Predicting Traffic Congestion at Urban Intersections Using Data-Driven Modeling

arXiv:2404.08838v1 Announce Type: new
Abstract: Traffic congestion at intersections is a significant issue in urban areas, leading to increased commute times, safety hazards, and operational inefficiencies. This study aims to develop a predictive model for congestion at intersections in major U.S. cities, utilizing a dataset of trip-logging metrics from commercial vehicles across 4,800 intersections. The dataset encompasses 27 features, including intersection coordinates, street names, time of day, and traffic metrics (Kashyap et al., 2019). Additional features, such as rainfall/snowfall percentage, …

abstract arxiv cities commercial congestion cs.lg data data-driven dataset hazards issue logging major metrics modeling predictive safety study traffic traffic congestion trip type urban vehicles