Detecting danger in roads: an immune-inspired technique to identify heavy goods vehicles incident hot spots
We report on the adaptation of an immune-inspired instance selection technique to solve a real-world big data problem of determining vehicle incident hot spots. The technique, which is inspired by the Immune System self-regulation mechanism, was originally conceptualised to eliminate very similar in...
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| Format: | Article |
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Institute of Electrical and Electronics Engineers
2017
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| Online Access: | https://eprints.nottingham.ac.uk/44196/ |
| _version_ | 1848796860221751296 |
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| author | Figueredo, Grazziela P. Triguero, Isaac Mesgarpour, Mohammad Maciel Guerra, Alexandre Garibaldi, Jonathan M. John, Robert |
| author_facet | Figueredo, Grazziela P. Triguero, Isaac Mesgarpour, Mohammad Maciel Guerra, Alexandre Garibaldi, Jonathan M. John, Robert |
| author_sort | Figueredo, Grazziela P. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | We report on the adaptation of an immune-inspired instance selection technique to solve a real-world big data problem of determining vehicle incident hot spots. The technique, which is inspired by the Immune System self-regulation mechanism, was originally conceptualised to eliminate very similar instances in data classification tasks. We adapt the method to detect hot spots from a telematics data set containing hundreds of thousands of data points indicating incident locations involving heavy goods vehicles (HGVs) across the United Kingdom. The objective is to provide HGV drivers with information regarding areas of high likelihood of incidents in order to continuously improve road safety and vehicle economy. The problem presents several challenges and constraints. An accurate view of the hot spots produced in a timely manner is necessary. In addition, the solution is required to be adaptable and dynamic, as thousands of new incidents are included in the database daily. Furthermore, the impact on hot spots after informing drivers about their existence has to be considered. Our solution successfully addresses these constraints. It is fast, robust, and applicable to all different incidents investigated. The method is also self-adjustable, which means that if the hot spots’ configuration changes with time, the algorithm automatically evolves to present the most current topology. Our solution has been implemented by a telematics company to improve HGV safety in the United Kingdom. |
| first_indexed | 2025-11-14T19:54:41Z |
| format | Article |
| id | nottingham-44196 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:54:41Z |
| publishDate | 2017 |
| publisher | Institute of Electrical and Electronics Engineers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-441962020-05-04T18:51:24Z https://eprints.nottingham.ac.uk/44196/ Detecting danger in roads: an immune-inspired technique to identify heavy goods vehicles incident hot spots Figueredo, Grazziela P. Triguero, Isaac Mesgarpour, Mohammad Maciel Guerra, Alexandre Garibaldi, Jonathan M. John, Robert We report on the adaptation of an immune-inspired instance selection technique to solve a real-world big data problem of determining vehicle incident hot spots. The technique, which is inspired by the Immune System self-regulation mechanism, was originally conceptualised to eliminate very similar instances in data classification tasks. We adapt the method to detect hot spots from a telematics data set containing hundreds of thousands of data points indicating incident locations involving heavy goods vehicles (HGVs) across the United Kingdom. The objective is to provide HGV drivers with information regarding areas of high likelihood of incidents in order to continuously improve road safety and vehicle economy. The problem presents several challenges and constraints. An accurate view of the hot spots produced in a timely manner is necessary. In addition, the solution is required to be adaptable and dynamic, as thousands of new incidents are included in the database daily. Furthermore, the impact on hot spots after informing drivers about their existence has to be considered. Our solution successfully addresses these constraints. It is fast, robust, and applicable to all different incidents investigated. The method is also self-adjustable, which means that if the hot spots’ configuration changes with time, the algorithm automatically evolves to present the most current topology. Our solution has been implemented by a telematics company to improve HGV safety in the United Kingdom. Institute of Electrical and Electronics Engineers 2017-06-25 Article PeerReviewed Figueredo, Grazziela P., Triguero, Isaac, Mesgarpour, Mohammad, Maciel Guerra, Alexandre, Garibaldi, Jonathan M. and John, Robert (2017) Detecting danger in roads: an immune-inspired technique to identify heavy goods vehicles incident hot spots. IEEE Transactions on Emerging Topics in Computational Intelligence . ISSN 2471-285X (In Press) Hot Spots Road incidents Instance selection Telematics Big Data Artificial Immune Systems |
| spellingShingle | Hot Spots Road incidents Instance selection Telematics Big Data Artificial Immune Systems Figueredo, Grazziela P. Triguero, Isaac Mesgarpour, Mohammad Maciel Guerra, Alexandre Garibaldi, Jonathan M. John, Robert Detecting danger in roads: an immune-inspired technique to identify heavy goods vehicles incident hot spots |
| title | Detecting danger in roads: an immune-inspired technique to identify heavy goods vehicles incident hot spots |
| title_full | Detecting danger in roads: an immune-inspired technique to identify heavy goods vehicles incident hot spots |
| title_fullStr | Detecting danger in roads: an immune-inspired technique to identify heavy goods vehicles incident hot spots |
| title_full_unstemmed | Detecting danger in roads: an immune-inspired technique to identify heavy goods vehicles incident hot spots |
| title_short | Detecting danger in roads: an immune-inspired technique to identify heavy goods vehicles incident hot spots |
| title_sort | detecting danger in roads: an immune-inspired technique to identify heavy goods vehicles incident hot spots |
| topic | Hot Spots Road incidents Instance selection Telematics Big Data Artificial Immune Systems |
| url | https://eprints.nottingham.ac.uk/44196/ |