%A Jiangtao WANG,Yasha WANG,Daqing ZHANG,Leye WANG,Chao CHEN,JaeWoong LEE,Yuanduo HE %T Real-time and generic queue time estimation based on mobile crowdsensing %0 Journal Article %D 2017 %J Front. Comput. Sci. %J Frontiers of Computer Science %@ 2095-2228 %R 10.1007/s11704-016-5553-z %P 49-60 %V 11 %N 1 %U {https://journal.hep.com.cn/fcs/EN/10.1007/s11704-016-5553-z %8 2017-01-11 %X

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.