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Dynamic Emergency Transit Forecasting with IoT Sequential Data

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Abstract

Medical emergency transit counts minutes as real human lives. It is important to plan emergency transport routes according to real-time traffic flow status which leads to the the essential requirement of correct dynamic traffic prediction. Many Internet of Things (IoT) devices have been employed to assist emergency transit. Dynamic traffic flow patterns can be better predicted using data given by those devices. In small cities, however, the data are sent into separated management offices or just saved inside edge devices due to system compatibility or the cost of mobile network to computer centres. This condition leads to small and local datasets. Making full use of small local data to conduct prediction is one way to solve local emergency planning problems. In this work, we design a dynamic graph structure to work with Graph Neural Network (GNN) algorithm to forecast traffic flow levels considering this scenario. The proposed graph considers both geographical and time information with the potential to grow within a local mobile communication network. The commonly used Extreme Gradient Boosting (XGBoost) is included in the comparison. Experimental results show that our new design provides high prediction efficiency and accuracy.

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Data Availability

The dataset is available from the corresponding author on reasonable request.

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Funding

This work is supported by National Key R &D Program of China 2018AAA0101703, Shandong Key R &D Program 2019JZZY021005 and Natural Science Foundation of Shandong ZR2020MF067.

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Authors and Affiliations

  1. School of EE, University of Jinan, 250022, Jinan, Shandong, China

    Bin Sun, Renkang Geng, Tao Shen, Yuan Xu & Shuhui Bi

  2. HIT Robot Group Co., Ltd, Xingkai Road, 150001, Pingfang Harbin, China

    Tao Shen

Authors
  1. Bin Sun
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  2. Renkang Geng
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  3. Tao Shen
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  4. Yuan Xu
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  5. Shuhui Bi
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Contributions

Bin Sun made a substantial contribution to the concept and design of the article. Renkang Geng collected the data and performed the experiments. Tao Shen conducted the analysis. Yuan Xu and Shuhui Bi reviewed and corrected the design, writing and experiments.

Corresponding authors

Correspondence to Yuan Xu or Shuhui Bi.

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The authors declare that there is no conflict of interest.

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Cite this article

Sun, B., Geng, R., Shen, T. et al. Dynamic Emergency Transit Forecasting with IoT Sequential Data. Mobile Netw Appl (2022). https://doi.org/10.1007/s11036-022-02027-0

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