IoT-based traffic prediction and traffic signal control system for smart city (original) (raw)

Abstract

Because of the population increasing so high, and traffic density remaining the same, traffic prediction has become a great challenge today. Creating a higher degree of communication in automobiles results in the time wastage, fuel wastage, environmental damage, and even death caused by citizens being trapped in the middle of traffic. Only a few researchers work in traffic congestion prediction and control systems, but it may provide less accuracy. So, this paper proposed an efficient IoT-based traffic prediction using OWENN algorithm and traffic signal control system using Intel 80,286 microprocessor for a smart city. The proposed system consists of ‘5’ phases, namely IoT data collection, feature extraction, classification, optimized traffic IoT values, and traffic signal control system. Initially, the IoT traffic data are collected from the dataset. After that, traffic, weather, and direction information are extracted, and these extracted features are given as input to the OWENN classifier, which classifies which place has more traffic. Suppose one direction of the place has more traffic, it optimizes the IoT values by using IBSO, and finally, the traffic is controlled by using Intel 80,286 microprocessor. An efficient OWENN algorithm for traffic prediction and traffic signal control using a Intel 80,286 microprocessor for a smart city. After extracting the features, the classification is performed in this step. Hereabout, the classification is done by using the optimized weight Elman neural network (OWENN) algorithm that classifies which places have more traffic. OWENN attains 98.23% accuracy than existing model also its achieved 96.69% F-score than existing model. The experimental results show that the proposed system outperforms state-of-the-art methods.

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References

• Bai M, Lin Y, Ma M, Wang P, Duan L (2020) PrePCT: Traffic congestion prediction in smart cities with relative position congestion tensor. Neurocomputing. https://doi.org/10.1016/j.neucom.2020.08.075
  Article Google Scholar
• Chakraborty ADA, Kumar A, Roy A, Roy S, Chakraborty D, Saha HN et al (2019) Intelligent traffic control system: towards smart city. In: IEEE 10th annual information technology, electronics and mobile communication conference (IEMCON), pp 1124–1129 Doi: https://doi.org/10.1109/IEMCON.2019.8936188
• Doolan R, Muntean G-M (2016) EcoTrec—A novel VANET-based approach to reducing vehicle emissions. IEEE Trans Intell Transp Syst 18(3):608–620
  Article Google Scholar
• GM Lingani, DB Rawat, M Garuba (2019) Smart traffic management system using deep learning for smart city applications. In: IEEE 9th annual computing and communication workshop and conference (CCWC), pp 0101–0106, Doi: https://doi.org/10.1109/CCWC.2019.8666539
• Janahan SK, Veeramanickam M, Arun S, Kumar N, Anandan R, Javed S (2018) IoT based smart traffic signal monitoring system using vehicles counts. Int J Eng Technol 7:309. https://doi.org/10.14419/ijet.v7i2.21.12388
  Article Google Scholar
• Jin J, Ma X, Kosonen I (2017) An intelligent control system for traffic lights with simulation-based evaluation. Control Eng Pract 58:24–33. https://doi.org/10.1016/j.conengprac.2016.09.009
  Article Google Scholar
• Joo H, Ahmed SH, Lim Y (2020) Traffic signal control for smart cities using reinforcement learning. Comput Commun. https://doi.org/10.1016/j.comcom.2020.03.005
  Article Google Scholar
• Kumar N, Rahman SS, Dhakad N (2020) Fuzzy inference enabled deep reinforcement learning-based traffic light control for intelligent transportation syste. IEEE Trans Intell Transp Syst. https://doi.org/10.1109/TITS.2020.2984033
  Article Google Scholar
• Lee W-H, Chiu C-Y (2020) Design and Implementation of a Smart Traffic Signal Control System for Smart City Applications. Sensors 20(2):508. https://doi.org/10.3390/s20020508
  Article MathSciNet Google Scholar
• Mondal Md A, Rehena Z (2019) Intelligent traffic congestion classification system using artificial neural network. In: Companion proceedings of the world wide web conference, pp 110–116 Doi: https://doi.org/10.1145/3308560.3317053.
• Muthanna MSA, Lyachek YT, Musaeed AMO, Esmail YAH, Adam ABM (2020) Smart system of a real-time pedestrian detection for smart city. In: IEEE Conference of Russian young researchers in electrical and electronic engineering (EIConRus), pp 45–50. Doi: https://doi.org/10.1109/EIConRus49466.2020.9039333.
• Neelakandan S (2016) Large scale optimization to minimize network traffic using MapReduce in big data applications. In: International conference on computation of power, energy information and commuincation (ICCPEIC), pp 193–199 Doi: https://doi.org/10.1109/ICCPEIC.2016.7557196
• Neelakandan S, Paulraj D (2020) An Automated Exploring And Learning Model For Data Prediction Using Balanced CA-Svm. J Ambient Intell Hum Comput, pp 1–12, 2020. Springer, Berlin. ISSN 1868–5137
• Pattanaik V, Singh M, Gupta PK, Singh SK (2016) Smart real-time traffic congestion estimation and clustering technique for urban vehicular roads. In: IEEE region 10 conference (TENCON), pp 3420–3423 Doi: https://doi.org/10.1109/TENCON.2016.7848689.
• 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 Gener Comput Syst 88:243–253
  Article Google Scholar
• Sarrab M, Pulparambil S, Awadalla M (2020) Development of an IoT based real-time traffic monitoring system for city governance. Glob Trans 2:230–245. https://doi.org/10.1016/j.glt.2020.09.004
  Article Google Scholar
• Satpathy S, Mohan P, Das S et al (2020) A new healthcare diagnosis system using an IoT-based fuzzy classifier with FPGA. J Supercomput 76:5849–5861. https://doi.org/10.1007/s11227-019-03013-2
  Article Google Scholar
• Subbulakshmi P, Prakash M (2018) Mitigating eavesdropping by using fuzzy based MDPOP-Q learning approach and multilevel Stackelberg game theoretic approach in wireless CRN. Cognit Syst Res 52:853–861
  Article Google Scholar
• Tchuitcheu WC, Bobda C, Pantho MJH (2020) Internet of smart-cameras for traffic lights optimization in smart cities. Internet Things. https://doi.org/10.1016/j.iot.2020.100207
  Article Google Scholar
• Yao H, Gao P, Wang J, Zhang P, Jiang C, Han Z (2019) Capsule network assisted IoT traffic classification mechanism for smart cities. IEEE Internet Things J 6(5):7515–7525
  Article Google Scholar
• Zhao S, Ye K, Xu C-Z (2019) Traffic classification and application identification based on machine learning in large-scale supercomputing center. In: IEEE 21st international conference on high performance computing and communications, IEEE 17th international conference on smart city, IEEE 5th international conference on data science and Systems (HPCC/SmartCity/DSS), pp 2299–2304, Doi: https://doi.org/10.1109/HPCC/SmartCity/DSS.2019.00319

