A data mining approach to investigate patterns of powered two-wheeler crashes in Spain

Alfonso Montella; Rocìo de Oña; Filomena Mauriello; Maria Rella Riccardi; Giuseppe Silvestro

doi:10.1016/j.aap.2019.07.027

A data mining approach to investigate patterns of powered two-wheeler crashes in Spain

Accid Anal Prev. 2020 Jan:134:105251. doi: 10.1016/j.aap.2019.07.027. Epub 2019 Aug 9.

Authors

Alfonso Montella¹, Rocìo de Oña², Filomena Mauriello³, Maria Rella Riccardi³, Giuseppe Silvestro³

Affiliations

¹ University of Naples Federico II, Department of Civil, Architectural and Environmental Engineering, Via Claudio 21, 80125 Naples, Italy. Electronic address: alfonso.montella@unina.it.
² University of Granada, TRYSE Research Group, Department of Civil Engineering, Spain.
³ University of Naples Federico II, Department of Civil, Architectural and Environmental Engineering, Via Claudio 21, 80125 Naples, Italy.

PMID: 31402051
DOI: 10.1016/j.aap.2019.07.027

Abstract

Powered two-wheelers (PTWs) are growing globally each year as they are considered an attractive alternative to cars (flexible, small, affordable, fast and easy to park), especially on congested traffic situations. However, PTWs represent an important challenge for road safety. In fact, in 2016, Spain ranked fifth in terms of PTW fatalities among EU 28. For this reason, this paper aims to investigate which are the patterns among crash characteristics contributing to PTW crashes in Spain. Data from 78,611 crashes involving PTWs occurred in Spain in the period 2011-2013 were analyzed. The analysis was performed by using classification trees and rules discovery which are suitable models aimed at extracting knowledge and identifying valid and understandable patterns from large amounts of data previously unknown and indistinguishable. The response variables assessed in this study were severity and crash type. As a result, several combinations of road, environmental and drivers' characteristics associated with severity and typology of PTW crashes in Spain were identified. Based on the analysis results, several countermeasures to solve or mitigate the safety issues identified in the study were proposed. From the methodological point of view, study results show that both the classification trees and the a priori algorithm were effective in providing non-trivial and unsuspected relations in the data. Classification trees structure allowed a simpler understanding of the phenomenon under study while association discovery provided new information which was previously hidden in the data. Given that the results of the two different techniques were never contradictory, we recommend using classification trees and association discovery as complementary approaches since their combination is effective in exploring data providing meaningful insights about PTW crash characteristics and their interdependencies.

Keywords: Classification trees; Data mining; Head-on crashes; Injury severity; Powered two-wheelers; Rules discovery; Run-off-the-road crashes.

MeSH terms

Accidents, Traffic / mortality*
Algorithms
Data Mining / methods*
Humans
Motorcycles / statistics & numerical data*
ROC Curve
Spain / epidemiology