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

Abstract

Modeling the severity of accidents based on the most effective variables accounts for developing a high-precision model presenting the possibility of occurrence of each category of future accidents, and it could be utilized to prioritize the corrective measures for authorities. The purpose of this study is to identify the variables affecting the severity of the injury, fatal, and property damage only (PDO) accidents in Rasht city by collecting information on urban accidents from March 2019 to March 2020. In this regard, the multiple logistic regression and the pattern recognition type of artificial neural network (ANN) as a machine learning solution are used to recognize the most influential variables on the severity of accidents and the superior approach for accident prediction. Results show that the multiple logistic regression in the forward stepwise method has R2 of 0.854 and an accuracy prediction power of 89.17%. It turns out that the accidents occurred between 18 and 24 and KIA Pride vehicle has the highest effect on increasing the severity of accidents, respectively. The most important result of the logit model accentuates the role of environmental variables, including poor lighting conditions alongside unfavorable weather and the dominant role of unsafe and poor quality of vehicles on increasing the severity of accidents. In addition, the machine learning model performs significantly better and has higher prediction accuracy (98.9%) than the logit model. In addition, the ANN model’s greater power to predict and estimate future accidents is confirmed through performance and sensitivity analysis.

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.…”

Understanding the Factors Affecting Urban Vehicle-to-Vehicle Crash Severity with Focus on Drivers' Route Familiarity

“…Guilan is the 28 th biggest province in Iran, while it is the 12 th province by population [16,24,25]. Rasht (Figure 1) is its capital and one of the most congested cities in Iran, and the most populous city in the north of Iran, with high traffic volume most days during the year [26]. According to the 2016 official census, the city's population is 680,000.…”

Application of Artificial Neural Network Approaches for Predicting Accident Severity on Rural Roads (Case Study: Tehran-Qom and Tehran-Saveh Rural Roads)

“…Furthermore, statistics of forensic medicine show that road accidents in rural areas have hit 101,996 persons from 2011 to 2016 [6]. So, to reduce accidents, the safety authorities should adopt appropriate solutions to enhance road safety and reduce the likelihood of accidents [7,8]. Determining the factors associated with accident severity enables tra c engineers to lessen accidents in developing countries such as Iran, and thus the tra c safety can be improved by analyzing accident characteristics [9,10].…”