A data-driven approach to determining freeway incident impact areas with fuzzy and graph theory-based clustering

摘要

Determining spatiotemporal impact areas of incidents plays a significant role in incident impact analysis. Although existing empirical methods have proven to be promising, they suffer from the drawbacks that limit their wide applications in automated freeway safety management. This study presents a data-driven approach to automatically determining the spatiotemporal impact areas of freeway incidents. The spatiotemporal contour plots were first constructed using three representative traffic measures. Next, a nonrecurrent congestion area identification method based on fuzzy clustering was developed. To distinguish possible multiple independent blocks in the nonrecurrent congestion area, a clustering algorithm based on graph theory was adopted. The incident impact areas were then determined by conducting a postprocessing strategy. The incident records and the associated traffic flow data, collected on 1-5 freeway segments in San Diego Region, CA, were used to evaluate the proposed approach. Experimental results show the proposed approach can automatically and properly determine incident impact areas while accounting for the uncertainty resulting from traffic variations.