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Vehicle incident hot spots identification: an approach for big data

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

Authors

Mohammad Mesgarpour

Robert John robert.john@nottingham.ac.uk



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). Vehicle incident hot spots identification: an approach for big data. https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.329

Conference Name 11th IEEE International Conference on Big Data Science and Engineering (IEEE BigDataSE)i
End Date Aug 4, 2017
Acceptance Date Jun 15, 2017
Publication Date Sep 11, 2017
Deposit Date Aug 30, 2017
Publicly Available Date Sep 11, 2017
Electronic ISSN 2324-9013
Peer Reviewed Peer Reviewed
DOI https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.329
Public URL http://eprints.nottingham.ac.uk/id/eprint/45214
Publisher URL http://ieeexplore.ieee.org/document/8029532/
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf
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.

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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