Real-time and generic queue time estimation based on mobile crowdsensing

作     者：Jiangtao WANG Yasha WANG Daqing ZHANG Leye WANG Chao CHEN Jae Woong LEE Yuanduo HE 

作者机构：Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing 100871 China School of Electronics Engineering and Computer Science Peking University Beijing 100871 China Beida (Binhai) Information Research Tianjin 300450 China National Engineering Research Center of Software Engineering Peking University Beijing 100871 China Network & Services Department Institut Mines-Telecom/Telecom SudParis Evry 91011 France Department of Computer ScienceChongqing University Chongqing 400044 China Department of Mathematics and Computer Science University of Central Missouri Warrensburg MO 64093 USA 

出 版 物：《Frontiers of Computer Science》 (中国计算机科学前沿（英文版）)

年 卷 期：2017年第11卷第1期

页      面：49-60页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 080202[工学-机械电子工程] 08[工学] 0802[工学-机械工程] 

基　　金：This work was mainly funded by the National Natural Science Foundation of China (Grant No. 61572048)  Research Fund from China Electric Power Research Institute (JS71-16-005)  and Microsoft Col- laboration Research Grant. Besides  the work was partially supported by the Fundamental Research Funds for the Central Universities (106112015CD-JXY180001)  Open Research Fund Program of Shenzhen Key Laboratory of Spatial Smart Sensing and Services (Shenzhen University  China)  and Chongqing Basic and Frontier Research Program (cstc2015jcyjA00016) 

主　　题：mobile crowdsensing queue time estimation opportunistic and participatory sensing 

摘      要：People often have to queue for a busy service in many places around a city, and knowing the queue time can be helpful for making better activity plans to avoid long queues. Traditional solutions to the queue time monitoring are based on pre-deployed infrastructures, such as cameras and infrared sensors, which are costly and fail to deliver the queue time information to scattered citizens. This paper presents CrowdQTE, a mobile crowdsensing system, which utilizes the sensor-enhanced mobile devices and crowd hu- man intelligence to monitor and provide real-time queue time information for various queuing scenarios. When people are waiting in a line, we utilize the accelerometer sensor data and ambient contexts to automatically detect the queueing behav- ior and calculate the queue time. When people are not waiting in a line, it estimates the queue time based on the information reported manually by participants. We evaluate the perfor- mance of the system with a two-week and 12-person deploy- ment using commercially-available smartphones. The results demonstrate that CrowdQTE is effective in estimating queu- ing status.