Real-time and generic queue time estimation based on mobile crowdsensing
- 103 Downloads
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.
Keywordsmobile crowdsensing queue time estimation opportunistic and participatory sensing
Unable to display preview. Download preview PDF.
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).
- 1.Kong D, Gray D, Tao H. Counting pedestrians in crowds using viewpoint invariant training. In: Proceedings of British Machine Vision Conference. 2005Google Scholar
- 3.Reisman P, Mano O, Avidan S, Shashua A. Crowd detection in video sequences. In: Proceedings of IEEE Intelligent Vehicles Symposium. 2004, 66–71Google Scholar
- 4.Huang X Y, Li L Y, Sim T. Stereo-based human head detection from crowd scenes. In: Proceedings of International Conference on Image Processing. 2004, 1353–1356Google Scholar
- 8.Wang Y, Yang J, Chen Y Y, Liu H B, Gruteser M, Martin R P. Tracking human queues using single-point signal monitoring. In: Proceedings of the 12th ACM Annual International Conference on Mobile Systems, Applications, and Services. 2014, 42–54Google Scholar
- 11.Koukoumidis E, Peh L S, Martonosi M R. SignalGuru: leveraging mobile phones for collaborative traffic signal schedule advisory. In: Proceedings of the 9th ACMInternational Conference onMobile Systems, Applications, and Services. 2011, 127–140Google Scholar
- 14.Rana R K, Chou C T, Kanhere S S, Bulusu N, Hu W. Ear-phone: an end-to-end participatory urban noise mapping system. In: Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks. 2010, 105–116Google Scholar
- 16.Zhou P F, Zheng Y Q, Li M. How long to wait? predicting bus arrival time with mobile phone based participatory sensing. In: Proceedings of the 10th ACM International Conference on Mobile Systems, Applications, and Services. 2012, 379–392Google Scholar
- 19.Burke J, Estrin D, Hansen M, Parker A, Ramanathan N, Reddy S, Srivastava M B. Participatory sensing. Center for Embedded Network Sensing, 2006Google Scholar
- 23.Faulkner M, Olson N, Chandy R, Krause J, Chandy K M, Krause A. The next big one: detecting earthquakes and other rare events from community-based sensors. In: Proceedings of the 10th IEEE International Conference on Information Processing in Sensor Networks. 2011, 13–24Google Scholar
- 24.Bao X, Choudhury R R. MoVi: mobile phone based video highlights via collaborative sensing. In: Proceedings of the 8th ACM Iinternational Conference on Mobile Systems, Applications, and Services. 2010, 357–370Google Scholar
- 26.Li Q, Han Q, Cheng X Z, Sun L M, QueueSense: collaborative recognition of queuing behavior on mobile phones. IEEE Transactions on Mobile Computing, 2016, 15(1):60–73Google Scholar
- 27.Hossan M A, Memon S, Gregory M A. A novel approach for MFCC features extraction. In: Proceedings of the 4th IEEE International Conference on Signal Processing and Communication Systems. 2010Google Scholar
- 28.Chu S, Narayanan S, Kuo C J. Environmental sound recognition using MP-based features. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing. 2008, 1–4Google Scholar
- 29.Li Q H, Cao G H. Providing privacy-aware incentives in mobile sensing systems. IEEE Transactions on Mobile Computing, 2016, 9770(5): 76–84Google Scholar