A Machine Learning Approach to Traffic Congestion Hotspot Identification and Prediction

Travel-time delays due to recurring congestion cause productivity loss, increase the likelihood of accidents, and lead to environmental pollution due to greenhouse gas emissions. The National Highway Traffic Safety Administration in the United States has listed several driver assistance technologies that are now common in most newer vehicles. While these technologies can help reduce the likelihood of traffic-related accidents, they do little to reduce recurring congestion in urban areas. Recurring congestion during rush hours is prevalent, for example, along Interstate 95 and Capital Beltway 495 in the Baltimore-Washington area. Such congestion also enhances the likelihood of crashes. Previous approaches to hotspot identification are primarily theoretical, which limits their practical applicability. In this paper, we develop a Machine Learning (ML) approach that integrates geospatial data with artificial neural networks to predict traffic congestion hotspots during rush hour. The approach uses live traffic sensor data. A case study from Maryland is presented. The result shows top hotspot segments across Maryland. Using a snapshot of hotspots at eight different time periods, the likelihood of hotspot locations is predicted using an artificial neural network. The framework is validated using live loop detector data (speed and volume) from Maryland freeways, particularly I-495 and I-95. The research can serve as a valuable tool for traffic congestion hotspot identification and travel-time prediction.