United States Road Accident Prediction using Random Forest Predictor
Dominic Parosh Yamarthi, Haripriya Raman, Shamsad Parvin
TL;DR
Predicts nationwide accident counts using a large, multi-source dataset and a Random Forest regression model to capture nonlinear interactions among environmental, road, and temporal factors. The study demonstrates strong predictive performance with $MSE$ = 85.21 and $R^2$ = 0.75, and shows that ensemble methods can further enhance accuracy. The findings support proactive safety planning and resource optimization across the United States, with potential for finer-grained spatiotemporal extensions and expanded data integration in future work.
Abstract
Road accidents significantly threaten public safety and require in-depth analysis for effective prevention and mitigation strategies. This paper focuses on predicting accidents through the examination of a comprehensive traffic dataset covering 49 states in the United States. The dataset integrates information from diverse sources, including transportation departments, law enforcement, and traffic sensors. This paper specifically emphasizes predicting the number of accidents, utilizing advanced machine learning models such as regression analysis and time series analysis. The inclusion of various factors, ranging from environmental conditions to human behavior and infrastructure, ensures a holistic understanding of the dynamics influencing road safety. Temporal and spatial analysis further allows for the identification of trends, seasonal variations, and high-risk areas. The implications of this research extend to proactive decision-making for policymakers and transportation authorities. By providing accurate predictions and quantifiable insights into expected accident rates under different conditions, the paper aims to empower authorities to allocate resources efficiently and implement targeted interventions. The goal is to contribute to the development of informed policies and interventions that enhance road safety, creating a safer environment for all road users. Keywords: Machine Learning, Random Forest, Accident Prediction, AutoML, LSTM.