ew 22(39): e3

Research Article

Sustainable Urban Mobility Boost Smart Toolbox Upgrade

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  • @ARTICLE{10.4108/ew.v9i39.1193,
        author={M. Sostaric and M. Jakovljevic and K. Vidovic and O. Lale},
        title={Sustainable Urban Mobility Boost Smart Toolbox Upgrade},
        journal={EAI Endorsed Transactions on Energy Web},
        keywords={big data science, origin/destination matrices, modal split, telecom operator, innovative methodology, mobile network data},
  • M. Sostaric
    M. Jakovljevic
    K. Vidovic
    O. Lale
    Year: 2022
    Sustainable Urban Mobility Boost Smart Toolbox Upgrade
    DOI: 10.4108/ew.v9i39.1193
M. Sostaric1,*, M. Jakovljevic1, K. Vidovic2, O. Lale1
  • 1: Faculty of Transport and Traffic Sciences, Zagreb, Croatia
  • 2: Ericsson Nikola Tesla d.d., Zagreb, Croatia
*Contact email: msostaric@fpz.unizg.hr


SUMBooST2 research develops universally applicable data science methodology which extracts key urban mobility parameters and origin/destination matrices from the anonymized big data set gathered from telecom operator. The methodology (toolbox) provides transport planners with a method for fast, efficient, and reliable provision of data on movements within the certain area. Origin/destination matrices with modal split will provide transport planners with valid input data for the planning of urban transport systems. The algorithms which separate relevant mobility data from the overall dataset are the unique part of the toolbox. The algorithms to identify passenger car trips are developed in 2020 project SUMBooST, and they are being upgraded in 2021 to detect trips made by active mobility modes and public transport. For the methodology to be valid, it must be implemented in representative number of cities. Previous SUMBooST project included implementation and validation in the City of Rijeka, and SUMBooST2 continues with two other cities, City of Zagreb, and City of Dubrovnik. The aim of the paper is to present innovative toolbox for the boost of sustainable urban planning based on big data science.