Tracking Time Evolving Data Streams for Short-Term Traffic Forecasting
- Submitting institution
-
The University of Bradford
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 32
- Type
- D - Journal article
- DOI
-
10.1007/s41019-017-0048-y
- Title of journal
- Data Science and Engineering
- Article number
- -
- First page
- 210
- Volume
- 2
- Issue
- 3
- ISSN
- 2364-1185
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2017
- URL
-
https://link.springer.com/article/10.1007%2Fs41019-017-0048-y
- 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)
-
-
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper, published in a leading journal on Data Science and Engineering, presents a Robust Layered Ensemble Model that combines Artificial Neural Networks and Graded Possibilistic Clustering models, obtaining in such a way an accurate forecaster of the traffic flow rates with outlier detection. The RLEM was a part of PLUG-IN platform with collaboration with University of Genoa, CNR Genoa, Selex Communications, Bombardier Transportation, and Ansaldo STS. Currently, it is deployed at General Electric as as part of their inventory management system.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
