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Title	Employing Applying Big Data Analytics Lifecycle In Uncovering The Factors That Relate To Causing Road Traffic Accidents To Reach Sustainable Smart Cities
ID_Doc	22882
Authors	Allaymoun M.H.; Elastal M.; Alastal A.Y.; Elbastawisy T.K.; Iqbal D.; Yaqoob A.; Ehsan A.S.
Year	2024
Published	Studies in Systems, Decision and Control, 487
DOI	http://dx.doi.org/10.1007/978-3-031-35828-9_18
Abstract	This paper aims to identify the factors that relate to causing road traffic accidents to reach sustainable smart cities—this issue is significant to society and people. To that end, the paper pro-poses a model for uncovering such factors using big data analytics lifecycle: discovery, data preparation, model planning, model building, communication of results, and operationalization. In other words, the paper presents a simplified methodology taking advantage of the big data analysis life cycle and the velocity of analyzing data to reduce traffic accidents—velocity is the most important characteristic of big data. The velocity in analyzing data and identifying the factors that relate to causing car accidents helps decision-makers quickly make appropriate decisions. Worthy of mentioning that Google Data Studio (GDS) was used here to produce the visualizations and reports needed. Hopefully, researchers will build on this methodology in the future to achieve sustainability, to propose a more inclusive and effective methodology to minimize the negative effects of traffic accidents in the smart cities. © The Author(s), under exclusive license to Springer Nature Switzerland AG. 2024.
Author Keywords	Big data; Big data analysis lifecycle; Decision making; Google data studio; Road traffic accidents; SDG11

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