1. Key Laboratory of High Confidence Software Technologies,Ministry of Education, Beijing 100871, China 2. School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China 3. Beida (Binhai) Information Research, Tianjin 300450, China 4. National Engineering Research Center of Software Engineering, Peking University, Beijing 100871, China 5. Network & Services Department, Institut Mines-Télécom/Télécom SudParis, Evry 91011, France 6. Department of Computer Science, Chongqing University, Chongqing 400044, China 7. Department of Mathematics and Computer Science, University of Central Missouri, Warrensburg MO 64093, USA
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.
Kong D, Gray D, Tao H. Counting pedestrians in crowds using viewpoint invariant training. In: Proceedings of British Machine Vision Conference. 2005
https://doi.org/10.5244/c.19.63
2
Lin S F, Chen J Y, Chao H X. Estimation of number of people in crowded scenes using perspective transformation. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 2001, 31(6): 645–654
https://doi.org/10.1109/3468.983420
3
Reisman P, Mano O, Avidan S, Shashua A. Crowd detection in video sequences. In: Proceedings of IEEE Intelligent Vehicles Symposium. 2004, 66–71
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, 1353–1356
5
Mckenna S J, Jabri S, Duric Z, Rosenfeld A, Wechsler H. Tracking groups of people. Computer Vision and Image Understanding, 2000, 80(1): 42–56
https://doi.org/10.1006/cviu.2000.0870
6
Bauer D, Ray M, Seer S. Simple sensors used for measuring service times and counting pedestrians. Transportation Research Record: Journal of the Transportation Research Board, 2011, (2214): 77–84
https://doi.org/10.3141/2214-10
7
Bullock D, Haseman R, Wasson J, Spitler R. Automated measurement of wait times at airport security. Transportation Research Record: Journal of the Transportation Research Board, 2010, (2177): 60–68
https://doi.org/10.3141/2177-08
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, 42–54
https://doi.org/10.1145/2594368.2594382
9
Ganti R K, Ye F, Lei H. Mobile crowdsensing: current state and future challenges. IEEE Communications Magazine, 2011, 49(11): 32–39
https://doi.org/10.1109/MCOM.2011.6069707
10
Guo B, Wang Z, Yu Z W, Wang Y, Yen N Y, Huang R H, Zhou X S. Mobile crowd sensing and computing: the review of an emerging human-powered sensing paradigm. ACM Computing Surveys, 2015, 48(1): 7
https://doi.org/10.1145/2794400
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, 127–140
https://doi.org/10.1145/1999995.2000008
12
Xu C R, Li S G, Liu G, Zhang Y Y, Miluzzo E, Chen Y F, Li J, Firner B. Crowd++: unsupervised speaker count with smartphones. In: Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing. 2013, 43–52
https://doi.org/10.1145/2493432.2493435
13
Guo B, Chen H H, Yu Z W. FlierMeet: a mobile crowdsensing system for cross-space public information reposting, tagging, and sharing. IEEE Transactions on Mobile Computing, 2015, 14(10): 2020–2033
https://doi.org/10.1109/TMC.2014.2385097
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, 105–116
https://doi.org/10.1145/1791212.1791226
15
Zheng Y, Liu F, Hsieh H P. U-Air: when urban air quality inference meets big data. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2013, 1436–1444
https://doi.org/10.1145/2487575.2488188
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, 379–392
17
Lane N D, Miluzzo E, Lu H, Peebles D, Choudhury T, Campbell A T. A survey of mobile phone sensing. IEEE Communications magazine, 2010, 48(9): 140–150
https://doi.org/10.1109/MCOM.2010.5560598
18
Campbell A T, Eisenman S B, Lane N D, Miluzzo E, Peterson R A, Lu H, Zheng X, Musolesi M, Fodor K, Ahn G S. The rise of people-centric sensing. IEEE Internet Computing, 2008, 12(4): 12–21
https://doi.org/10.1109/MIC.2008.90
19
Burke J, Estrin D, Hansen M, Parker A, Ramanathan N, Reddy S, Srivastava M B. Participatory sensing. Center for Embedded Network Sensing, 2006
Chon Y H, Lane N D, Li F, Cha H J, Zhao F. Automatically characterizing places with opportunistic crowdsensing using smartphones. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing. 2012, 481–490
https://doi.org/10.1145/2370216.2370288
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, 13–24
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, 357–370
https://doi.org/10.1145/1814433.1814468
25
Bulut M F, Yilmaz Y S, Demirbas M, Ferhatosmanoglu N, Ferhatosmanoglu H. Lineking: crowdsourced line wait-time estimation using smartphones. In: Proceedings of International Conference on Mobile Computing, Applications, and Services. 2013, 205–224
https://doi.org/10.1007/978-3-642-36632-1_12
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):60–73
https://doi.org/10.1109/TMC.2015.2407400
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. 2010
https://doi.org/10.1109/icspcs.2010.5709752
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, 1–4
https://doi.org/10.1109/icassp.2008.4517531
29
Li Q H, Cao G H. Providing privacy-aware incentives in mobile sensing systems. IEEE Transactions on Mobile Computing, 2016, 9770(5): 76–84
https://doi.org/10.1109/tmc.2015.2465375
