Spatial Inference of Traffic Transition Using Micro–Macro Traffic Variables


This paper proposes an online traffic inference algorithm for road segments in which local traffic info cannot be directly observed. Using macro-micro traffic variables as inputs, the algorithm consists of 3 main operations. Initial, it uses interarrival time (time headway) statistics from upstream and downstream locations to spatially infer traffic transitions at an unsupervised piece of segment. Second, it estimates lane-level flow and occupancy at the identical unsupervised target site. Third, it estimates individual lane-level shockwave propagation times on the section. Using real-world closed-circuit television knowledge, it's shown that the proposed algorithm outperforms previously proposed methods within the literature.

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