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

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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.

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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

Authors and Affiliations

  1. Key Laboratory of High Confidence Software Technologies, Ministry of Education, Beijing, 100871, China

    Jiangtao Wang, Yasha Wang, Daqing Zhang & Yuanduo He

  2. School of Electronics Engineering and Computer Science, Peking University, Beijing, 100871, China

    Jiangtao Wang, Daqing Zhang & Yuanduo He

  3. Beida (Binhai) Information Research, Tianjin, 300450, China

    Jiangtao Wang, Yasha Wang, Daqing Zhang & Yuanduo He

  4. National Engineering Research Center of Software Engineering, Peking University, Beijing, 100871, China

    Yasha Wang

  5. Network & Services Department, Institut Mines-Télécom/Télécom SudParis, Evry, 91011, France

    Leye Wang

  6. Department of Computer Science, Chongqing University, Chongqing, 400044, China

    Chao Chen

  7. Department of Mathematics and Computer Science, University of Central Missouri, Warrensburg, MO, 64093, USA

    Jae Woong Lee

Authors
  1. Jiangtao Wang
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  2. Yasha Wang
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  3. Daqing Zhang
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  4. Leye Wang
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  5. Chao Chen
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  6. Jae Woong Lee
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  7. Yuanduo He
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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.

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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

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  • DOI: https://doi.org/10.1007/s11704-016-5553-z

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