Real-Time Smart Parking Systems Integration in Distributed ITS for Smart Cities

Intelligent Transportation Systems (ITS) have evolved as a key research topic in recent years, revolutionizing the overall traffic and travel experience by providing a set of advanced services and applications. These data-driven services contribute to mitigate major problems arising from the ever growing need of transport in daily lives. Despite the progress, there is still need for an enhanced and distributed solution that can exploit the data from the available systems and provide an appropriate and real-time reaction on transportation systems. Therefore, in this paper, the authors present a new architecture where the intelligence is distributed and the decisions are decentralized. The proposed architecture is scalable since the incremental addition of new peripheral subsystems is supported by the introduction of gateways which requires no reengineering of the communication infrastructure. The proposed architecture is deployed to tackle the problem of traffic management inefficiency in urban areas, where traffic load is substantially increased, by vehicles moving around unnecessarily, to find a free parking space. This can be significantly reduced through the availability and diffusion of local information regarding vacant parking slots to drivers in a given area. Two types of parking systems, magnetic and vision sensor based, have been introduced, deployed, and tested in different scenarios. The effectiveness of the proposed architecture, together with the proposed algorithms, is assessed in field trials.

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    • Copyright © 2018 Muhammad Alam et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • Authors:
    • Alam, Muhammad
    • Moroni, Davide
    • Pieri, Gabriele
    • Tampucci, Marco
    • Gomes, Miguel
    • Fonseca, Jose
    • Ferreira, Joaquim
  • Publication Date: 2018

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  • English

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  • Accession Number: 01685969
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Oct 29 2018 10:54AM