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Authors and Affiliations

1. Department of IT, Jeppiaar Institute of Technology, Chennai, India
   S. Neelakandan
2. Department of CSE, R.M.D Engineering College, Chennai, India
   M. A. Berlin
3. Department of Computer Science & Engineering, Birla Institute of Applied Sciences, Bhimtal, India
   Sandesh Tripathi
4. Department of Information Technology, Sri Sai Ram Institute of Technology, Chennai, India
   V. Brindha Devi
5. Galgotias University, Greater Noida, India
   Indu Bhardwaj
6. Department of CS, CHRIST (Deemed To Be University), Banglore, India
   N. Arulkumar

Authors

1. S. Neelakandan
2. M. A. Berlin
3. Sandesh Tripathi
4. V. Brindha Devi
5. Indu Bhardwaj
6. N. Arulkumar

Contributions

SN, MAB contributed to technical and conceptual content and architectural design and ST, VBD contributed to guidance and counselling on the writing of the paper. IB and AN contributed for testing and validation of outcomes.

Corresponding author

Correspondence toS. Neelakandan.

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The authors declare that they have no conflict of interest.

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Communicated by Vicente Garcia Diaz.

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Neelakandan, S., Berlin, M.A., Tripathi, S. et al. IoT-based traffic prediction and traffic signal control system for smart city.Soft Comput 25, 12241–12248 (2021). https://doi.org/10.1007/s00500-021-05896-x

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• Accepted: 19 May 2021
• Published: 31 May 2021
• Version of record: 31 May 2021
• Issue date: September 2021
• DOI: https://doi.org/10.1007/s00500-021-05896-x

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