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Assessing urban road safety through multidimensional indexes: Application of multicriteria decision making analysis to rank the Spanish provinces

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  • Castro-Nuño, Mercedes
  • Arévalo-Quijada, M. Teresa

Abstract

The traditional unidimensional approach used in road safety research to assess road safety performance is based on achievements in outcomes, such as number of traffic accidents, fatalities and injuries. However, taking into account the complex nature of the road safety framework, a multidimensional approach may be advisable in which all agents involved in the decision making process are properly represented. This article provides two multidimensional safety indicators that combine a set of criteria related to economics, demographics and sustainable urban transportation to assess urban road safety performance in 50 Spanish provinces (NUTS-3 regions). Multicriteria Decision Making Analysis (MDMA) is used to determine the set of factors that should be prioritized to minimize urban traffic accidents and fatalities. Using an objective weighting method for the chosen criteria, the obtained results point to aspects associated with the degree of urban development being the most important factors in discriminating and ranking the alternatives (provinces). Consequently, elements such as higher urban population and services concentration, and more advanced both transport systems and roads network, are related to safer urban areas. The two proposed safety indexes can provide policymakers with a useful tool for decision making in the area of urban road safety by identifying key attributes that should be promoted in urban planning.

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  • Castro-Nuño, Mercedes & Arévalo-Quijada, M. Teresa, 2018. "Assessing urban road safety through multidimensional indexes: Application of multicriteria decision making analysis to rank the Spanish provinces," Transport Policy, Elsevier, vol. 68(C), pages 118-129.
    • Handle: RePEc:eee:trapol:v:68:y:2018:i:c:p:118-129
    DOI: 10.1016/j.tranpol.2018.04.017
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    1. Dong-Young Lim, 2021. "A Neural Frequency-Severity Model and Its Application to Insurance Claims," Papers 2106.10770, arXiv.org, revised Feb 2024.

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