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Route selection for opportunity-sensing and prediction of waterlogging

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Abstract

Accurate monitoring of urban waterlogging contributes to the city’s normal operation and the safety of residents’ daily travel. However, due to feedback delays or high costs, existing methods make large-scale, fine-grained waterlogging monitoring impossible. A common method is to forecast the city’s global waterlogging status using its partial waterlogging data. This method has two challenges: first, existing predictive algorithms are either driven by knowledge or data alone; and second, the partial waterlogging data is not collected selectively, resulting in poor predictions. To overcome the aforementioned challenges, this paper proposes a framework for large-scale and fine-grained spatiotemporal waterlogging monitoring based on the opportunistic sensing of limited bus routes. This framework follows the Sparse Crowdsensing and mainly comprises a pair of iterative predictor and selector. The predictor uses the collected waterlogging status and the predicted status of the uncollected area to train the graph convolutional neural network. It combines both knowledge-driven and data-driven approaches and can be used to forecast waterlogging status in all regions for the upcoming term. The selector consists of a two-stage selection procedure that can select valuable bus routes while satisfying budget constraints. The experimental results on real waterlogging and bus routes in Shenzhen show that the proposed framework could easily perform urban waterlogging monitoring with low cost, high accuracy, wide coverage, and fine granularity.

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Acknowledgements

This work was supported by the Natural Science Foundation of Fujian Province (Nos. 2020H0008, 2021J01619), and the National Natural Science Foundation of China (Grant No. 61772136).

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Authors and Affiliations

  1. College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China

    Jingbin Wang, Weijie Zhang, Zhiyong Yu, Fangwan Huang & Weiping Zhu

  2. Department of Computer Science and Technology, Xiamen University, Xiamen, 361005, China

    Longbiao Chen

Authors
  1. Jingbin Wang
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  2. Weijie Zhang
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  3. Zhiyong Yu
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  4. Fangwan Huang
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Corresponding author

Correspondence to Fangwan Huang.

Additional information

Jingbin Wang received the ME degree in computer application from Fuzhou University, China in 2001. She is a member of the China Computer Federation (CCF). Her research interests include crowdsensing, knowledge graphs, and knowledge representation.

Weijie Zhang received the BS degree from Minnan Normal University, China in 2019, and the ME degree from Fuzhou University, China in 2022. His research interests include ubiquitous computing and crowdsensing.

Zhiyong Yu is a full professor at the College of Computer and Data Science, Fuzhou University, China. He received ME and PhD degrees in computer science and technology from Northwestern Polytechnical University, China in 2007 and 2011, respectively. He was also a visiting student at Kyoto University, Japan from 2007 to 2009, and a visiting researcher at the Institute Mines-Telecom, TELECOM SudParis, Evry, France from 2012 to 2013. His current research interests include pervasive computing, mobile social networks, and mobile crowdsensing.

Fangwan Huang is a senior lecturer at the College of Computer and Data Science, Fuzhou University, China. She received the ME degree in computer system structure and a PhD degree in intelligent information technology from Fuzhou University, China in 2005 and 2022, respectively. She is a member of the China Computer Federation (CCF) and Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing. Her research interests include computational intelligence, big data analysis, and mobile crowdsensing.

Weiping Zhu received a BS degree in information and computing science from Fujian Normal University, China in 2014 and the PhD degree in intelligent information technology from Fuzhou University, China in 2022. His research interests include pervasive computing and mobile crowdsensing.

Longbiao Chen is an associate professor at Xiamen University, China. He received the PhD degree in computer science from Sorbonne University, France in 2018 and Zhejiang University, China in 2016. His research interests include mobile computing, big data analytics, crowdsensing, and urban computing.

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Wang, J., Zhang, W., Yu, Z. et al. Route selection for opportunity-sensing and prediction of waterlogging. Front. Comput. Sci. 18, 184503 (2024). https://doi.org/10.1007/s11704-023-2714-8

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  • DOI: https://doi.org/10.1007/s11704-023-2714-8

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