Enhancing Accident Risk Prediction with Novel Data and Findings from Heterogeneous Sparse Sources

Abstract

Accurate accident risk prediction is paramount for proactive safety measures and resource allocation. This paper introduces an innovative approach to enhance accident risk prediction by leveraging novel data sources and uncovering insights from heterogeneous sparse data. Traditional models often suffer from limitations in data diversity and scope, hindering their predictive capabilities. In response, our study integrates a wide range of heterogeneous data, including traffic flow data, weather conditions, road infrastructure, and historical accident records. To address the challenges of working with sparse data, we employ advanced data science techniques, including feature engineering, imputation, and machine learning. The paper presents a new dataset that combines diverse data types, providing a comprehensive foundation for our predictive model. Through rigorous analysis, we extract valuable insights from these heterogeneous sources to improve accident risk assessment. The proposed approach offers several advantages, including the ability to predict accidents in previously underrepresented areas and under varying conditions. We evaluate the model's performance through extensive experimentation and validate its accuracy against real-world accident data. Our findings demonstrate significant enhancements in prediction accuracy compared to conventional models. This research contributes to the field of accident risk prediction by showcasing the potential of heterogeneous sparse data integration and advanced data science techniques. It highlights the importance of utilizing novel data sources and the value of uncovering hidden patterns and insights to foster safer environments and more efficient resource allocation in accident-prone areas.