Vehicle incident hot spots identification: An approach for big data

Triguero, Isaac; Figueredo, Grazziela P.; Mesgarpour, Mohammad; Garibaldi, Jonathan M.; John, Robert

Abstract

In this work we introduce a fast big data approach for road incident hot spot identification using Apache Spark. We implement an existing immuno-inspired mechanism, namely SeleSup, as a series of MapReduce-like operations. SeleSup is composed of a number of iterations that remove data redundancies and result in the detection of areas of high likelihood of vehicles incidents. It has been successfully applied to large datasets, however, as the size of the data increases to millions of instances, its performance drops significantly. Our objective therefore is to re-conceptualise the method for big data. In this paper we present the new implementation, the challenges faced when converting the method for the Apache Spark platform as well as the outcomes obtained. For our experiments we employ a large dataset containing hundreds of thousands of Heavy Good Vehicles incidents, collected via telematics. Results show a significant improvement in performance with no detriment to the accuracy of the method.

Citation

Triguero, I., Figueredo, G. P., Mesgarpour, M., Garibaldi, J. M., & John, R. (2017, August). Vehicle incident hot spots identification: An approach for big data. Presented at 2017 IEEE Trustcom/BigDataSE/ICESS, Sydney, NSW, Australia

• Presentation Conference Type: Edited Proceedings
• Conference Name: 2017 IEEE Trustcom/BigDataSE/ICESS
• Start Date: Aug 1, 2017
• End Date: Aug 4, 2017
• Acceptance Date: Jun 15, 2017
• Online Publication Date: Sep 11, 2017
• Publication Date: 2017
• Deposit Date: Aug 30, 2017
• Publicly Available Date: Sep 11, 2017
• Electronic ISSN: 2324-9013
• Peer Reviewed: Peer Reviewed
• Pages: 901-908
• Series ISSN: 2324-9013
• Book Title: Proceedings - 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications; 11th IEEE International Conference on Big Data Science and Engineering (IEEE BigDataSE); and 14th IEEE International Conference on Embedded So
• ISBN: 978-1-5090-4907-3
• DOI: https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.329
• Public URL: https://nottingham-repository.worktribe.com/output/881990
• Publisher URL: http://ieeexplore.ieee.org/document/8029532/
• Additional Information: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works.
• Contract Date: Aug 30, 2017