Prediction and Analysis of the Severity and Number of Suburban Accidents Using Logit Model, Factor Analysis and Machine Learning: A case study in a developing country

Abstract

The purpose of this study is to investigate and determine the factors affecting vehicle and pedestrian accidents taking place in the busiest suburban highway of Guilan Province located in the north of Iran and provide the most accurate prediction model. Therefore, the effective principal variables and the probability of occurrence of each category of crashes are analyzed and computed utilizing the factor analysis, logit, and Machine Learning approaches simultaneously. This method not only could contribute to achieving the most comprehensive and efficient model to specify the major contributing factor, but also it can provide officials with suggestions to take effective measures with higher precision to lessen accident impacts and improve road safety. Both the factor analysis and logit model show the significant roles of exceeding lawful speed, rainy weather and driver age (30–50) variables in the severity of vehicle accidents. On the other hand, the rainy weather and lighting condition variables as the most contributing factors in pedestrian accidents severity, underline the dominant role of environmental factors in the severity of all vehicle-pedestrian accidents. Moreover, considering both utilized methods, the machine-learning model has higher predictive power in all cases, especially in pedestrian accidents, with 41.6% increase in the predictive power of fatal accidents and 12.4% in whole accidents. Thus, the Artificial Neural Network model is chosen as the superior approach in predicting the number and severity of crashes. Besides, the good performance and validation of the machine learning is proved through performance and sensitivity analysis.

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

“…e dependent variable in this study is the severity of accidents, which are classified into three categories: fatal, injury, and PDO accidents. Since the amount of fatal accidents is small in comparison to total accidents, and the independent variables significance and goodness of fit of a model cannot be satisfied by considering the three types of dependent variables, fatal accidents have been merged with injury accidents, and the 2 Mathematical Problems in Engineering dependent variable has been split into two categories: PDO and injury/fatal accidents [55]. Table 1 classifies the independent variables influencing the occurrence of accidents in Rasht city and the appropriate coding for each of them for modeling purposes.…”

“…Given the qualitative data used in this analysis, the prediction model is created using a neural network with pattern recognition capabilities. Using either supervised or unsupervised grouping, pattern recognition divides input data into objects or classes based on main characteristics [55].…”

“…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.…”

“…eir studies showed the outstanding role of environmental factors, such as rainy weather and inadequate lighting condition, on increasing the severity of pedestrian accidents [55].…”

Analysis of Factors Associated with Traffic Injury Severity on Urban Roads in Different Lighting Conditions

“…The frequency of accidents in the study area based on lighting conditions (based on Rasht City urban traffic police database from 2015 to 2021 using KAM 114 forms) shows 7144, 2848, and 256 accidents for daylight and night with and without enough lighting, respectively. Due to the objective of this study, the target variable is the accident severity, a binary variable including two values of no injury or injury/fatal [15,16]. The independent variables consist of 13 variables, including human, environmental, and road characteristics-related factors.…”