The Severity of Traffic Crashes in Italy: An Explorative Analysis among Different Driving Circumstances

Analyzing traffic accidents is very important due to their direct impact on the social environment. In the literature, many studies focus on the different aspects that influence traffic accidents, such as human, vehicle, road and environment risk factors. In this paper, we propose a methodology for testing the relationship between road, external environment, driver and vehicle characteristics, and certain circumstances that lead to the traffic crashes. Particularly, we elaborate on logistic regression models for evaluating how these different characteristics impact on crash severity, considering the combination of traffic circumstances that caused the crash. In each combination, a vehicle proceeded regularly, whereas the other vehicle did an incorrect maneuver (the vehicle proceeded: with distracted driving; without maintaining the safety distance; with speeding; by maneuvering to join the circulation flow; against the flow). The present work analyzes data related to road crashes which occurred in Italy during 2016 involving two vehicles. The results show that the variables significantly influencing crash severity are different depending on the combinations of circumstances that cause the crash.

[1]  Chandra R. Bhat,et al.  Unobserved heterogeneity and the statistical analysis of highway accident data , 2016 .

[2]  D Shinar,et al.  Self-reports of safe driving behaviors in relationship to sex, age, education and income in the US adult driving population. , 2001, Accident; analysis and prevention.

[3]  Juan de Oña,et al.  Socio-economic and driving experience factors affecting drivers’ perceptions of traffic crash risk , 2016 .

[4]  S. Dissanayake Comparison of severity affecting factors between young and older drivers involved in single vehicle crashes , 2004 .

[5]  Griselda López,et al.  Analysis of traffic accidents on rural highways using Latent Class Clustering and Bayesian Networks. , 2013, Accident; analysis and prevention.

[6]  Laura Eboli,et al.  Willingness to use mobile application for smartphone for improving road safety , 2016, International journal of injury control and safety promotion.

[7]  Gudmundur F. Ulfarsson,et al.  Driver-injury severity in single-vehicle crashes in California: A mixed logit analysis of heterogeneity due to age and gender. , 2013, Accident; analysis and prevention.

[8]  Guohui Zhang,et al.  A latent class approach for driver injury severity analysis in highway single vehicle crash considering unobserved heterogeneity and temporal influence , 2019 .

[9]  Steven Jones,et al.  Effects of Human-Centered Factors on Crash Injury Severities , 2017 .

[10]  M Hatakka,et al.  Driving Circumstances and Accidents Among Novice Drivers , 2006, Traffic injury prevention.

[11]  Guangnan Zhang,et al.  Risk factors associated with traffic violations and accident severity in China. , 2013, Accident; analysis and prevention.

[12]  Xiaoyan Huo,et al.  Examination of driver injury severity in freeway single-vehicle crashes using a mixed logit model with heterogeneity-in-means , 2019, Physica A: Statistical Mechanics and its Applications.

[13]  A. S. Al-Ghamdi Using logistic regression to estimate the influence of accident factors on accident severity. , 2002, Accident; analysis and prevention.

[14]  Francesco Russo,et al.  From the analysis of European accident data to safety assessment for planning: the role of good vehicles in urban area , 2017, European Transport Research Review.

[15]  Srinivas S Pulugurtha,et al.  Risk drivers pose to themselves and other drivers by violating traffic rules , 2017, Traffic injury prevention.

[16]  Mogens Fosgerau,et al.  Speed and income , 2005 .

[17]  Laura Eboli,et al.  How to define the accident risk level of car drivers by combining objective and subjective measures of driving style , 2017 .

[18]  Manuela Alcañiz,et al.  The impact of traffic violations on the estimated cost of traffic accidents with victims. , 2010, Accident; analysis and prevention.

[19]  George Yannis,et al.  Exploring Crash Injury Severity on Urban Motorways by Applying Finite Mixture Models , 2019, Transportation Research Procedia.

[20]  Fred L. Mannering,et al.  The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives , 2010 .

[21]  George Yannis,et al.  Estimation of Fatality and Injury Risk by Means of In-Depth Fatal Accident Investigation Data , 2010, Traffic injury prevention.

[22]  W. Renner,et al.  Venturesomeness and extraversion as correlates of juvenile drivers' traffic violations. , 2000, Accident; analysis and prevention.

[23]  Laura Eboli,et al.  How usual behaviour can affect perceived drivers’ psychological state while driving , 2015 .

[24]  Laura Eboli,et al.  How to identify the key factors that affect driver perception of accident risk. A comparison between Italian and Spanish driver behavior. , 2014, Accident; analysis and prevention.

[25]  Fred L. Mannering,et al.  The analysis of vehicle crash injury-severity data: A Markov switching approach with road-segment heterogeneity , 2014 .

[26]  David W. Hosmer,et al.  Applied Logistic Regression , 1991 .

[27]  Laura Eboli,et al.  Drivers’ Road Accident Risk Perception. A Comparison Between Face-To-Face Interview and Web-Based Survey , 2014 .