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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

Authors

Mohammad Mesgarpour

Alexandre Maciel Guerra

Robert John



Abstract

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.

Citation

Figueredo, G. P., Triguero, I., Mesgarpour, M., Maciel Guerra, A., Garibaldi, J. M., & John, R. (2017). An Immune-Inspired Technique to Identify Heavy Goods Vehicles Incident Hot Spots. IEEE Transactions on Emerging Topics in Computational Intelligence, 1(4), 248-258. https://doi.org/10.1109/TETCI.2017.2721960

Journal Article Type Article
Acceptance Date Jun 25, 2017
Publication Date 2017-08
Deposit Date Jul 18, 2017
Publicly Available Date Mar 29, 2024
Journal IEEE Transactions on Emerging Topics in Computational Intelligence
Electronic ISSN 2471-285X
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 1
Issue 4
Pages 248-258
DOI https://doi.org/10.1109/TETCI.2017.2721960
Keywords Hot Spots, Road incidents, Instance selection, Telematics,Big Data, Artificial Immune Systems
Public URL https://nottingham-repository.worktribe.com/output/867798
Publisher URL https://ieeexplore.ieee.org/document/8006368
Additional Information ©2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.

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