Frontiers of Computer Science

, Volume 11, Issue 1, pp 49–60 | Cite as

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

  • Jiangtao Wang
  • Yasha WangEmail author
  • Daqing Zhang
  • Leye Wang
  • Chao Chen
  • Jae Woong Lee
  • Yuanduo He
Research Article


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 human 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 behavior 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 performance of the system with a two-week and 12-person deployment using commercially-available smartphones. The results demonstrate that CrowdQTE is effective in estimating queuing status.


mobile crowdsensing queue time estimation opportunistic and participatory sensing 


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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 Collaboration Research Grant. Besides, the work was partially supported by the Fundamental Research Funds for the Central Universities (106112015CDJXY180001), 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).

Supplementary material

11704_2016_5553_MOESM1_ESM.ppt (370 kb)
Supplementary material, approximately 371 KB.


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Copyright information

© Higher Education Press and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Jiangtao Wang
    • 1
    • 2
    • 3
  • Yasha Wang
    • 1
    • 3
    • 4
    Email author
  • Daqing Zhang
    • 1
    • 2
    • 3
  • Leye Wang
    • 5
  • Chao Chen
    • 6
  • Jae Woong Lee
    • 7
  • Yuanduo He
    • 1
    • 2
    • 3
  1. 1.Key Laboratory of High Confidence Software TechnologiesMinistry of EducationBeijingChina
  2. 2.School of Electronics Engineering and Computer SciencePeking UniversityBeijingChina
  3. 3.Beida (Binhai) Information ResearchTianjinChina
  4. 4.National Engineering Research Center of Software EngineeringPeking UniversityBeijingChina
  5. 5.Network & Services DepartmentInstitut Mines-Télécom/Télécom SudParisEvryFrance
  6. 6.Department of Computer ScienceChongqing UniversityChongqingChina
  7. 7.Department of Mathematics and Computer ScienceUniversity of Central MissouriWarrensburgUSA

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