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

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Real-time and generic queue time estimation based on mobile crowdsensing. / Wang, Jiangtao; Wang, Yasha; Zhang, Daqing et al.
In: FRONTIERS OF COMPUTER SCIENCE, Vol. 11, No. 1, 02.2017, p. 49-60.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Wang, J, Wang, Y, Zhang, D, Wang, L, Chen, C, Lee, JW & He, Y 2017, 'Real-time and generic queue time estimation based on mobile crowdsensing', FRONTIERS OF COMPUTER SCIENCE, vol. 11, no. 1, pp. 49-60. https://doi.org/10.1007/s11704-016-5553-z

APA

Wang, J., Wang, Y., Zhang, D., Wang, L., Chen, C., Lee, J. W., & He, Y. (2017). Real-time and generic queue time estimation based on mobile crowdsensing. FRONTIERS OF COMPUTER SCIENCE, 11(1), 49-60. https://doi.org/10.1007/s11704-016-5553-z

Vancouver

Wang J, Wang Y, Zhang D, Wang L, Chen C, Lee JW et al. Real-time and generic queue time estimation based on mobile crowdsensing. FRONTIERS OF COMPUTER SCIENCE. 2017 Feb;11(1):49-60. doi: 10.1007/s11704-016-5553-z

Author

Wang, Jiangtao ; Wang, Yasha ; Zhang, Daqing et al. / Real-time and generic queue time estimation based on mobile crowdsensing. In: FRONTIERS OF COMPUTER SCIENCE. 2017 ; Vol. 11, No. 1. pp. 49-60.

Bibtex

@article{2f2553bbcf5247c491456704a67ad6fb,
title = "Real-time and generic queue time estimation based on mobile crowdsensing",
abstract = "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 andambient 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.",
keywords = "mobile crowdsensing, queue time estimation, opportunistic and participatory sensing",
author = "Jiangtao Wang and Yasha Wang and Daqing Zhang and Leye Wang and Chao Chen and Lee, {Jae Woong} and Yuanduo He",
year = "2017",
month = feb,
doi = "10.1007/s11704-016-5553-z",
language = "English",
volume = "11",
pages = "49--60",
journal = "FRONTIERS OF COMPUTER SCIENCE",
issn = "2095-2228",
publisher = "Springer Science + Business Media",
number = "1",

}

RIS

TY - JOUR

T1 - Real-time and generic queue time estimation based on mobile crowdsensing

AU - Wang, Jiangtao

AU - Wang, Yasha

AU - Zhang, Daqing

AU - Wang, Leye

AU - Chen, Chao

AU - Lee, Jae Woong

AU - He, Yuanduo

PY - 2017/2

Y1 - 2017/2

N2 - 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 andambient 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.

AB - 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 andambient 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.

KW - mobile crowdsensing

KW - queue time estimation

KW - opportunistic and participatory sensing

U2 - 10.1007/s11704-016-5553-z

DO - 10.1007/s11704-016-5553-z

M3 - Journal article

VL - 11

SP - 49

EP - 60

JO - FRONTIERS OF COMPUTER SCIENCE

JF - FRONTIERS OF COMPUTER SCIENCE

SN - 2095-2228

IS - 1

ER -