Business Intelligence (BI) Approach for Traffic Accidents Analysis
DOI:
https://doi.org/10.58776/ijitcsa.v1i2.32Keywords:
Business Intelligence, accident analysis, traffic accidents, Data VisualizationAbstract
‘FLCRASH’ is the data source used for this report that consists of crash data from the year 2008 to 2009. The purpose of this report is to find ways to reduce the number of fatal accidents on the road by identifying the main culprits on why these accidents happen so frequently. Business intelligence is used in the process of finding the relationships and trends in the major causes of the crashes. The technologies related to Business Intelligence that will help in aiding the organization to their goal are also discussed along with the supporting factors for the use of Business Intelligence for the organization. A dashboard has been created from various graphs and charts for easy viewing and understanding of the situation so logical decisions can be made based on it. This report seeks to help us better understand the nature of why traffic accidents happen in the first place and how to prevent them from happening in the future.
References
. Y. Su, “Game Analytics Research: Status and trends,” Advances in E-Business Engineering for Ubiquitous Computing, pp. 572–589, 2019.
. X. Wang, Y. Peng, S. Yi, H. Wang, and W. Yu, “Risky behaviors, psychological failures and kinematics in vehicle-to-powered two-wheeler accidents: Results from in-depth Chinese crash data,” Accident Analysis & Prevention, vol. 156, p. 106150, 2021.
. F. Basso, R. Pezoa, M. Varas, and M. Villalobos, “A deep learning approach for real-time crash prediction using vehicle-by-vehicle data,” Accident Analysis & Prevention, vol. 162, p. 106409, 2021.
. N. Veeramisti, A. Paz, and J. Baker, “A framework for corridor-level traffic safety network screening and its implementation using business intelligence,” Safety Science, vol. 121, pp. 100–110, 2020.
. H. Surbakti and A. B. Ta'a, “Tacit Knowledge for Business Intelligence Framework: A Part of Unstructured Data?,” Journal of Theoretical and Applied Information Technology, vol. 96, no. 3, pp. 616–625, Feb. 2018.
. B. Parashar and G. Rana, “The impact of artificial intelligence on Global Business Practices,” Reinventing Manufacturing and Business Processes Through Artificial Intelligence, pp. 95–113, 2021.
. I. Fafaliou, M. Giaka, D. Konstantios, and M. Polemis, “Firms’ ESG reputational risk and market longevity: A firm-level analysis for the United States,” Journal of Business Research, vol. 149, pp. 161–177, 2022.
. V. Mkrttchian, L. A. Gamidullaeva, and S. Panasenko, “Optimizing and enhancing digital marketing techniques in Intellectual Big Data Analytics,” Advances in Data Mining and Database Management, pp. 98–109, 2019.
. A. A. Audu, O. F. Iyiola, A. A. Popoola, B. M. Adeleye, S. Medayese, C. Mosima, and N. Blamah, “The application of Geographic Information System as an intelligent system towards emergency responses in road traffic accident in Ibadan,” Journal of Transport and Supply Chain Management, vol. 15, 2021.
. S. Das, X. Kong, and I. Tsapakis, “Hit and run crash analysis using association rules mining,” Journal of Transportation Safety & Security, vol. 13, no. 2, pp. 123–142, 2019.
. K. Topuz and D. Delen, “A probabilistic bayesian inference model to investigate injury severity in automobile crashes,” Decision Support Systems, vol. 150, p. 113557, 2021.
. M. Shahzad, “Review of road accident analysis using GIS technique,” International Journal of Injury Control and Safety Promotion, vol. 27, no. 4, pp. 472–481, 2020.
. S. He, “Who is liable for the uber self-driving crash? analysis of the liability allocation and the regulatory model for Autonomous Vehicles,” Autonomous Vehicles, pp. 93–111, 2020.
. M. Khder, “Web scraping or web crawling: State of Art, Techniques, approaches and application,” International Journal of Advances in Soft Computing and its Applications, vol. 13, no. 3, pp. 145–168, 2021.
. A. Mohandu and M. Kubendiran, “Survey on big data techniques in intelligent transportation system (ITS),” Materials Today: Proceedings, vol. 47, pp. 8–17, 2021.
. Y. Wang, W. Xu, W. Zhang, and J. L. Zhao, “SafeDrive: A new model for driving risk analysis based on Crash avoidance,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 3, pp. 2116–2129, 2022.
. C. Lennerholt, J. Van Laere, and E. Söderström, “User-related challenges of self-service business intelligence,” Information Systems Management, vol. 38, no. 4, pp. 309–323, 2020.
. M. K. Daradkeh, “Exploring the usefulness of user-generated content for business intelligence in Innovation,” International Journal of Enterprise Information Systems, vol. 17, no. 2, pp. 44–70, 2021.
. M. D. Tamang, V. Kumar Shukla, S. Anwar, and R. Punhani, “Improving business intelligence through machine learning algorithms,” 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM), 2021.
. L. Wang, H. Zhong, W. Ma, M. Abdel-Aty, and J. Park, “How many crashes can connected vehicle and Automated Vehicle Technologies Prevent: A meta-analysis,” Accident Analysis & Prevention, vol. 136, p. 105299, 2020.
. H. Ospina-Mateus, L. A. Quintana Jiménez, F. J. Lopez-Valdes, and K. Salas-Navarro, “Bibliometric analysis in Motorcycle Accident Research: A global overview,” Scientometrics, vol. 121, no. 2, pp. 793–815, 2019.
. X. Zou, H. L. Vu, and H. Huang, “Fifty Years of Accident Analysis & Prevention: A Bibliometric and Scientometric Overview,” Accident Analysis & Prevention, vol. 144, p. 105568, 2020.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Shujana

This work is licensed under a Creative Commons Attribution 4.0 International License.
Attribution 4.0 International
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.


