Transformer model-based multi-scale fine-grained identification and classification of regional traffic states.

ABSTRACT: To address the limitations in precision of conventional traffic state estimation methods, this article introduces a novel approach based on the Transformer model for traffic state identification and classification. Traditional methods commonly categorize traffic states into four or six classes; however, they often fail to accurately capture the nuanced transitions in traffic states before and after the implementation of traffic congestion reduction strategies. Many traffic congestion reduction strategies can alleviate congestion, but they often fail to effectively transition the traffic state from a congested condition to a free-flowing one. To address this issue, we propose a classification framework that divides traffic states into sixteen distinct categories. We design a Transformer mod

SUBMITTER: Zhang J 

PROVIDER: S-EPMC11784819 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature