A User-Oriented Taxi Ridesharing System with Large-Scale Urban GPS Sensor Data
- Submitting institution
-
The University of Huddersfield
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 9
- Type
- D - Journal article
- DOI
-
10.1109/TBDATA.2018.2872450
- Title of journal
- IEEE Transactions on Big Data
- Article number
- -
- First page
- n/a
- Volume
- n/a
- Issue
- -
- ISSN
- 2332-7790
- Open access status
- Not compliant
- Month of publication
- September
- Year of publication
- 2018
- 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
- Yes
- Number of additional authors
-
5
- Research group(s)
-
-
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Published in an IEEE Transactions journal, this output was a result of Qin's collaboration with academics at Australia's Macquarie University and China's Dalian University of Technology. It introduces the TRIPS framework which is designed for dynamic taxi ridesharing. The novelty lies in the use of the probability of user decisions, and a reformulation of the criteria for searching and ranking ridesharing alternatives to optimize the process. The output’s results were enabled by involvement from Microsoft Research (contact Dr. Yu Zheng, senior research manager) who provided the data for the city of Bejing.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
