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Citation |
Roqué C, Lourenço Cardoso J, Connell T, Schermers G, Weber R. Accid. Anal. Prev. 2019; 131: 336-349. |
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Affiliation |
RW, Germany. Electronic address: rolandweber@gmx.de. |
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Copyright |
(Copyright © 2019, Elsevier Publishing) |
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DOI |
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PMID |
31377497 |
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Abstract |
Under the Safe System framework, Road Authorities have a responsibility to deliver inherently safe roads and streets. Addressing this problem depends on knowledge of the road network safety conditions and the number of funds available for new road safety interventions. It also requires the prioritisation of the various interventions that may generate benefits, increasing safety, while ensuring that reasonable steps are taken to remedy the deficiencies detected within a reasonable timeframe. In this context, Road Safety Inspections (RSI) are a proactive tool for identifying safety issues, consisting of a regular, systematic, on-site inspection of existing roads, covering the whole road network, carried out by trained safety expert teams. This paper aims to describe how topic modelling can be effectively used to identify co-occurrence patterns of attributes related to the run-off-road crashes, as well as the corresponding patterns of road safety interventions, as described in the RSI reports. We apply latent Dirichlet allocation (LDA), a widespread method for fitting a topic model, to analyse the topics mentioned in RSI reports, divided into two groups: problems found; and proposed solutions. For this study, 54 RSI gathered over six years (2012-2017) were analysed, covering 4011 km of Irish roads. The results indicate that important keywords relating to the "forgiving roadside" and "clear zone" concepts, as well as the relevant European technical standards (CEN-EN1317 and EN 12,767), are absent from the extracted latent topics. We also found that the frequency of topics related to roadside safety is higher in the problems record set than in the solutions record set, meaning that problems are more easily identified and related to the roadside area than interventions may be. This paper presents methodological empirical evidence that the LDA is appropriate for identifying the co-occurrence patterns of attributes related to the ROR crashes in road safety inspections' reports, as well as the interventions' patterns associated with these crashes. Also, it provides valuable information aimed to determine the extent to which national road authorities in Europe and their contractors are currently capable of implementing and maintaining compliance with roadside standards and guidelines throughout the life cycle of roads. Language: en |
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Keywords |
Latent Dirichlet allocation; Road Safety Inspection; Roadside safety; Text mining; Topic Modeling |