CrowdPatrol: A Mobile Crowdsensing Framework for Traffic Violation Hotspot Patrolling

被引:35
作者
Jiang, Zhihan [1 ]
Zhu, Hang [1 ]
Zhou, Binbin [2 ]
Lu, Chenhui [1 ]
Sun, Mingfei [3 ]
Ma, Xiaojuan [4 ]
Fan, Xiaoliang [1 ]
Wang, Cheng [1 ]
Chen, Longbiao [1 ]
机构
[1] Xiamen Univ, Sch Informat, Fujian Key Lab Sensing & Comp Smart Cities SCSC, Xiamen 361005, Fujian, Peoples R China
[2] Zhejiang Univ City Coll, Dept Comp Sci & Comp, Hangzhou 310015, Zhejiang, Peoples R China
[3] Univ Oxford, Oxford OX1, England
[4] Hong Kong Univ Sci & Technol, Hong Kong, Peoples R China
关键词
Roads; Crowdsensing; Urban areas; Task analysis; Schedules; Law enforcement; Context modeling; Traffic violation; urban computing; patrol task scheduling; mobile crowdsensing; DRIVERS; PATHS;
D O I
10.1109/TMC.2021.3110592
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Traffic violations have become one of the major threats to urban transportation systems, undermining human safety and causing economic losses. To alleviate this problem, crowd-based patrol forces including traffic police and voluntary participants have been employed in many cities. To adaptively optimize patrol routes with limited manpower, it is essential to be aware of traffic violation hotspots. Traditionally, traffic violation hotspots are directly inferred from experience, and existing patrol routes are usually fixed. In this paper, we propose a mobile crowdsensing-based framework to dynamically infer traffic violation hotspots and adaptively schedule crowd patrol routes. Specifically, we first extract traffic violation-prone locations from heterogeneous crowd-sensed data and propose a spatiotemporal context-aware self-adaptive learning model (CSTA) to infer traffic violation hotspots. Then, we propose a tensor-based integer linear problem modeling method (TILP) to adaptively find optimal patrol routes under human labor constraints. Experiments on real-world data from two Chinese cities (Xiamen and Chengdu) show that our approach accurately infers traffic violation hotspots with F1-scores above 90% in both cities, and generates patrol routes with relative coverage ratios above 85%, significantly outperforming baseline methods.
引用
收藏
页码:1401 / 1416
页数:16
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