skip to main content
10.1145/3551504.3551507acmotherconferencesArticle/Chapter ViewAbstractPublication PagesceeegovConference Proceedingsconference-collections
research-article

Using Business Intelligence to Analyze Road Traffic Accidents

Authors Info & Claims
Published:25 October 2022Publication History

ABSTRACT

Road Traffic Accidents constitute a significant concern around the world. Understanding the primary and contributing factors may combat traffic accidents and mitigate their impact. Based on an actual traffic accidents dataset of the United Arab Emirates (UAE) between 2012-2019, we investigate the importance of data science and Business Intelligence (BI) in visualizing traffic accidents in a descriptive format. The proposed BI solution provides visual data exploration for authorities to analyze and make informed decisions. This paper provides an example of how open data can save lives and resources. The design and implementation of the BI solution and its features are also presented in this paper.

References

  1. WHO. (June 2021). Road traffic injuries. World Health Organization, [Online]. Retrieved March 27, 2022 from https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuriesGoogle ScholarGoogle Scholar
  2. WHO. (December 2020). The top 10 causes of death. World Health Organization, [Online]. Retrieved March 25, 2022 from https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-deathGoogle ScholarGoogle Scholar
  3. Rayessa Absal. (March 2008). Traffic accidents 'are second leading cause of death in UAE. Gulf News [Online]. Retrieved March 21, 2022 from https://gulfnews.com/uae/transport/traffic-accidents-are-second-leading-cause-of-death-in-uae-1.89596Google ScholarGoogle Scholar
  4. Ismail Sebugwaawo. (March 2019). 2 People killed daily in crashes on UAE roads during last five years. Khaleej Times [Online]. Retrieved March 27, 2022 from https://www.khaleejtimes.com/news /transport/2-people-killed-daily-in-crashes-on-uae-roads-during-last-five-yearsGoogle ScholarGoogle Scholar
  5. Dana Shahin, Mohammed Awad, and Salam Fraihat. 2021. Meteorological Data Analytic System: Descriptive and Predictive Analysis. Journal of Theoretical and Applied Information Technology (JTAIT). Vol 99, No 14. ISSN 1992-8645Google ScholarGoogle Scholar
  6. Salam Fraihat, Walid A. Salameh, Ammar Elhassan, Bushra Abu Tahoun, and Maisa Asasfeh. 2021. Business Intelligence Framework Design and Implementation: A Real-estate Market Case Study. ACM Journal of Data and Information Quality (JDIQ) 13 (2).Google ScholarGoogle Scholar
  7. Layanah AlWreikat, Ali AlShawa, Dalia Al-Rimawi, and Salam Fraihat. 2019. Business intelligence and data analytics system for mobile money. Proceedings of the Second International Conference on Data Science. Dubai, UAEGoogle ScholarGoogle ScholarDigital LibraryDigital Library
  8. Batool AlArmouty and Salam Fraihat. 2019. Data analytics and business intelligence framework for stock market trading. 2019 2nd International Conference on New Trends in Computing Sciences. Amman, JordanGoogle ScholarGoogle ScholarCross RefCross Ref
  9. Khaled Assi, Syed. M. Rahman, Umer Mansoor, and Nedal Ratrout. 2020. Predicting crash injury severity with machine learning algorithm synergized with clustering technique: A promising protocol. International Journal of Environmental Research and Public Health, vol. 17, no. 15, pp. 2–17Google ScholarGoogle ScholarCross RefCross Ref
  10. Miao Chong, Ajith Abraham, and Marcin Paprzycki. 2004. Traffic accident data mining using machine learning paradigms. In Fourth International Conference on Intelligent Systems Design and Applications (ISDA'04). Hungary, pp. 415-420Google ScholarGoogle Scholar
  11. Jaspreet Singh, Gurvinder Singh, Prithvipal Singh, and Mandeep Kaur. 2019. Evaluation and classification of road accidents using machine learning techniques. In Emerging Research in Computing, Information, Communication and Applications, Singapore. pp. 193-204Google ScholarGoogle Scholar
  12. S. Krishnaveni and M. Hemalatha. 2011. A perspective analysis of traffic accident using data mining techniques. International Journal of Computer Applications. Vol. 23, no. 7, pp. 40–48Google ScholarGoogle ScholarCross RefCross Ref
