LineKing : coffee shop wait-time monitoring using smartphones
- Submitting institution
-
The University of Warwick
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 5973
- Type
- D - Journal article
- DOI
-
10.1109/TMC.2014.2384032
- Title of journal
- IEEE Transactions on Mobile Computing
- Article number
- -
- First page
- 2045
- Volume
- 14
- Issue
- 10
- ISSN
- 1536-1233
- Open access status
- Out of scope for open access requirements
- Month of publication
- October
- Year of publication
- 2015
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
-
D - Data Science, Systems and Security
- Citation count
- 14
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Published in the flagship journal of the field, this paper introduced the first end-to-end solution for dynamic prediction of queue times using mobile data. It was accompanied by a scalable platform and mobile service that led to thousands of active users providing further data after publication while using the solution in real life. This research has impacted new similar tools, e.g. QueueSense by Q Li et al., IEEE Trans. Mobile Computing 2016 follows up the preliminary conference version; and technology products, e.g. this paper is the only publication reference in Amazon’s patent on sensing-based arrival and wait-time estimation (US10762462B1, 2020).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -