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

Jiangtao Wang, Yasha Wang, Daqing Zhang, Leye Wang, Chao Chen, Jae Woong Lee, Yuanduo He

Research output: Contribution to journalArticlepeer-review

33 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)49–60
Number of pages12
JournalFrontiers of Computer Science
Publication statusPublished - 7 Apr 2017
Externally publishedYes


  • mobile crowdsensing
  • queue time estimation
  • opportunistic and participatory sensing


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