A Crash Data Analysis through a Comparative Application of Regression and Neural Network Models

One way to reduce road crashes is to determine the main influential factors among a long list that are attributable to driver behavior, environmental conditions, vehicle features, road type, and traffic signs. Hence, selecting the best modelling tool for extracting the relations between crash factor...

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Bibliographic Details
Published in:Safety (Basel) 2023-04, Vol.9 (2), p.20
Main Authors: Mussone, Lorenzo, Alizadeh Meinagh, Mohammadamin
Format: Article
Language:English
Subjects:
Analysis
artificial neural networks
Automobile driving
Data analysis
Datasets
driver behavior
Fatalities
Generalized linear models
Infrastructure
Injuries
Machine learning
Mathematical functions
Mathematical models
model performance analysis
Motor vehicle driving
Neural networks
Questionnaires
Regression analysis
regression models
Research methodology
road crashes
Roads & highways
Social science research
Traffic
Traffic accidents
Traffic accidents & safety
Traffic safety
Variables
Vehicles
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Summary:One way to reduce road crashes is to determine the main influential factors among a long list that are attributable to driver behavior, environmental conditions, vehicle features, road type, and traffic signs. Hence, selecting the best modelling tool for extracting the relations between crash factors and their outcomes is a crucial task. To analyze the road crash data of Milan City, Italy, gathered between 2014–2017, this study used artificial neural networks (ANNs), generalized linear mixed-effects (GLME), multinomial regression (MNR), and general nonlinear regression (NLM), as the modelling tools. The data set contained 35,182 records of road crashes with injuries or fatalities. The findings showed that unbalanced and incomplete data sets had an impact on outcome performance, and data treatment methods could help overcome this problem. Age and gender were the most influential recurrent factors in crashes. Additionally, ANNs demonstrated a superior capability to approximate complicated relationships between an input and output better than the other regression models. However, they cannot provide an analytical formulation, but can be used as a baseline for other regression models. Due to this, GLME and MNR were utilized to gather information regarding the analytical framework of the model, that aimed to construct a particular NLM.
ISSN:2313-576X
2313-576X
DOI:10.3390/safety9020020