Pattern Recognition Approach for Road Collision Hotspots Analysis: Case Study of Northern Ireland

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In order to address road safety effectively, it is essential to understand all the factors, which
attribute to the occurrence of a road collision. This is achieved through road safety
assessment measures, which are primarily based on historical crash data. Recent advances
in uncertain reasoning technology have led to the development of robust machine learning
techniques, which are suitable for investigating road traffic collision data. These techniques
include supervised learning (e.g. SVM) and unsupervised learning (e.g. Cluster Analysis).
This study extends upon previous research work, carried out in Coll et al. [3], which
proposed a non-linear aggregation framework for identifying temporal and spatial hotspots.
The results from Coll et al. [3] identified Lisburn area as the hotspot, in terms of road safety,
in Northern Ireland. This study aims to use Cluster Analysis, to investigate and highlight any
hidden patterns associated with collisions that occurred in Lisburn area, which in turn, will
provide more clarity in the causation factors so that appropriate countermeasures can be put
in place.
Original languageEnglish
Title of host publicationProceedings of the ITRN2014
Number of pages8
Publication statusPublished - 2014
EventIrish Transport Research Network Conference - University of Limerick, Limerick, Ireland
Duration: 05 Sep 201406 Sep 2014


ConferenceIrish Transport Research Network Conference


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