Spatio-temporal traffic queue detection for uninterrupted flows

Abstract

When traffic flow exceeds capacity because of demand fluctuations, crashes, work zones, and special events, a traffic queue is formed on a highway. Traffic queues cause potentially hazardous situations at the end of the queue where drivers unexpectedly face slowed or stopped traffic while approaching at high speed. Therefore, detecting a queue is vital for protecting it. This study presents a real-time spatio-temporal traffic queue detection algorithm that builds on traffic flow fundamentals combined with a statistical pattern recognition procedure. Using flow-density data, traffic flow phase is classified as either congested or uncongested flow in a probabilistic manner, based on Gaussian mixture models for each location in such a way that detects the traffic phase transitions. The proposed detection algorithm was applied to detect traffic queues using traffic detector data from Interstate 40 in Knoxville, Tennessee. The detection results show that the algorithm detects queues successfully by accounting for varying queueing conditions and different queue types. In addition, the proposed algorithm performed better than speed threshold methods in the literature.