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Integration of flows and signals data from mobile phone network for statistical analyses of traffic in a flooding risk area

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  • Perazzini, Selene
  • Metulini, Rodolfo
  • Carpita, Maurizio

Abstract

In this paper, we present a robust spatiotemporal statistical methodology that is capable of accurately forecasting traffic in the flood-prone area of the Mandolossa in the Province of Brescia (Italy). An innovative combination of two sources of mobile phone data is proposed to obtain an extremely accurate representation of the flows of people passing by the streets directly linked to the risky area. Three types of flows have been considered: outflows (from the flood-prone area to the neighborhood), inflows (from the neighborhood to the flood-prone area), and internal flows (within the flood-prone area). The three flows are assumed to be dependent on each other and are modeled using a vector autoregressive approach. We found evidence of both weekly and daily seasonal components in the time series. To capture the seasonality, a dynamic harmonic regression component has been included, where the optimal number of Fourier bases in the periodic functions has been chosen according to a criterion based on the Akaike Information Criteria. On the other side, the set of autoregressive parameters has been defined in such a way as to represent the time period necessary for the mobile phone company to observe, process, and release the data. The forecasting ability of the model has been assessed using blocked k-folds cross-validation along with the mean absolute percentage error and the hit rate. Though the model performs better for non-summer days, we found that it satisfactorily forecasts both the number and the level of people moving.

Suggested Citation

  • Perazzini, Selene & Metulini, Rodolfo & Carpita, Maurizio, 2023. "Integration of flows and signals data from mobile phone network for statistical analyses of traffic in a flooding risk area," Socio-Economic Planning Sciences, Elsevier, vol. 90(C).
    • Handle: RePEc:eee:soceps:v:90:y:2023:i:c:s0038012123002598
    DOI: 10.1016/j.seps.2023.101747
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    References listed on IDEAS

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    1. Clara Benevolo & Renata Paola Dameri & Beatrice D’Auria, 2016. "Smart Mobility in Smart City," Lecture Notes in Information Systems and Organization, in: Teresina Torre & Alessio Maria Braccini & Riccardo Spinelli (ed.), Empowering Organizations, edition 1, pages 13-28, Springer.
    2. Chris Tofallis, 2015. "A better measure of relative prediction accuracy for model selection and model estimation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(8), pages 1352-1362, August.
    3. Mirta Bensic & Natasa Sarlija & Marijana Zekic‐Susac, 2005. "Modelling small‐business credit scoring by using logistic regression, neural networks and decision trees," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 13(3), pages 133-150, July.
    4. Rodolfo Metulini & Maurizio Carpita, 2021. "A Spatio-Temporal Indicator for City Users Based on Mobile Phone Signals and Administrative Data," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(2), pages 761-781, August.
    5. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    6. Chris Tofallis, 2015. "A better measure of relative prediction accuracy for model selection and model estimation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(3), pages 524-524, March.
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