title: Aggregated analysis of in-depth accident causation data.
abstract: Data collected from in-depth road accident investigations are very informative and may contain more than 500 accident-related variables for a single investigated case. These data may be used to get a more detailed knowledge on accident and injury causation associated with a specific accident scenario. However, due to their complexity, studies using in-depth data at aggregated levels are not common. The objective of this paper is to propose a methodology to analyse aggregated accident causation charts in order to highlight strong and weak relationships between crash causes and pre-crash scenarios. These relationships can be taken into account when developing or assessing new road safety measures (e.g. in-vehicle systems). The methodology has been applied to an in-depth accident dataset derived from the European project SafetyNet. Four different pre-crash scenarios associated with the accident scenario 'vehicles encountering something while remaining in their lane' have been investigated. Even if generalization of these results should be done with care because of database representativeness issues, the methodology is promising, highlighting, for example, a well-defined causation pattern related to vehicles striking a vehicle in rear-end accidents.
authors.affiliation: a Research Centre for Transport and Logistics , 'Sapienza' University , Rome , Italy.
authors.forename: Davide Shingo
authors.initials: DS
authors.lastname: Usami
authors.affiliation: a Research Centre for Transport and Logistics , 'Sapienza' University , Rome , Italy.
authors.forename: Gabriele
authors.initials: G
authors.lastname: Giustiniani
authors.affiliation: a Research Centre for Transport and Logistics , 'Sapienza' University , Rome , Italy.
authors.forename: Luca
authors.initials: L
authors.lastname: Persia
authors.affiliation: a Research Centre for Transport and Logistics , 'Sapienza' University , Rome , Italy.
authors.forename: Roberto
authors.initials: R
authors.lastname: Gigli
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data; D015984:Causality; D007558:Italy; D015233:Models, Statistical; D013223:Statistics as Topic
keywords: accident causation; correspondence analysis; in-depth investigation