Examining causal factors of traffic instability using traffic flow and driving behavior data

Developing proactive prevention measures before accidents is a powerful means of kerbing traffic accidents. Many studies have confirmed that the state of the traffic would fluctuate significantly before an accident, representing as traffic instability. This study develops a Traffic Instability Index (TII) to evaluate and identify factors affecting traffic instability, utilizing Non-negative Matrix Factorization (NMF) based on aggressive driving behaviors and traffic flow data. Machine learning algorithms and Partial Dependency Plots (PDP) reveal that traffic status significantly influence TII, followed by road attributes. The findings show that high TII is linked to more fatal accidents and that traffic instability is adversely impacted by increased speed differences, traffic volume, and the number of entrances and exits, but reduced by the proportion of trucks and median openings. Vehicle type mix has a non-linear effect on TII. Additionally, median openings mitigate, while entrances and exits exacerbate the adverse effects of vehicle mix and truck proportion on instability.