BeInsight

  • Project period: 2020 - 2024
  • Category: Comissioned Research Project

Description

The purpose of the project is to enable automated ticketing (Be-In, Be-Out) and new business models within public transportation, as well as to improve travel flow and behavior analysis for the mobility industry. This will make the travel experience more frictionless, safer, and fairer, and encourage increased use of environmentally friendly personal transport.



This will be made possible by unique technology. With the sensors in the travelers' mobile phones and other data sources, we will understand the position of the mobile phone very accurately. With this solution, mobility operators will be able to base automated ticketing and gain more insight for further optimization of services.



Travelers will experience this as a "ticket-free" everyday life with more fair pricing and positive environmental and safety consequences. The potential is global.



The most central R&D challenges we expect to encounter in the project:



• Real-time detection of people in various modes of transport



• Minimize the necessary data volume transferred from passengers' mobile phones to the cloud service for accurate detection



• Determine which sensors should be used and how data from these can be combined to achieve as high accuracy as possible.



• Minimize the amount of work required by services in the cloud service to perform the necessary calculations, thereby facilitating the scaling of the number of users.



The potential for application is enormous. The automatic ticketing, as well as the insight into travel flow and behavior that the innovation represents, is something the market, both nationally and internationally, has demanded for several years. The composition of project partners with two relevant market actors ensures access to real production environments, as well as testing and outlining new business models that increase the likelihood of success in the pilot and further realization in the market.


Financing

The project is financed by Kristiania University College and Research Council of Norway (RCN)

Participants

  • Tor-Morten Grønli

    Tor-Morten Grønli

    • Project manager
    • Dean

    Kristiania University College

    School of Economics, Innovations and Technology

    Tor-Morten Grønli
  • Karoline Hauge

    • Ulrik Prøitz

      • Raghava Rao Mukkamala

        • Professor

        Kristiania University College

        School EIT faglig

        Raghava Rao Mukkamala
      • Philippe Büdinger

          Kristiania University College

          Kristiania University College

        • Anders Skretting

            Kristiania University College

            Kristiania University College

          • Elahe Fazeldehkordi

              Kristiania University College

              Kristiania University College

            • Malte Bieler

                Kristiania University College

                Kristiania University College

              Publications

              • Bieler, Malte, Skretting, Anders, Büdinger, Philippe & Grønli, Tor-Morten (2022). Survey of Automated Fare Collection Solutions in Public Transportation. IEEE transactions on intelligent transportation systems (Print). ISSN 1524-9050. 23(9) p 14248-14266. doi:
              • Bieler, Malte, Mukkamala, Raghava Rao & Grønli, Tor-Morten (2022). A Context- and Trajectory-Based Destination Prediction of Public Transportation Users. IEEE Intelligent Transportation Systems Magazine (ITSM). ISSN 1939-1390. 15(1) p 300-317. doi:
              • Grønli, Tor-Morten & Skretting, Anders (2021). Baseline for Performance Prediction of Android Applications. I Wu, Xintao (red.) 2020 IEEE International Conference on Big Data . IEEE conference proceedings. ISBN 978-1-7281-6251-5. p 3304-3310. doi: