QUICKEST FREEWAY ACCIDENT DETECTION UNDER UNKNOWN POST-ACCIDENT CONDITIONS

Accurate traffic accident detection is crucial to improving road safety conditions and route navigation, and to making informed decisions in urban planning among others. This paper proposes a Bayesian quickest change detection approach for accurate freeway accident detection in near-real-time based on speed sensor readings. Since post-accident conditions are hardly known, a maximum likelihood method is devised to track the relevant unknown parameters over time. Four aggregation schemes are designed to exploit the spatial correlation among sensors. Evaluation on real-world data collected from the I405 freeway in the Los Angeles County demonstrates significant gains as compared to the state-of-the-art in terms of average detection delay and probability of false alarm by up to 58.9% and 81.5%, respectively.