Route selection for opportunity-sensing and prediction of waterlogging

IF 3.4 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Frontiers of Computer Science Pub Date : 2023-12-18 DOI:10.1007/s11704-023-2714-8

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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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机会感应和内涝预测的路线选择

摘要城市内涝的准确监测有助于城市的正常运行和居民的日常出行安全。然而，由于反馈延迟或成本高昂，现有方法无法实现大规模、精细化的内涝监测。一种常见的方法是利用部分内涝数据预测城市的整体内涝状况。这种方法存在两个挑战：首先，现有的预测算法要么仅由知识驱动，要么仅由数据驱动；其次，没有选择性地收集部分内涝数据，导致预测结果不佳。为了克服上述挑战，本文提出了一种基于有限公交线路机会感知的大规模精细时空内涝监测框架。该框架遵循稀疏人群感知原理，主要由一对迭代预测器和选择器组成。预测器使用收集到的内涝状况和未收集区域的预测状况来训练图卷积神经网络。它结合了知识驱动和数据驱动两种方法，可用于预测所有地区下一年度的内涝状况。选择器由两阶段选择程序组成，可在满足预算限制的前提下选择有价值的公交线路。在深圳实际内涝和公交线路上的实验结果表明，所提出的框架可以轻松实现低成本、高精度、广覆盖和细粒度的城市内涝监测。

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来源期刊

Frontiers of Computer Science COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, SOFTWARE ENGINEERING

CiteScore

8.60

自引率

2.40%

发文量

799

审稿时长

6-12 weeks

期刊介绍： Frontiers of Computer Science aims to provide a forum for the publication of peer-reviewed papers to promote rapid communication and exchange between computer scientists. The journal publishes research papers and review articles in a wide range of topics, including: architecture, software, artificial intelligence, theoretical computer science, networks and communication, information systems, multimedia and graphics, information security, interdisciplinary, etc. The journal especially encourages papers from new emerging and multidisciplinary areas, as well as papers reflecting the international trends of research and development and on special topics reporting progress made by Chinese computer scientists.