Multi-contextual machine-learning approach to modeling traffic impact of urban highway work zones
- Submitting institution
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University of the West of Scotland
- Unit of assessment
- 12 - Engineering
- Output identifier
- 12756244
- Type
- D - Journal article
- DOI
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10.3141/2645-20
- Title of journal
- Transportation Research Record: Journal of the Transportation Research Board
- Article number
- -
- First page
- 184
- Volume
- 2645
- Issue
- -
- ISSN
- 0361-1981
- Open access status
- Deposit exception
- Month of publication
- January
- Year of publication
- 2017
- URL
-
-
- Supplementary information
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- 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
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2
- Research group(s)
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-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This study reliably and accurately predicts traffic impacts of urban highway work zones by harnessing a novel multi-contextual machine learning for 17,518 traffic sensor and multi-contextual datasets. This paper was selected for publication by TRB’s AI Committee for over 4,600 papers submitted to the TRB’s 96th Annual Meeting. The work let to an invitation to deliver a keynote address (by Dr Bae) for the China’s State Administration for Foreign Expert Affairs (SAFEA) programme and an invited talk for the 10th anniversary of collaboration programme between UWS and Changchun Institute of Technology, China.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -