Methodology for the Identification of Vehicle Congestion Based on Dynamic Clustering

Abstract

Addressing sustainable mobility in urban areas has become a priority in today’s society, given the growing population and increasing vehicular flow in these areas. Intelligent Transportation Systems have emerged as innovative and effective technological solutions for addressing these challenges. Research in this area has become crucial, as it contributes not only to improving mobility in urban areas but also to positively impacting the quality of life of their inhabitants. To address this, a dynamic clustering methodology for vehicular trajectory data is proposed which can provide an accurate representation of the traffic state. Data were collected for the city of San Francisco, a dynamic clustering algorithm was applied and then an indicator was applied to identify areas with traffic congestion. Several experiments were also conducted with different parameterizations of the forgetting factor of the clustering algorithm. We observed that there is an inverse relationship between forgetting and accuracy, and the tolerance allows for a flexible margin of error that allows for better results in precision. The results showed in terms of precision that the dynamic clustering methodology achieved high match rates compared to the congestion indicator applied to static cells.