  13. Laura Cuenca, Enrique Puertas, Nourdine Aliane, and Javier Andres. 2018. Traffic accidents classification and injury severity prediction. In 2018 3rd IEEE International Conference on Intelligent Transportation Engineering (ICITE). Singapore. pp. 52-57Google ScholarGoogle ScholarCross RefCross Ref
  14. Sharaf Alkheder, Madhar Taamneh, and Salah Taamneh. 2017. Severity prediction of traffic accident using an artificial neural network. Journal of Forecasting. vol. 36, no. 1, pp. 100–108Google ScholarGoogle ScholarCross RefCross Ref
  15. Madhar Taamneh, and Salah Taamneh. 2018. Evaluation of the performance of random forests technique in predicting the severity of road traffic accidents. In International Conference on Applied Human Factors and Ergonomics. Springer, Cham. pp. 840-847Google ScholarGoogle Scholar
  16. Md Farhan Labib, Ahmed Rifat, Md Hossain, Amit Das, and Faria Nawrine. 2019. Road accident analysis and prediction of accident severity by using machine learning in Bangladesh. In 2019 7th International Conference on Smart Computing & Communications (ICSCC). Sarawak, Malaysia. pp. 1-5Google ScholarGoogle ScholarCross RefCross Ref
  17. Jirapon Sunkpho and Warit Wipulanusat. 2020. The role of data visualization and analytics of highway accidents. Walailak Journal of Science and Technology (WJST). vol. 17, no. 12, pp. 1379-1389Google ScholarGoogle ScholarCross RefCross Ref
  18. Gulin Ulker and Erman Coskun. 2015. The analysis of traffic accidents with a business intelligence approach: An application in Turkey. In proceedings of the International Interdisciplinary Business and Economics Conference, Florida, United States. pp. 80-87Google ScholarGoogle Scholar
  19. Swapnil Nikam. 2020. Analysis of US accidents and solutions. Electronic Theses. pp. 1- 55Google ScholarGoogle Scholar
  20. MOI. (2019). Traffic accidents. Bayanat [Online]. Retrieved October 13, 2021 from https://bayanat.ae/Google ScholarGoogle Scholar
  21. James Jose. (July 2021). UAE: Dh400 fine for tailgating shouldn't be the only reason for you not to do it. Khaleej Times [Online]. Retrieved March 5, 2022 from https://www.khaleejtimes.com/uae/uae-dh400-fine-for-tailgating-shouldnt-be-the-only-reason-for-you-not-to-do-itGoogle ScholarGoogle Scholar
  22. Ismail Sebugwaawo. (March 2022). UAE: Dh400 fine for sudden lane changes, police warn motorists. Khaleej Times [Online]. Retrieved March 5, 2022 from https://www.khaleejtimes.com/transport/uae-dh400-fine-for-sudden-change-of-lanes-police-warn-motoristsGoogle ScholarGoogle Scholar
  23. U.AE. (Last updated Feb 2022). Road Safety [Online]. Retrieved March 9, 2022 from https://u.ae/en/information-and-services/justice-safety-and-the-law/road-safetyGoogle ScholarGoogle Scholar
  24. Ismail Sebugwaawo. (April 2021). Ramadan in UAE: Over 1,400 minor accidents recorded during first week of holy month. Khaleej Times [Online]. Retrieved March 27, 2022 from https://www.khaleejtimes.com/news/ramadan-in-uae-over-1400-minor-accidents-recorded-during-first-week-of-holy-monthGoogle ScholarGoogle Scholar
  25. Josh Fruhlinger and Mary Pratt. (Oct. 2019). What is business intelligence? Turning data into business insights. CIO [Online]. Retrieved March 3, 2022 from https://www.cio.com/article/2439504/business-intelligence-definition-and-solutions.htmlGoogle ScholarGoogle Scholar
  26. Microsoft Team. (Oct. 2021). What is Power BI desktop?. Microsoft [Online]. Retrieved October 19, 2021 from https://docs.microsoft.com/en-us/power-bi/fundamentals/desktop-what-is-desktopGoogle ScholarGoogle Scholar

Index Terms

  1. Using Business Intelligence to Analyze Road Traffic Accidents
        Index terms have been assigned to the content through auto-classification.

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in
        • Published in

          cover image ACM Other conferences
          CEEeGov '22: Proceedings of the Central and Eastern European eDem and eGov Days
          September 2022
          192 pages
          ISBN:9781450397667
          DOI:10.1145/3551504

          Copyright © 2022 ACM

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 25 October 2022

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article
          • Research
          • Refereed limited

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        HTML Format

        View this article in HTML Format .

        View HTML Format