Big data analytics-based traffic flow forecasting using inductive spatial-temporal network

Chunyang Hu (), Bin Ning, Qiong Gu, Junfeng Qu, Seunggil Jeon and Bowen Du
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Chunyang Hu: Hubei University of Arts and Science
Bin Ning: Hubei University of Arts and Science
Qiong Gu: Hubei University of Arts and Science
Junfeng Qu: Hubei University of Arts and Science
Seunggil Jeon: Samsung Electronics
Bowen Du: Beihang University

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2025, vol. 27, issue 10, No 62, 24799-24815

Abstract: Abstract Traffic flow forecasting is crucial for urban traffic management, which alleviates traffic congestion. However, one inherent feature of urban traffic is it’s instability, making it difficult to accurately forecast the future traffic flow. In this paper, we propose a model using Inductive Spatial-Temporal Network to predict the traffic flow speed of road networks. Specifically, we first utilize GraphSAGE(Graph SAmple and aggreGatE) to inductively extract the spatial features of road networks. Furthermore, we design a global temporal block to capture the temporal pattern. Then, we adopt the self-attention mechanism for evaluating the importance of nodes. Finally we introduced an autoregressive module to increase the robustness of the model. Experiments on real-world data demonstrate that considering spatial and temporal dependencies of the traffic data can achieves better performance than models without considering such relations.

Keywords: Inductive spatial-temporal network; GraphSAGE; Global temporal block (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10668-022-02585-z

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