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A system dynamic approach for the smart mobility of people: Implications in the age of big data

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  • Vecchio, Pasquale Del
  • Secundo, Giustina
  • Maruccia, Ylenia
  • Passiante, Giuseppina

Abstract

Mobility of people can be configured as an information intensive process resulting from a complex set of factors. Its effective implementation requires the adoption of methods able to leverage on a set of complex and dynamic variables, and mainly on a huge amount of data available. Moving from this assumption, this paper aims to demonstrate that system dynamics could present a useful approach for optimising decision making for people's mobility. The conceptual model is built by using the principles of system dynamics methodology and is based on causal feedback relationships among the various factors related to the different needs of people's mobility. The causal feedback loops and interrelationship among various parameters illustrate the dynamicity and the influence of parameters on one another. The simulation analysis was conducted to dynamically evaluate six scenarios corresponding to the different solutions available for particular segments of demand. Findings highlight that the modelling approaches could guide the city planners to evolve responsive policy interventions for further developing smart mobility of people. Implications for policy makers regard the developing sustainable mobility scenarios based on the analysis of big data from the adoption of digital platforms grounded on the simulation model.

Suggested Citation

  • Vecchio, Pasquale Del & Secundo, Giustina & Maruccia, Ylenia & Passiante, Giuseppina, 2019. "A system dynamic approach for the smart mobility of people: Implications in the age of big data," Technological Forecasting and Social Change, Elsevier, vol. 149(C).
    • Handle: RePEc:eee:tefoso:v:149:y:2019:i:c:s0040162519306419
    DOI: 10.1016/j.techfore.2019.119771
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    8. Maruccia, Ylenia & Solazzo, Gianluca & Del Vecchio, Pasquale & Passiante, Giuseppina, 2020. "Evidence from Network Analysis application to Innovation Systems and Quintuple Helix," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    9. Oliver Kunze & Fabian Frommer, 2021. "The Matrix vs. The Fifth Element—Assessing Future Scenarios of Urban Transport from a Sustainability Perspective," Sustainability, MDPI, vol. 13(6), pages 1-19, March.
    10. Rocio de la Torre & Canan G. Corlu & Javier Faulin & Bhakti S. Onggo & Angel A. Juan, 2021. "Simulation, Optimization, and Machine Learning in Sustainable Transportation Systems: Models and Applications," Sustainability, MDPI, vol. 13(3), pages 1-21, February.
    11. Erik Karger & Marvin Jagals & Frederik Ahlemann, 2021. "Blockchain for Smart Mobility—Literature Review and Future Research Agenda," Sustainability, MDPI, vol. 13(23), pages 1-32, November.
    12. Paulo Antonio Maldonado Silveira Alonso Munhoz & Fabricio da Costa Dias & Christine Kowal Chinelli & André Luis Azevedo Guedes & João Alberto Neves dos Santos & Wainer da Silveira e Silva & Carlos Alb, 2020. "Smart Mobility: The Main Drivers for Increasing the Intelligence of Urban Mobility," Sustainability, MDPI, vol. 12(24), pages 1-25, December.
    13. Monika Stoma & Agnieszka Dudziak & Jacek Caban & Paweł Droździel, 2021. "The Future of Autonomous Vehicles in the Opinion of Automotive Market Users," Energies, MDPI, vol. 14(16), pages 1-19, August.
    14. Richter, Maximilian A. & Hagenmaier, Markus & Bandte, Oliver & Parida, Vinit & Wincent, Joakim, 2022. "Smart cities, urban mobility and autonomous vehicles: How different cities needs different sustainable investment strategies," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    15. Ibrahim, Awad Elsayed Awad & Elamer, Ahmed A. & Ezat, Amr Nazieh, 2021. "The convergence of big data and accounting: innovative research opportunities," Technological Forecasting and Social Change, Elsevier, vol. 173(C).

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