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A Straightforward Framework for Road Network Screening to Lombardy Region (Italy)

Author

Listed:
  • Michela Bonera

    (Department of Studies, Innovation and Development, Brescia Mobilità S.p.A., Via L. Magnolini 3, 25135 Brescia, Italy)

  • Benedetto Barabino

    (Department of Civil, Environmental, Architectural Engineering and Mathematics (DICATAM), University of Brescia, Via Branze 43, 25123 Brescia, Italy)

  • Giulio Maternini

    (Department of Civil, Environmental, Architectural Engineering and Mathematics (DICATAM), University of Brescia, Via Branze 43, 25123 Brescia, Italy)

Abstract

It is not possible to deal with sustainable mobility without considering road safety as a key element: Target 3.6 of the Sustainable Development Goals aims at halving the number of road deaths by 2030. To do so, further effort and effective tools are required for road authorities, to implement improvement measures and enhance road safety for all. Road network screening (RNS) is the first step of the whole Road Infrastructure Safety Management (RISM) System process. It is applied to a wide scale to assess the safety performance of the whole road network and identify the worst performing roads (or sites). The literature is quite rich with RNS models and methods, which have greatly improved, recently. Moreover, although many national frameworks on road safety have been issued over time, some barriers remain, specifically related to data quality, such as accurate crash location, which is mainly used to integrate crash data with other databases. In addition, most of these frameworks adopted partial indexes to identify black spots and presented results using fixed maps for visualization. This paper fills these gaps by the proposal of a straightforward operational framework to perform RNS, based on a simple and flexible rationale to integrate raw crash, traffic, and road data. Specifically, the framework: (i) manages crash location data, without relying on plane or geographical coordinates, which are missing or inaccurate and still are a crucial issue in many European countries such as Italy; (ii) adopts an adjusted accident cost rate index that integrates frequency and severity of crashes as well as a measurement of exposure; (iii) introduces variable maps that show the results at different jurisdiction levels. A relevant case study demonstrates the usefulness of this framework using 30,000+ crash data of the whole non-urban road network of the Lombardy Region (Northern Italy). Road authorities could adopt this framework to perform an accurate safety screening on the overall regional road network. Moreover, this framework could be implemented in a road traffic safety managerial system to better prioritise safety interventions within a tight budget and help achieve sustainable development targets.

Suggested Citation

  • Michela Bonera & Benedetto Barabino & Giulio Maternini, 2022. "A Straightforward Framework for Road Network Screening to Lombardy Region (Italy)," Sustainability, MDPI, vol. 14(19), pages 1-26, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12424-:d:929385
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    References listed on IDEAS

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    1. Kuo, Pei-Fen & Lord, Dominique & Walden, Troy Duane, 2013. "Using geographical information systems to organize police patrol routes effectively by grouping hotspots of crash and crime data," Journal of Transport Geography, Elsevier, vol. 30(C), pages 138-148.
    2. Elyasi, Mohammad Reza & Saffarzade, Mahmoud & Boroujerdian, Amin Mirza, 2016. "A novel dynamic segmentation model for identification and prioritization of black spots based on the pattern of potential for safety improvement," Transportation Research Part A: Policy and Practice, Elsevier, vol. 91(C), pages 346-357.
    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.
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