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 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.

This is a preview of subscription content, log in to check access.

References

  1. 1.

    Kong D, Gray D, Tao H. Counting pedestrians in crowds using viewpoint invariant training. In: Proceedings of British Machine Vision Conference. 2005

    Google Scholar 

  2. 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

    Article  Google Scholar 

  3. 3.

    Reisman P, Mano O, Avidan S, Shashua A. Crowd detection in video sequences. In: Proceedings of IEEE Intelligent Vehicles Symposium. 2004, 66–71

    Google Scholar 

  4. 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

    Google Scholar 

  5. 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

    Article  MATH  Google Scholar 

  6. 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

    Article  Google Scholar 

  7. 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

    Article  Google Scholar 

  8. 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

    Google Scholar 

  9. 9.

    Ganti R K, Ye F, Lei H. Mobile crowdsensing: current state and future challenges. IEEE Communications Magazine, 2011, 49(11): 32–39

    Article  Google Scholar 

  10. 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

    Article  Google Scholar 

  11. 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

    Google Scholar 

  12. 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

    Google Scholar 

  13. 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

    Article  Google Scholar 

  14. 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

    Google Scholar 

  15. 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

    Google Scholar 

  16. 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

    Google Scholar 

  17. 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

    Article  Google Scholar 

  18. 18.

    Campbell A T, Eisenman S B, Lane N D, Miluzzo E, Peterson R A, Lu H, Zheng X,MusolesiM, Fodor K, Ahn G S. The rise of people-centric sensing. IEEE Internet Computing, 2008, 12(4): 12–21

    Article  Google Scholar 

  19. 19.

    Burke J, Estrin D, Hansen M, Parker A, Ramanathan N, Reddy S, Srivastava M B. Participatory sensing. Center for Embedded Network Sensing, 2006

    Google Scholar 

  20. 20.

    Ma H D, Zhao D, Yuan P Y. Opportunities in mobile crowd sensing. IEEE Communications Magazine, 2014, 52(8), 29–35

    Article  Google Scholar 

  21. 21.

    Zhang D Q, Wang L Y, Xiong H Y, Guo, B. 4W1H in mobile crowd sensing. IEEE Communications Magazine, 2014, 52(8): 42–48

    Article  Google Scholar 

  22. 22.

    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

    Google Scholar 

  23. 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

    Google Scholar 

  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

    Google Scholar 

  25. 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

    Google Scholar 

  26. 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

  27. 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

    Google Scholar 

  28. 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

    Google Scholar 

  29. 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

    Google Scholar 

Download references

Acknowledgments

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).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Yasha Wang.

Additional information

Jiangtao Wang received his PhD degree in Peking University (PKU), China in 2015. He is currently a postdoc researcher in Institute of Software, School of Electronics Engineering and Computer Science, PKU. His research interest includes ubiquitous computing, urban data analytics and software engineering.

Yasha Wang received his PhD degree in Northeastern University, China in 2003. He is a professor and associate director of National Research & Engineering Center of Software Engineering in Peking University, China. He has published more than 50 papers in prestigious conferences and journals, such as ICWS, UbiComp, ICSP, etc. As a technical leader and manager, he has accomplished several key national projects on software engineering and smart cities. Cooperating with major smart-city solution providing companies, he carried out a lot of research work which has been widely adopted in more than 20 cities in China. His research interest includes urban data analytics, ubiquitous computing, software reuse, and online software development environment.

Daqing Zhang is a professor at Peking University, China and Télécom SudParis, France. He obtained his PhD from the University of Rome “La Sapienza,” Italy in 1996. He served as the General or Program Chair for more than ten international conferences. He is an associate editor for ACM Transactions on Intelligent Systems and Technology, IEEE Transactions on Big Data, and others. His research interests include context-aware computing, urban computing, mobile computing, and so on.

Leye Wang obtained his PhD from Institut Mines-Télécom/Télécom SudParis and Université Pierre et Marie Curie, France in 2016. He received his MS and BS in computer science from Peking University, China. His research interests include mobile crowdsensing, social networks, and intelligent transportation systems.

Chao Chen is an associate professor at College of Computer Science, Chongqing University, China. He obtained his PhD degree from Pierre and Marie Curie University, France in 2014. His research interests include pervasive computing, urban logistics, data mining from large-scale taxi data, and big data analytics for smart cities.

Jae Woong Lee is an assistant professor in the School of Computer Science and Mathematics, University of Central Missouri, USA. He received the PhD degree from the Department of Computer and Information Science and Engineering, University of Florida, USA. His research focuses on modeling and simulation of human activities and sensor-based smart spaces, which especially advances assistive and intelligent systems for health cares. His research interests include human-centric environments, mobile health, data analytics and data science. He is currently researching future technologies equipped for smart cities and health informatics.

Yuanduo He received his bachelor degree in Peking University (PKU), China in 2014. He is currently an PhD student in the Institute of Software, School of Electronics Engineering and Computer Science, PKU. His research interests include ubiquitous computing, mobile computing, and data mining.

Electronic supplementary material

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wang, J., Wang, Y., Zhang, D. et al. Real-time and generic queue time estimation based on mobile crowdsensing. Front. Comput. Sci. 11, 49–60 (2017). https://doi.org/10.1007/s11704-016-5553-z

Download citation

Keywords

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