Search results

    Search results

    Show all results for ""
    Can not find any results or suggestions for "."

    Search tips

    • Make sure there are no spelling errors
    • Try different search terms or synonyms
    • Narrow your search for more hits

    How can we help?

    Contact Us

    Find Employees

    Skip to sub menu Skip to content
    University of Skövde, link to startpage
    University of Skövde, link to startpage

    Search results

      Search results

      Show all results for ""
      Can not find any results or suggestions for "."

      Search tips

      • Make sure there are no spelling errors
      • Try different search terms or synonyms
      • Narrow your search for more hits

      How can we help?

      Contact Us

      Find Employees

      SmartPark: Parking availability prediction and guidance

      Research Group Distributed Real-Time Systems Resarch Environment Informatics

      SmartPark: Parking availability prediction and guidance

      Research Group Distributed Real-Time Systems
      Resarch Environment Informatics

      Quick Facts

      Full project name

      Sustainable smart-parking management for connected and autonomous vehicles

      Duration

      December 2018 – December 2019

      Partners

      VTI, Swedish National Road and Transport Research Institute, Kista Science Park, Town of Stockholm and Stockholm parking.

      Financing

      VINNOVA

      SmartPark Parking availability prediction and guidance

      Cutting-edge Internet of Things (IoT) devices are increasingly deployed in urban environments creating new data streams, which could be exploited to deliver novel cloud-based services. Connected vehicles and road-infrastructure data are leveraged in this project to alleviate parking and induced traffic-congestion issues. The proposed SmartPark algorithm maximizing parking availability prediction, while minimizing the journey duration to the selected parking lot.

      The increased volume of individuals in a limited land area to seek some activity provides grounds for a parking problem to arise. Ultrasonic and magnetic sensors are already mounted in parking lots to detect the availability of a parking spot in parking lots. The sensor conveys the parking-spot availability information in real time to a parking system, which communicates this information to a Cloud-based service that is further exploited by third-party parking-service providers (PSPs).

      This trend is part of the current Internet- of-Things (IoT) evolution that is powering the reach to contextual information of a wide range of future smart-city sub-systems. The streams of data that literally "senses the city" is increasingly driving open Cloud-services to stimulate technology and business innovations.

      Service providers

      Smart parking service providers are potential beneficiaries of this evolution, particularly with the expected progression towards connected and driverless vehicles. These services are expected to be exploited for better infrastructure management and new added-value services to both users, city managers and businesses. Smart parking service providers are potential beneficiaries of this evolution, particularly with the expected progression towards connected and driverless vehicles. In this foreseeable context, parking land is a commodity used by the real-estate owner and/or by tenants for a fee, while new parking-service provider (PSP) intermediaries supply necessary IoT infrastructures to bring that parking information to a Cloud service.

      Cloud service

      Parking as A Service is driven by IoT integration in traffic subsystems of smart cities. Outsourced parking-facility to a PSP generate new revenue streams by relaying parking-lot availabilities along with congestion-aware navigation services to vehicles. This is where our proposed SmartPark approach comes into action to run as a Cloud service using PSP-provided data, offering an available spot in a parking lot, as well as a congestion-aware navigation route.

       

      BILD SKA IN

       

       

       

      Project leader

      Professor of Informatics

      Arne Nåbo

      Swedish National Road and Transport Research Institute

      Participating Researchers