A Classification Model for Predicting Road Accidents Using Web Data

Authors

  • Luan Sinanaj South East European University, North Macedonia https://orcid.org/0009-0009-4852-5571
  • Erind Bedalli University of Elbasan, North Macedonia
  • Lejla Abazi Bexheti South East European University, North Macedonia

DOI:

https://doi.org/10.54820/entrenova-2023-0006

Keywords:

data mining, web scraping, classification model, road accident prediction

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.

Author Biographies

Luan Sinanaj, South East European University, North Macedonia

Luan Sinanaj is a PhD student at South East European University, North Macedonia, working as a full-time lecturer in the Department of Information Technology at "Aleksandër Moisiu" University of Durrës. He completed his B.Sc. studies in the "Informatics" program at the University of Pisa, Italy. He then pursued a professional master's degree in "Internet Technologies" at the same university. Later, he graduated with a scientific master's degree in the "Economic Informatics" program at the European University of Tirana. PhD(c) Sinanaj is the co-author of the book "Basics of Programming in JAVA" and the author or co-author of several publications at national and international conferences. Also, his work and research experiences and interests are in Programming, Data Mining and Artificial Intelligence. The author can be contacted at ls30441@seeu.edu.mk

Erind Bedalli, University of Elbasan, North Macedonia

Erind Bedalli is a lecturer in the Department of Informatics at the University of Elbasan. He has received his B.Sc. degree in Computer Engineering from Hacettepe University, Ankara, and his M.Sc. degree in Informatics from the University of Tirana. He completed his doctoral studies in fuzzy logic and exploratory data analysis at the University of Tirana in 2014. His research experience and interests include fuzzy logic, data mining, artificial intelligence, and large-scale computing. The author can be contacted at erind.bedalli@uniel.edu.al

Lejla Abazi Bexheti, South East European University, North Macedonia

Lejla Abazi Bexheti is an Associate Professor at the Faculty of Contemporary Sciences and Technologies at South East European University in Macedonia. She holds a PhD in Computer Science and has been part of the CST teaching staff since 2002. Her main research activity is in Learning Systems and eLearning, and she has been involved in many international projects and research activities in this area. At SEE University, she was involved in resolving the Learning Management System issue. Currently, she is Pro-rector for academic issues at SEEU. The author can be contacted at l.abazi@seeu.edu.mk

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Published

2024-08-26

How to Cite

Sinanaj, L. ., Bedalli, E. ., & Abazi Bexheti, L. . (2024). A Classification Model for Predicting Road Accidents Using Web Data. ENTRENOVA - ENTerprise REsearch InNOVAtion, 9(1), 60–71. https://doi.org/10.54820/entrenova-2023-0006

Issue

Vol. 9 No. 1 (2023)

Section

Mathematical and Quantitative Methods

License

Copyright (c) 2024 Erind Bedalli, Luan Sinanaj, Lejla Abazi Bexheti

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.