A Hierarchical Framework for Timely Freeway Accident Detection and Localization

The increase of motor vehicle accidents over the past decades has made their early and accurate detection crucial not only for saving human lives, but also for decreasing time and energy wasted due to congestion. Unlike existing work that has thus far focused on accident detection, this work recognizes the problem of accident localization as an equally important challenge to be addressed. A novel algorithm is proposed to optimally detect the time of an accident and subsequently estimate its geographic location in near-real-time based on speed sensor readings. Evaluation on a large-scale real-world dataset demonstrates that the proposed approach achieves significant gains compared to state-of-the-art with respect to false alarm rate and average detection delay, while at the same time being capable of pinpointing the exact location of accidents within less than 2 miles on average.