Robust Freeway Accident Detection: A Two-Stage Approach

Abstract

In this paper, the problem of detecting freeway accidents in real-time based on speed readings from spatially distributed road sensors of variable accuracy is addressed. To ensure robust decision-making, a novel two-stage approach is proposed. Specifically, in the first stage, each sensor generates decisions using a Bayesian quickest change detection framework. In the second stage, individual sensor decisions are aggregated via an optimal stopping approach that optimizes the tradeoff between the costs of aggregation and misclassification. Evaluation of the proposed two-stage approach on a real-world traffic dataset collected from the I405 freeway that passes through the Los Angeles County demonstrates improvements up to 65.2% and 87.2% in average detection delay and probability of false alarm, respectively, as compared to the state-of-the-art.