10] Z. Zamani, M. Pourmand, and M. H. Saraee, “Application of data mining in traffic management: Case of City of Isfahan,” in ICECT 2010 - Proceedings of the 2010 2nd International Conference on Electronic Computer Technology, 2010, pp. 102–106, doi: 10.1109/ICECTECH.2010.5479977.
[11] C. Jacob and B. Abdulhai, “Integrated traffic corridor control using machine learning,” in Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 2005, vol. 4, pp. 3460–3465, doi: 10.1109/icsmc.2005.1571683.
[12] H. Che, H. Zhang, Z. Lin, D. Luo, and J. Wu, “Urban traffic signal coordinated control optimization with bus priority based on quantum genetic algorithm,” in Advanced Materials Research, 2012, vol. 433–440, pp. 829–834, doi: 10.4028/www.scientific.net/AMR.433-440.829.
[13] S. Djahel, M. Salehie, I. Tal, and P. Jamshidi, “Adaptive traffic management for secure and efficient emergency services in smart cities,” in 2013 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2013, 2013, pp. 340–343, doi: 10.1109/PerComW.2013.6529511.
[14] S. P., P. H., G. D., and K. G., “Intelligent Traffic Management based on IoT,” Int. J. Comput. Appl., vol. 157, no. 2, pp. 26–28, Jan. 2017, doi: 10.5120/ijca2017912639.
[15] R. P. and S. D.K., “Artificial neural networks approach induced by fuzzy logic for traffic,” J. Eng. Technol., vol. 1, p. 15.
[16] A. A., A. K. M., A. S., A. G., and F. A., “Modeling smart road traffic congestion control system using machine learning techniques,” Neural Netw. World, vol. 29, p. 2.
[17] A. Ata, M. A. Khan, S. Abbas, M. S. Khan, and G. Ahmad, “Adaptive IoT Empowered Smart Road Traffic Congestion Control System Using Supervised Machine Learning Algorithm,” Comput. J., May 2020, doi: 10.1093/comjnl/bxz129.
|
