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Assessing the Risk of Car Crashes in Road Networks

Author

Listed:
  • Riccardo Borgoni

    (Università di Milano-Bicocca)

  • Andrea Gilardi

    (Università di Milano-Bicocca)

  • Diego Zappa

    (Università Cattolica del Sacro Cuore)

Abstract

Worldwide, thousands of people die annually in highway-related crashes and millions are injured. Hence, car wrecks have very high direct social costs but also relevant indirect economic effects such as an adverse impact on the burden of hospitalization and an increased health expenditure. The analysis of car crash data has long been used as a basis for influencing highway and vehicle designs but also, and perhaps more importantly, to support local authorities in allocating resources aimed at improving road safety and making political decisions to mitigate road risks in the most exposed areas. In this paper, we show how a range of information collected from open data sources concerning the structure of the road network (road typology, traffic lights, pedestrian crossings, etc.), socio-demographical dimensions and crash history can be proficiently used for this aim. We adopt a dynamic Zero Inflated Poisson (ZIP) regression model to define two indexes. The first index, derived from the counting component of the ZIP model, measures how prone to crash risk a segment is. The other, derived by the zero component of the ZIP model, represents a measure of the likelihood of segments to not be exposed to crashes. Focussing on the city of Milan (Northern Italy), we found that the most relevant determinant of road risk proneness is crash history and that structural characteristics of the road are much more relevant than demographic information. Finally, we show how this information can be spatialized to produce maps of crash proneness and predict future spatial risk indexes.

Suggested Citation

  • Riccardo Borgoni & Andrea Gilardi & Diego Zappa, 2021. "Assessing the Risk of Car Crashes in Road Networks," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(2), pages 429-447, August.
    • Handle: RePEc:spr:soinre:v:156:y:2021:i:2:d:10.1007_s11205-020-02295-x
    DOI: 10.1007/s11205-020-02295-x
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    References listed on IDEAS

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    1. C. Dugas & Y. Bengio & N. Chapados & P. Vincent & G. Denoncourt & C. Fournier, 2003. "Statistical Learning Algorithms Applied to Automobile Insurance Ratemaking," World Scientific Book Chapters, in: A F Shapiro & L C Jain (ed.), Intelligent And Other Computational Techniques In Insurance Theory and Applications, chapter 4, pages 137-197, World Scientific Publishing Co. Pte. Ltd..
    2. de Jong,Piet & Heller,Gillian Z., 2008. "Generalized Linear Models for Insurance Data," Cambridge Books, Cambridge University Press, number 9780521879149, September.
    3. Lord, Dominique & Mannering, Fred, 2010. "The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(5), pages 291-305, June.
    4. Hermans, Elke & Van den Bossche, Filip & Wets, Geert, 2009. "Uncertainty assessment of the road safety index," Reliability Engineering and System Safety, Elsevier, vol. 94(7), pages 1220-1228.
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    Cited by:

    1. Andrea Gilardi & Jorge Mateu & Riccardo Borgoni & Robin Lovelace, 2022. "Multivariate hierarchical analysis of car crashes data considering a spatial network lattice," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1150-1177, July.

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