Multi-step citywide traffic flow forecasting based on multiscale spatio-temporal transformer
Year: 2025
Type: article
Abstract: Accurate citywide traffic flow forecasting is an essential task in intelligent transportation systems. Unlike single-step forecasting, multi-step traffic flow forecasting offers extended insights that support proactive traffic management and resource allocation over longer time horizons. This paper proposes a Multi-Step Multiscale Spatial–Temporal Transformer (MS-MSTformer) for citywide traffic flow forecasting, which leverages a multiscale patch mechanism to capture both local and global spatial dependencies while integrating temporal patterns of closeness, period, and trend. Two novel cross-attention modules, namely Patch-Temporal Cross-Attention (PTCA) and Region-Temporal Cross-Attention (RTCA) are presented. These modules utilize temporal information as the query, with PTCA and RTCA focusing on patches and regions, respectively, to effectively fuse diverse spatio-temporal features. Extensive experiments on the widely used New York City Taxi (NYCTaxi) and New York City Bike (NYCBike) datasets demonstrate the MS-MSTformer’s capability to provide accurate multi-step citywide traffic flow forecasting. Specifically, the proposed model outperforms the baseline models in 11 out of 12 evaluation scenarios. On average, MS-MSTformer improves Root Mean Square Error (RMSE) by 31.13% and Mean Absolute Error (MAE) by 29.22% over the deep learning baselines. In addition, the ablation study demonstrates the contributions of both PTCA and RTCA to the proposed MS-MSTformer.
Authors: Shenkai Zhang, Taiyong Li, Yingqi Chen, Jiang Wu, Xiao Yan
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,200
Award: G647001345
OpenAlex ID: W4411369798