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RUTOD: real-time urban traffic outlier detection on streaming trajectory

  • S.I. : Deep Geospatial Data Understanding
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Abstract

With the rapid development of internet technology and mobile devices, massive streaming data of spatiotemporal information is available for real-time data mining. Outlier detection is playing as one of the most important analysis tasks for trajectory stream processing, such as traffic control and urban planning. Existing work about this issue can be divided into two main categories: individual outlier detection (IOD) and group outlier detection (GOD). IOD focuses on detecting an individual trajectory outlier generated by a single moving object (e.g., an over-speeding car), while GOD aims to detect a group outlier generated by various moving objects (e.g., a group of cars influenced by a traffic jam). However, existing studies only support one of them and cannot comprehensively understand the traffic conditions by combining both of them. To this end, we propose a novel framework for real-time urban traffic outlier detection (RUTOD) in this paper. RUTOD contains IOD based on current traffic conditions and GOD according to historical data. In addition, we adopt a street-based trajectory division to accelerate investigation. Experimental results show that our proposal is not only effective but also efficient for detecting both individual outliers and group outliers in real-time scenario.

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Notes

  1. https://github.com/apache/flink.

  2. https://outreach.didichuxing.com/research/opendata/.

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Acknowledgements

This work was supported by National Natural Science Foundation of China under grant (No. 61802273), Postdoctoral Science Foundation of China (No. 2020M681529).

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Authors and Affiliations

  1. Department of Computer Science and Technology, Soochow University, Suzhou, China

    Juntian Shi, Zhicheng Pan & Junhua Fang

  2. School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD, Australia

    **fu Chao

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  1. Juntian Shi
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  2. Zhicheng Pan
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  3. Junhua Fang
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  4. **fu Chao
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Correspondence to Juntian Shi.

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Shi, J., Pan, Z., Fang, J. et al. RUTOD: real-time urban traffic outlier detection on streaming trajectory. Neural Comput & Applic 35, 3625–3637 (2023). https://doi.org/10.1007/s00521-021-06294-y

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