Presentation of Analytical Methods for Better Decision Making about the Most Important Factor Influencing Rural Accidents

Abstract

Due to population growth and the increasing number of vehicles on rural roads, traffic accidents have become one of the most important problems in the transportation system, which greatly affects the social and economic situation of the people. The main purpose of this study was to apply the analytical method to investigate the factors affecting the severity of traffic accidents on rural roads of Guilan, Iran, in order to determine the most effective factor in the occurrence of these accidents. At first, the frequency analysis was used to evaluate the variables and their frequency, then the Friedman test (FT) was applied to prioritize the factors, and the exploratory factor analysis (EFA) was used to determine the most effective factor in the occurrence of vehicle accidents in Guilan rural roads. Based on the FT, weather condition was the most important factor effective in these accidents. According to the results of the EFA, five factors were identified as the main factors involved in accidents in which the first factor contributing to accidents was the environmental factor, including weather condition and road surface condition. This indicates that concurrent result of the FT and the EFA, weather condition as an environmental factor, was identified as the most important factor affecting vehicle accidents on rural roads of Guilan. Finally, safety strategies were proposed to increase safety and reduce accidents along these roads.

Data-Driven Urban Traffic Accident Analysis and Prediction Using Logit and Machine Learning-Based Pattern Recognition Models

“…Since statistical analysis and programming models are usually not able to consider all the required details of the problem, for future studies, it is suggested that to utilize Geographic Information System (GIS) roadway profile data along with more deep learning and optimization techniques to have an in-depth analysis and find the most desirable solutions [65][66][67][68]. Moreover, due to the significant importance of pedestrian accidents and permanent interference of nonmotorized users and vehicles flow within a city, it is recommended to use more analytical methods and pattern recognition type of machine learning approach to provide better decision making approaches and present the most accurate prediction model for pedestrian accidents separately occurring in urban environments [55,69]. Last but not least, to have a more in-depth analysis about pedestrian accidents and find the most dangerous conflicts, it is highly suggested to use AI-based object detection and image processing approaches.…”

Modeling of Pedestrian Flow Density in Signalized Intersections Using Fuzzy Method (Case Study: Rasht City)

“…• For future research, other machine learning techniques [20][21][22][23][24] and optimization algorithms [25][26][27][28] can be incorporated into the proposed approaches to obtain more accurate results.…”

Assessment of the Influence of Technology-Based Distracted Driving on Drivers’ Infractions and Their Subsequent Impact on Traffic Accidents Severity

“…Traffic accidents have become a major health public issue and preservation of the lives of drivers, passengers and users is among the main concerns of communities around the world. The literature revealed the fact that numerous factors contribute to and influence the occurrence of these accidents and the severity of injuries [ 64 , 65 , 66 ]. Although the continuous efforts of governments and numerous traffic safety policies being issued to control traffic accidents, the rates of disabilities, fatalities, and injuries continue to increase dramatically.…”