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Data-Driven Methodology for Sustainable Urban Mobility Assessment and Improvement

Author

Listed:
  • Marko Šoštarić

    (Faculty of Transport and Traffic Sciences, University of Zagreb, Vukelićeva 4, 10000 Zagreb, Croatia)

  • Krešimir Vidović

    (Ericsson Nikola Tesla, Krapinska 45, 10000 Zagreb, Croatia)

  • Marijan Jakovljević

    (Faculty of Transport and Traffic Sciences, University of Zagreb, Vukelićeva 4, 10000 Zagreb, Croatia)

  • Orsat Lale

    (Faculty of Transport and Traffic Sciences, University of Zagreb, Vukelićeva 4, 10000 Zagreb, Croatia)

Abstract

The transport system is sensitive to external influences generated by various economic, social and environmental changes. The society and the environment are changing extremely fast, resulting in the need for rapid adjustment of the transport system. Traffic system management, especially in urban areas, is a dynamic process, which is why transport planners are in need of a proven and validated methodology for fast and efficient transport data collection, fusion and analytics that will be used in sustainable urban mobility policy creation. The paper presents a development of a methodology in data rich reality that combines traditional and novel data science approach for transport system analysis and planning. The result is overall process consisting of 150 steps from first desktop research to final solution development. It enables urban mobility stakeholders to identify transport problems, analyze the urban mobility situation and to propose dedicated measures for sustainable urban mobility strengthening. The methodology is based on a big data research and analysis on anonymized big data sets originating from mobile telecommunication network, where the extraction of mobility data from the big dataset is the most innovative part of the proposed process. The extracted mobility data were validated through a “conventional” field research. The methodology was, for additional testing, applied in a pilot study, performed in the City of Rijeka in Croatia. It resulted in a set of alternative measures for modal shift from passenger cars to sustainable mobility modes, that were validated by the local public and urban mobility stakeholders.

Suggested Citation

  • Marko Šoštarić & Krešimir Vidović & Marijan Jakovljević & Orsat Lale, 2021. "Data-Driven Methodology for Sustainable Urban Mobility Assessment and Improvement," Sustainability, MDPI, vol. 13(13), pages 1-22, June.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:13:p:7162-:d:582379
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    References listed on IDEAS

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    3. Krešimir Vidović & Marko Šoštarić & Sadko Mandžuka & Goran Kos, 2020. "Model for Estimating Urban Mobility Based on the Records of User Activities in Public Mobile Networks," Sustainability, MDPI, vol. 12(3), pages 1-27, January.
    4. Ivana Semanjski & Rik Bellens & Sidharta Gautama & Frank Witlox, 2016. "Integrating Big Data into a Sustainable Mobility Policy 2.0 Planning Support System," Sustainability, MDPI, vol. 8(11), pages 1-19, November.
    5. Milne, Dave & Watling, David, 2019. "Big data and understanding change in the context of planning transport systems," Journal of Transport Geography, Elsevier, vol. 76(C), pages 235-244.
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    Cited by:

    1. Min Wu & Bingxin Yan & Ying Huang & Md Nazirul Islam Sarker, 2022. "Big Data-Driven Urban Management: Potential for Urban Sustainability," Land, MDPI, vol. 11(5), pages 1-16, May.
    2. Xi Lu & Jiaqing Lu & Xinzheng Yang & Xumei Chen, 2022. "Assessment of Urban Mobility via a Pressure-State-Response (PSR) Model with the IVIF-AHP and FCE Methods: A Case Study of Beijing, China," Sustainability, MDPI, vol. 14(5), pages 1-23, March.

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