A Smart Cloud and IoVT-Based Kernel Adaptive Filtering Framework for Parking Prediction

Divya Anand, Aman Singh, Khalid Alsubhi, Nitin Goyal, Atef Abdrabou, Ankit Vidyarthi, Joel J.P.C. Rodrigues

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


Smart vehicle parking is a collaborative effort of technology and human innovation where the efforts are to be minimized to save time and efforts. In smart cities it is one of the common challenges to introduce smart parking to increase parking efficiency and combat numerous issues like identification of free parking slot and real-time dynamic updation on traffic to save fuel and energy. In this work, a new cloud-based smart parking architecture is proposed that can help in predicting the available free parking slots in smart cities. Initially, the methodology collects the car count at any near by parking using Internet of Things (IoT) and Cloud-based approach. Later, the approach uses the Kernel Least Mean Square algorithm to make heuristic predictions about future vacancy using auto-regression. The proposed approach thus utilizes the online learning or model training. To validate the efficacy of the proposed work, the testing is done on the real-time dataset. The extensive numerical investigation is performed on parking lots of four international airports of a smart city in actual deployment scenarios. The experimentation has revealed superior performance of the method in terms of vacancy prediction.

Original languageEnglish
Pages (from-to)1-9
Number of pages9
JournalIEEE Transactions on Intelligent Transportation Systems
Publication statusAccepted/In press - 2022


  • Cloud computing
  • intelligent parking
  • Internet of Things
  • Kernel
  • Kernel adaptive filtering
  • parking prediction problem
  • Prediction algorithms
  • Predictive models
  • Real-time systems
  • Smart cities
  • Time series analysis

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications


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