An automated statistically-principled bottleneck identification algorithm (ASBIA)

Bottlenecks are key features of any freeway system. The deployment of stationary sensors and proliferation of mobile vehicle probes provides researchers with a wealth of data that can be used for the automatic identification of active freeway bottlenecks. In this paper we introduce an automated statistically principled algorithm to characterize traffic into two states: free-flow or congested and subsequently identify the spatiotemporal activation of bottlenecks. The proposed algorithm uses speed measurements over short temporal and spatial intervals and segments, respectively to identify the status of a segment while accounting for spatiotemporal correlations and interactions. The outputs of the algorithm are the status of the roadway segment (free-flow or congested) and the confidence level of the test (p-value). The experimental results based on archived data from the northbound Interstate 5 (I-5) corridor in the Portland, Oregon, metropolitan region demonstrates significant improvements over state-of-the-art bottleneck identification algorithms.