Short-term freeway traffic speed multistep prediction using an iTransformer model

Yajie Zou, Yubin Chen, Yajiao Xu, Hao Zhang and Siyang Zhang

Physica A: Statistical Mechanics and its Applications, 2024, vol. 655, issue C

Abstract: Accurate prediction of the freeway traffic speed is important for enhancing intelligent transportation management and assisting with route planning. Current traffic speed prediction studies usually neglect predictions extending to an hour or longer. Thus, to address this gap, the inverted Transformer (iTransformer) model is utilized to predict freeway speeds across intervals from 5 to 150 minutes. The iTransformer model provides a global view by encapsulating the entire time series from each detector into variate tokens, allowing it to capture the complex patterns and dependencies that change over time. Additionally, a multi-head attention mechanism is employed to identify short-term and long-term speed patterns. This study validates the prediction performance of iTransformer by using the traffic speed data from an interstate freeway in Minnesota, comparing it against traditional traffic prediction methods and two other Transformer models. Results indicate that the iTransformer model outperforms these benchmark approaches, particularly when predicting speed over 60 minutes for peak hour periods.

Keywords: Deep learning; ITransformer; Traffic speed; Multistep prediction (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:655:y:2024:i:c:s0378437124006940

DOI: 10.1016/j.physa.2024.130185

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