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Predicting Road Accidents with Web Scraping and Machine Learning Techniques

In: Economic Recovery, Consolidation, and Sustainable Growth

Author

Listed:
  • Luan Sinanaj

    (South East European University)

  • Lejla Abazi Bexheti

    (South East European University)

Abstract

In recent decades, with the rapid growth of urbanization, the use of vehicles has increased and urbanization has not always been adapted to road infrastructure. This, in turn, has led to an increase in traffic and road accidents, especially in the most populated areas. Regarding these problems, there is a need to focus on accident prevention by predicting the accident areas. Knowing the accident areas can facilitate the study of why they occur and improve policies to prevent them. The primary purpose of this research is to build an application using Python to identify accident areas in the state of Albania. Accident information is extracted through the application and Web Scraping techniques from some selected servers of the top media in Albania and it is processed to find the areas/cities where it happened. As a result of this research, an application is implemented. From the results obtained from this application, its accuracy (precision) in finding accident areas turns out to be 67%. The result is good and hopeful for further work in the future and for the improvement of this application. The predicted results of our application for the number of accidents are a little closer to the results of the accident reports published by the state of Albania.

Suggested Citation

  • Luan Sinanaj & Lejla Abazi Bexheti, 2023. "Predicting Road Accidents with Web Scraping and Machine Learning Techniques," Springer Proceedings in Business and Economics, in: Abdylmenaf Bexheti & Hyrije Abazi-Alili & Léo-Paul Dana & Veland Ramadani & Andrea Caputo (ed.), Economic Recovery, Consolidation, and Sustainable Growth, pages 781-792, Springer.
    • Handle: RePEc:spr:prbchp:978-3-031-42511-0_51
    DOI: 10.1007/978-3-031-42511-0_51
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