A tool for safety evaluations of road improvements

Harri Peltola (Corresponding Author), Riikka Rajamäki, Juha Luoma

    Research output: Contribution to journalArticleScientificpeer-review

    8 Citations (Scopus)

    Abstract

    Road safety impact assessments are requested in general, and the directive on road infrastructure safety management makes them compulsory for Member States of the European Union. However, there is no widely used, science-based safety evaluation tool available. We demonstrate a safety evaluation tool called TARVA. It uses EB safety predictions as the basis for selecting locations for implementing road-safety improvements and provides estimates of safety benefits of selected improvements. Comparing different road accident prediction methods, we demonstrate that the most accurate estimates are produced by EB models, followed by simple accident prediction models, the same average number of accidents for every entity and accident record only. Consequently, advanced model-based estimates should be used. Furthermore, we demonstrate regional comparisons that benefit substantially from such tools. Comparisons between districts have revealed significant differences. However, comparisons like these produce useful improvement ideas only after taking into account the differences in road characteristics between areas. Estimates on crash modification factors can be transferred from other countries but their benefit is greatly limited if the number of target accidents is not properly predicted. Our experience suggests that making predictions and evaluations using the same principle and tools will remarkably improve the quality and comparability of safety estimations.
    Original languageEnglish
    Pages (from-to)277-288
    Number of pages11
    JournalAccident Analysis and Prevention
    Volume60
    DOIs
    Publication statusPublished - 2013
    MoE publication typeA1 Journal article-refereed

    Fingerprint

    accident
    road
    Safety
    Accidents
    evaluation
    regional comparison
    Safety Management
    European Union
    Highway accidents
    district
    infrastructure
    science
    management
    experience

    Keywords

    • cost-effectiveness
    • effect
    • evaluation
    • tools
    • traffic safety

    Cite this

    Peltola, Harri ; Rajamäki, Riikka ; Luoma, Juha. / A tool for safety evaluations of road improvements. In: Accident Analysis and Prevention. 2013 ; Vol. 60. pp. 277-288.
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    A tool for safety evaluations of road improvements. / Peltola, Harri (Corresponding Author); Rajamäki, Riikka; Luoma, Juha.

    In: Accident Analysis and Prevention, Vol. 60, 2013, p. 277-288.

    Research output: Contribution to journalArticleScientificpeer-review

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    T1 - A tool for safety evaluations of road improvements

    AU - Peltola, Harri

    AU - Rajamäki, Riikka

    AU - Luoma, Juha

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    N2 - Road safety impact assessments are requested in general, and the directive on road infrastructure safety management makes them compulsory for Member States of the European Union. However, there is no widely used, science-based safety evaluation tool available. We demonstrate a safety evaluation tool called TARVA. It uses EB safety predictions as the basis for selecting locations for implementing road-safety improvements and provides estimates of safety benefits of selected improvements. Comparing different road accident prediction methods, we demonstrate that the most accurate estimates are produced by EB models, followed by simple accident prediction models, the same average number of accidents for every entity and accident record only. Consequently, advanced model-based estimates should be used. Furthermore, we demonstrate regional comparisons that benefit substantially from such tools. Comparisons between districts have revealed significant differences. However, comparisons like these produce useful improvement ideas only after taking into account the differences in road characteristics between areas. Estimates on crash modification factors can be transferred from other countries but their benefit is greatly limited if the number of target accidents is not properly predicted. Our experience suggests that making predictions and evaluations using the same principle and tools will remarkably improve the quality and comparability of safety estimations.

    AB - Road safety impact assessments are requested in general, and the directive on road infrastructure safety management makes them compulsory for Member States of the European Union. However, there is no widely used, science-based safety evaluation tool available. We demonstrate a safety evaluation tool called TARVA. It uses EB safety predictions as the basis for selecting locations for implementing road-safety improvements and provides estimates of safety benefits of selected improvements. Comparing different road accident prediction methods, we demonstrate that the most accurate estimates are produced by EB models, followed by simple accident prediction models, the same average number of accidents for every entity and accident record only. Consequently, advanced model-based estimates should be used. Furthermore, we demonstrate regional comparisons that benefit substantially from such tools. Comparisons between districts have revealed significant differences. However, comparisons like these produce useful improvement ideas only after taking into account the differences in road characteristics between areas. Estimates on crash modification factors can be transferred from other countries but their benefit is greatly limited if the number of target accidents is not properly predicted. Our experience suggests that making predictions and evaluations using the same principle and tools will remarkably improve the quality and comparability of safety estimations.

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