PROJECT TITLE :

Spatial Inference of Traffic Transition Using Micro–Macro Traffic Variables

ABSTRACT:

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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