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Open Access

ARTICLE

Bi-STAT+: An Enhanced Bidirectional Spatio-Temporal Adaptive Transformer for Urban Traffic Flow Forecasting

Yali Cao1, Weijian Hu1,2, Lingfang Li1,*, Minchao Li1, Meng Xu2, Ke Han2
1 Digital Intelligence Industry Academy, Inner Mongolia University of Science and Technology, Baotou, 014010, China
2 School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, 611756, China
* Corresponding Author: Lingfang Li. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.069373

Received 21 June 2025; Accepted 18 September 2025; Published online 16 October 2025

Abstract

Traffic flow prediction constitutes a fundamental component of Intelligent Transportation Systems (ITS), playing a pivotal role in mitigating congestion, enhancing route optimization, and improving the utilization efficiency of roadway infrastructure. However, existing methods struggle in complex traffic scenarios due to static spatio-temporal embedding, restricted multi-scale temporal modeling, and weak representation of local spatial interactions. This study proposes Bi-STAT+, an enhanced bidirectional spatio-temporal attention framework to address existing limitations through three principal contributions: (1) an adaptive spatio-temporal embedding module that dynamically adjusts embeddings to capture complex traffic variations; (2) frequency-domain analysis in the temporal dimension for simultaneous high-frequency details and low-frequency trend extraction; and (3) an agent attention mechanism in the spatial dimension that enhances local feature extraction through dynamic weight allocation. Extensive experiments were performed on four distinct datasets, including two publicly benchmark datasets (PEMS04 and PEMS08) and two private datasets collected from Baotou and Chengdu, China. The results demonstrate that Bi-STAT+ consistently outperforms existing methods in terms of MAE, RMSE, and MAPE, while maintaining strong robustness against missing data and noise. Furthermore, the results highlight that prediction accuracy improves significantly with higher sampling rates, providing crucial insights for optimizing real-world deployment scenarios.

Keywords

Traffic flow prediction; spatio-temporal feature modeling; transformer; intelligent transportation; deep learning

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