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

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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.

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Data availability statement

Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data is not available.

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Funding

Funding was provided by the Guidance Programs of Science and Technology Funds of the Xiangyang city (2020ZD32), the Major Research Development Program of Hubei Province (No.2020BBB092).

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Authors and Affiliations

  1. School of Computer Engineering, Hubei University of Arts and Science, Xiangyang, 441053, Hubei, China

    Chunyang Hu, Bin Ning, Qiong Gu & Junfeng Qu

  2. Samsung Electronics, 129, Samseong-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, 16677, South Korea

    Seunggil Jeon

  3. School of Computer Science and Engineering, Beihang University, Beijing, 100191, China

    Bowen Du

Authors
  1. Chunyang Hu
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  2. Bin Ning
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  3. Qiong Gu
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  6. Bowen Du
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Correspondence to Chunyang Hu.

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Hu, C., Ning, B., Gu, Q. et al. Big data analytics-based traffic flow forecasting using inductive spatial-temporal network. Environ Dev Sustain (2022). https://doi.org/10.1007/s10668-022-02585-z

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