A novel Bayesian hierarchical model for road safety hotspot prediction
- Submitting institution
-
University of Newcastle upon Tyne
- Unit of assessment
- 12 - Engineering
- Output identifier
- 237263-70951-1293
- Type
- D - Journal article
- DOI
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10.1016/j.aap.2016.11.021
- Title of journal
- Accident Analysis & Prevention
- Article number
- -
- First page
- 262
- Volume
- 99
- Issue
- Part A
- ISSN
- 0001-4575
- Open access status
- Compliant
- Month of publication
- December
- Year of publication
- 2016
- URL
-
https://doi.org/10.1016/j.aap.2016.11.021
- 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
-
3
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper creates a novel Bayesian hierarchical model to predict where traffic collisions are likely to happen in the future, allowing for targeted investment. The method is implemented in the open source RAPTOR software and in PTV’s commercial VISUM Safety software (paulo.humanes@ptvgroup.com). It has been applied around the world (e.g. Bolivia, Lisbon, New York, North Yorkshire, Tyne and Wear), and is used by national highway authorities and police and local road safety partnerships (e.g. www.northyorkshire-pfcc.gov.uk and andywaters@gateshead.gov.uk). Funded by industry, EPSRC and government, the work underpins a 2021 Impact Case Study.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -