A Natural Language Processing method for the identification of the factors influencing road accident severity

Abstract

Although road safety has improved in the last decades, the rate of accidents with severe and fatal consequences is still exceeding the safety objectives (European Commission 2019; World Health Organization 2018).
This work explores the possibility of using Natural Language Processing (NLP) techniques for the automatic extraction of knowledge from road accidents reports, with the objective of supporting the safety management of the road infrastructure system (Persia et al. 2016).
To this aim, we consider databases of textual reports on road accidents, provided by the local public authorities. These reports contain the descriptions of the accidents and the results of the post-accident investigations. The aim is to analyze the reports by NLP to extract the features that most influence the accidents, for informing road safety management.
For the analysis of the reports, we develop a method that combines Hierarchical Dirichlet Processes (HDPs) (Teh et al. 2006), Artificial Neural Networks (ANNs) and a feature selection technique based on the Sequential Forward Selection (SFS) strategy (Marcano-Cedeño et al. 2010). HDPs allow representing each report as a mixture of topics, i.e. distributions of words co-occurring in the reports. In practice, each report is transformed into a vector whose elements are the degrees of membership to each topic, i.e. a measure of the contribution of each topic to the description of the report. ANNs are then used to classify the reports, represented by the extracted vectors, into classes characterizing the severity of the accident consequences. Finally, the SFS technique is used for identifying those topics which most influence the reports classification. In this way, the factors causing the accidents and influencing its evolution are automatically extracted. The developed method is validated considering a database of real accident reports.

References
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