A statistical method for estimating predictable differences between daily traffic flow profiles
- Submitting institution
-
The University of Leeds
- Unit of assessment
- 12 - Engineering
- Output identifier
- ITS-29
- Type
- D - Journal article
- DOI
-
10.1016/j.trb.2016.11.004
- Title of journal
- Transportation Research Part B: Methodological
- Article number
- -
- First page
- 196
- Volume
- 95
- Issue
- -
- ISSN
- 0191-2615
- Open access status
- Access exception
- Month of publication
- November
- Year of publication
- 2016
- URL
-
https://doi.org/10.1016/j.trb.2016.11.004
- 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)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Based on a collaboration with Highways England, with Bluetooth data provided by Transport for Greater Manchester, this paper develops methods new to transportation, for the first time applying functional data analysis to traffic flow profiles. This specific approach has since been adopted by others (e.g. DOI 10.1109/TITS.2017.2706143) illustrating the applicability of the methodology developed in this paper. This work was central to Crawford’s European Friedrich-List Award 2019 for best dissertation (email on request), and led to her ESRC Post-Doctoral Fellowship (ES/S010165/1).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
