Advanced analytics for predicting traffic collision severity assessment

Accurate prediction of accident risks plays a crucial role in proactively implementing safety measures and allocating resources effectively. This paper introduces an innovative approach aimed at improving accident risk prediction by harnessing unique data sources and extracting insights from diverse yet sparse datasets. Traditional models often face limitations due to a lack of diversity and scope in the available data, which hinders their predictive capabilities. In response to this challenge, our study integrates a broad spectrum of heterogeneous data, encompassing traffic flow, weather conditions, road infrastructure details, and historical accident records. To overcome the difficulties associated with sparse data, we employ advanced data science techniques such as feature engineering, imputation, and machine learning. The paper introduces a novel dataset that amalgamates various data types, establishing a robust foundation for our predictive model. Through meticulous analysis, we derive valuable insights from these diverse sources, significantly enhancing our ability to assess accident risks. The proposed approach offers numerous advantages, including the capacity to predict accidents in areas that were previously underrepresented and under varying conditions. We rigorously evaluate the model's performance through extensive experimentation and validate its accuracy using real-world accident data. Our results indicate substantial improvements in prediction accuracy compared to conventional models. This research contributes significantly to the field of accident risk prediction by highlighting the potential benefits of integrating heterogeneous sparse data and leveraging advanced data science techniques. The study underscores the importance of tapping into novel data sources and extracting concealed patterns and insights to promote safety and optimize resource allocation in accident-prone regions, fostering more secure environments.