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Temporal Modeling of On-Street Parking Data for Detection of Parking Violation in Smart Cities

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Evolution in Computational Intelligence

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1176))

Abstract

With the increase in number of vehicles, the requirement of intelligent parking management is indispensable in smart cities. One of the major requirements in smart parking system is handling parking violations efficiently. The parking violation generally includes parking beyond allowed time. To detect parking violations and to manage it efficiently, the parking data collected through field sensor devices need to be analyzed intensively and thoroughly. To this end, this paper has presented temporal analysis of on-street parking data of Melbourne city and proposed a novel mathematical model and curve-fitting algorithm using quasi-Newton method to detect parking violation. The proposed model is validated with real dataset through simulation with a sum of squared error of \(4.888 \times 10^{-7}\).

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Acknowledgements

This research work is supported under NPIU,TEQIP-III sponsored Collaborative Research Scheme(CRS).

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Authors and Affiliations

  1. Parala Maharaja Engineering College, Berhampur, Odisha, India

    Shiv Kumar Sahoo, Niranjan Panigrahi, Debasis Mohapatra & Asutosh Panda

  2. National Institute of Technology, Raipur, Chhattisgarh, India

    Arvind Sinha

Authors
  1. Shiv Kumar Sahoo
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  2. Niranjan Panigrahi
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  3. Debasis Mohapatra
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  4. Asutosh Panda
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  5. Arvind Sinha
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Corresponding author

Correspondence to Niranjan Panigrahi .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Shri Ramswaroop Memorial Group of Professional Colleges (SRMGPC), Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  2. Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien, Taiwan

    Sheng-Lung Peng

  3. School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, Odisha, India

    Suresh Chandra Satapathy

  4. Department of Informatics, University of Leicester, Leicester, UK

    Yu-Dong Zhang

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Sahoo, S.K., Panigrahi, N., Mohapatra, D., Panda, A., Sinha, A. (2021). Temporal Modeling of On-Street Parking Data for Detection of Parking Violation in Smart Cities. In: Bhateja, V., Peng, SL., Satapathy, S.C., Zhang, YD. (eds) Evolution in Computational Intelligence. Advances in Intelligent Systems and Computing, vol 1176. Springer, Singapore. https://doi.org/10.1007/978-981-15-5788-0_25

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