Route selection for opportunity-sensing and prediction of waterlogging  

作　　者：Jingbin WANG Weijie ZHANG Zhiyong YU Fangwan HUANG Weiping ZHU Longbiao CHEN 

机构地区：[1]College of Computer and Data Science,Fuzhou University,Fuzhou 350108,China [2]Department of Computer Science and Technology,Xiamen University,Xiamen 361005,China

出　　处：《Frontiers of Computer Science》2024年第4期147-159,共13页中国计算机科学前沿（英文版）

基　　金：Natural Science Foundation of Fujian Province(Nos.2020H0008,2021J01619);National Natural Science Foundation of China(Grant No.61772136).

摘　　要：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.

关 键 词：waterlogging prediction sparse crowdsensing active learning route selection graph convolutional network 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]