Feasibility of Stochastic Models for Evaluation of Potential Factors for Safety: A Case Study in Southern Italy

Giuseppe Guido,Ashkan Shafiee Haghshenas,Alessandro Vitale,Vittorio Astarita,Sami Shaffiee Haghshenas,Sina Shaffiee Haghshenas

doi:10.3390/su12187541

Giuseppe Guido, Ashkan Shafiee Haghshenas + Show 4 more

Open Access

https://doi.org/10.3390/su12187541

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Journal: Sustainability	Publication Date: Sep 12, 2020
Citations: 24	License type: CC BY 4.0

Affiliation: McMaster University, University of Calabria

Abstract

There is no definite conclusion about what the main variables that play a fundamental role in road safety are. Therefore, the identification of significant factors in road accidents has been a primary concern of the transportation safety research community for many years. Every accident is influenced by multiple variables that, in a given time interval, concur to cause a crash scenario. Information coming from crash reports is very useful in traffic safety research, and several reported crash variables can be analyzed with modern statistical methods to establish whether a classification or clustering of different crash variables is possible. Hence, this study aims to use stochastic techniques for evaluating the role of some variables in accidents with a clustering analysis. The variables that are considered in this paper are light conditions, weekday, average speed, annual average daily traffic, number of vehicles, and type of accident. For this purpose, a combination of particle swarm optimization (PSO) and the genetic algorithm (GA) with the k-means algorithm was used as the machine-learning technique to cluster and evaluate road safety data. According to a multiscale approach, based on a set of data from two years of crash records collected from rural and urban roads in the province of Cosenza, 154 accident cases were accurately investigated and selected for three categories of accident places, including straight, intersection, and other, in each urban and rural network. PSO had a superior performance, with 0.87% accuracy on urban and rural roads in comparison with GA, although the results of GA had an acceptable degree of accuracy. In addition, the results show that, on urban roads, social cost and type of accident had the most and least influence for all accident places, while, on rural roads, although the social cost was the most notable factor for all accident places, the type of accident had the least effect on the straight sections and curves, and the number of vehicles had the least influence at intersections.

Highlights

Traffic accidents are in the top 10 major causes of injuries and deaths [1], representing, with the associated economic loss, an important socioeconomic concern
The identification of the significant factors associated with such crashes has become of major interest and a great challenge in transportation safety research
There are many problems related to the inadequacy and difficulty of obtaining data for analysis as well as the limited crash counts

Summary

Introduction

Traffic accidents are in the top 10 major causes of injuries and deaths [1], representing, with the associated economic loss, an important socioeconomic concern For these reasons, the identification of the significant factors associated with such crashes has become of major interest and a great challenge in transportation safety research. Among them there are Poisson or negative binomial (NB) models [3,4,5,6,7] and zero-inflated Poisson or NB models [5,8,9] These safety models offer, as an advantage, the opportunity to correlate the specific roadway characteristics and expected crash frequencies. In the scientific literature, there is a need to develop novel algorithms to handle the traffic crash records efficiently

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