MixModel: A Hybrid TimesNet–Informer Architecture with 11-Dimensional Time Features for Enhanced Traffic Flow Forecasting

Chun-Chi Ting, Kuan-Ting Wu, Hui-Ting Christine Lin and Shinfeng Lin ()
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Chun-Chi Ting: Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien 974301, Taiwan
Kuan-Ting Wu: Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien 974301, Taiwan
Hui-Ting Christine Lin: Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan
Shinfeng Lin: Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien 974301, Taiwan

Mathematics, 2025, vol. 13, issue 19, 1-18

Abstract: The growing demand for reliable long-term traffic forecasting has become increasingly critical in the development of intelligent transportation systems (ITS). However, capturing both strong periodic patterns and long-range temporal dependencies presents a significant challenge, and existing approaches often fail to balance these factors effectively, resulting in unstable or suboptimal predictions. To address this issue, we propose MixModel , a novel hybrid framework that integrates TimesNet and Informer to leverage their complementary strengths. Specifically, the TimesNet branch extracts periodic variations through frequency-domain decomposition and multi-scale convolution, while the Informer branch employs ProbSparse attention to efficiently capture long-range dependencies across extended horizons. By unifying these capabilities, MixModel achieves enhanced forecasting accuracy, robustness, and stability compared with state-of-the-art baselines. Extensive experiments on real-world highway datasets demonstrate the effectiveness of our model, highlighting its potential for advancing large-scale urban traffic management and planning. To the best of our knowledge, MixModel is the first hybrid framework that explicitly bridges frequency-domain periodic modeling and efficient long-range dependency learning for long-term traffic forecasting, establishing a new benchmark for future research in Intelligent Transportation Systems.

Keywords: Artificial Intelligence; Deep Learning; pattern recognition; time-series forecasting; traffic flow prediction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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