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Title	Analysis Of Multi-Dimensional Road Accident Data For Disaster Management In Smart Cities
ID_Doc	9226
Authors	Kolisnyk M.; Kwiatkowski M.R.; Leung C.K.; Zacharias B.
Year	2022
Published	Proceedings - 2022 IEEE 23rd International Conference on Information Reuse and Integration for Data Science, IRI 2022
DOI	http://dx.doi.org/10.1109/IRI54793.2022.00022
Abstract	In the current data-driven era, large volumes of data of different dimensions are generated and collected at a rapid rate. Examples of these big data include transportation data (e.g., traffic accident data). Integration of different transportation data, as well as reuse of past knowledge and information on public transit, can be for social good (e.g., can help road users avoid traffic accidents). Multi-dimensional data analysis and mining helps reveal factors associating with, or contributing to, traffic accidents. To manage this type of human-made disaster, we present in this paper a data science solution for multi-dimensional analysis of traffic accident data. It integrates heterogeneous data regarding vehicles, accidents and causality. It reuses past knowledge and information discovered from historical data for handling future situations. Evaluation on real-life accident data from the UK reveals some common conditions leading to serious and/or fatal accidents. It demonstrates the practicality of our solution in multi-dimensional analysis of traffic accident data, as well as the benefits of data integration and information (and knowledge) reuse, for disaster management in smart cities. Moreover, it is important to note that, although we illustrate our solution on UK accident data, our solution is expected to be reusable for the analysis of traffic accidents, support of disaster management, and building of smart cities at other locations.
Author Keywords	Association rule; data mining; data science; disaster management; information integration; information reuse; multi-dimensional data; smart city; traffic accident; transportation

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