An Immune-Inspired Technique to Identify Heavy Goods Vehicles Incident Hot Spots
- Submitting institution
-
University of Nottingham, The
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1318888
- Type
- D - Journal article
- DOI
-
10.1109/TETCI.2017.2721960
- Title of journal
- IEEE Transactions on Emerging Topics in Computational Intelligence
- Article number
- -
- First page
- 248
- Volume
- 1
- Issue
- 4
- ISSN
- 2471-285X
- Open access status
- Compliant
- Month of publication
- August
- Year of publication
- 2017
- 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
- No
- Number of additional authors
-
5
- Research group(s)
-
-
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper presents a novel approach to HGV incident hot spot identification using an immune-inspired instance selection algorithm. Our solution addresses the real-world big data stream transportation problem accurately and efficiently. Experiments were conducted with data provided by a partner telematics company, the largest dataset ever used in the literature, with millions of instances. The solution has now been adopted by the company to detect areas of incidents, accidents and crime, helping to promote danger awareness and safe driving in over 30% of the HGV fleet in the UK, across Europe and India. Industry contact: Mohammad Mesgarpour, Microlise https://www.linkedin.com/in/mmesgarpour/?originalSubdomain=uk
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
