Vehicle incident hot spots identification: an approach for big data
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 a...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
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2017
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| Online Access: | https://eprints.nottingham.ac.uk/45214/ |
| _version_ | 1848797090880159744 |
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| author | Triguero, Isaac Figueredo, Grazziela P. Mesgarpour, Mohammad Garibaldi, Jonathan M. John, Robert |
| author_facet | Triguero, Isaac Figueredo, Grazziela P. Mesgarpour, Mohammad Garibaldi, Jonathan M. John, Robert |
| author_sort | Triguero, Isaac |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:58:21Z |
| format | Conference or Workshop Item |
| id | nottingham-45214 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:58:21Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-452142020-05-04T19:05:46Z https://eprints.nottingham.ac.uk/45214/ Vehicle incident hot spots identification: an approach for big data Triguero, Isaac Figueredo, Grazziela P. Mesgarpour, Mohammad Garibaldi, Jonathan M. John, Robert 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. 2017-09-11 Conference or Workshop Item PeerReviewed Triguero, Isaac, Figueredo, Grazziela P., Mesgarpour, Mohammad, Garibaldi, Jonathan M. and John, Robert (2017) Vehicle incident hot spots identification: an approach for big data. In: 11th IEEE International Conference on Big Data Science and Engineering (IEEE BigDataSE)i, 1-4 August 2017, Sydney, Australia. http://ieeexplore.ieee.org/document/8029532/ doi:10.1109/Trustcom/BigDataSE/ICESS.2017.329 doi:10.1109/Trustcom/BigDataSE/ICESS.2017.329 |
| spellingShingle | Triguero, Isaac Figueredo, Grazziela P. Mesgarpour, Mohammad Garibaldi, Jonathan M. John, Robert Vehicle incident hot spots identification: an approach for big data |
| title | Vehicle incident hot spots identification: an approach for big data |
| title_full | Vehicle incident hot spots identification: an approach for big data |
| title_fullStr | Vehicle incident hot spots identification: an approach for big data |
| title_full_unstemmed | Vehicle incident hot spots identification: an approach for big data |
| title_short | Vehicle incident hot spots identification: an approach for big data |
| title_sort | vehicle incident hot spots identification: an approach for big data |
| url | https://eprints.nottingham.ac.uk/45214/ https://eprints.nottingham.ac.uk/45214/ https://eprints.nottingham.ac.uk/45214/ |