Improved Heterogeneous Spatiotemporal Graph Network Model for Traffic Flow Prediction at Highway Toll Stations

Yaofang Zhang, Jian Chen, Fafu Chen and Jianjie Gao ()
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Yaofang Zhang: College of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China
Jian Chen: College of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China
Fafu Chen: College Electronic and Information Engineering, Southwest University, Chongqing 400715, China
Jianjie Gao: Sichuan Provincial Key Laboratory of Intelligent Policing, Sichuan Police College, Luzhou 646000, China

Sustainability, 2025, vol. 17, issue 17, 1-24

Abstract: This study aims to guide the management and service of highways towards a more efficient and intelligent direction, and also provides intelligent and green data support for achieving sustainable development goals. The forecasting of traffic flow at highway stations serves as the cornerstone for spatiotemporal analysis and is vital for effective highway management and control. Despite considerable advancements in data-driven traffic flow prediction, the majority of existing models fail to differentiate between directions. Specifically, entrance flow prediction has applications in dynamic route guidance, disseminating real-time traffic conditions, and offering optimal entrance selection suggestions. Meanwhile, exit flow prediction is instrumental for congestion and accident alerts, as well as for road network optimization decisions. In light of these needs, this study introduces an enhanced heterogeneous spatiotemporal graph network model tailored for predicting highway station traffic flow. To accurately capture the dynamic impact of upstream toll stations on the target station’s flow, we devise an influence probability matrix. This matrix, in conjunction with the covariance matrix across toll stations, updated graph structure data, and integrated external weather conditions, allows the attention mechanism to assign varied combination weights to the target toll station from temporal, spatial, and external standpoints, thereby augmenting prediction accuracy. We undertook a case study utilizing traffic flow data from the Chengdu-Chengyu station on the Sichuan Highway to gauge the efficacy of our proposed model. The experimental outcomes indicate that our model surpasses other baseline models in performance metrics. This study provides valuable insights for highway management and control, as well as for reducing traffic congestion. Furthermore, this research highlights the importance of using data-driven approaches to reduce carbon emissions associated with transportation, enhance resource allocation at toll plazas, and promote sustainable highway transportation systems.

Keywords: sustainable transportation; intelligent transportation; highway; traffic prediction; spatiotemporal characteristics; heterogeneous graph network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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