A Classification Model for Predicting Road Accidents Using Web Data

Luan Sinanaj, Erind Bedalli and Lejla Abazi Bexheti

A chapter in Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Hybrid Conference, Dubrovnik, Croatia, 4-6 September, 2023, 2023, pp 60-71 from IRENET - Society for Advancing Innovation and Research in Economy, Zagreb

Abstract: The increase in urbanisation and the use of vehicles in recent decades has also led to increased road accidents. The causes of road accidents can be various, including human error, weather conditions or even inadequate road infrastructure. Knowing the causes and areas of road accidents can help prevent them by state institutions taking necessary measures and citizens being informed about the areas of road accidents. The primary purpose of this study is to explore patterns in accident web data in Albania and to construct a classification model using data mining techniques and methods. These techniques have been applied to data obtained from several leading media portals in Albania, including about 30,000 articles from online portals and reports from the state authorities. The constructed classification model is expected to be utilised to predict the accident likelihood according to the locations, weather, and period of the year.

Keywords: data mining; web scraping; classification model; road accident prediction (search for similar items in EconPapers)
JEL-codes: C3 C6 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:entr23:302069

DOI: 10.54820/entrenova-2023-0006

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