Improved model for traffic accident management system using KDD and big data: case study Jordan


  • Faisal Alzyoud Department of Computer Science, Isra Univerity, Jordan



artificial intelligence, machine learning, big data, Jordan, KDD, road accidents, traffic management


This paper addresses the challenge of the increasing number of traffic accidents resulting from population growth and increased vehicle usage, leading to significant economic and environmental impacts. To address this issue, this paper proposes an improved model for traffic accident management that employs Knowledge Discovery in Databases (KDD) and extensive data analysis techniques to extract the main factors contributing to car accidents. A case study of traffic accidents in Jordan is conducted, utilizing actual data from the Department of Statistics and the Public Security Directorate Records between 2016 to 2020 to identify the primary factors contributing to road accidents and their effects on various types of injuries. The study identifies 11 factors that contribute to car accidents, with driver error being the primary factor for increasing the number of accidents and injuries. Additionally, minor car accidents are found to cause more injuries based on the analysis of the proposed approach. The findings provide a solid foundation for developing an accurate and precise scheme to reduce or eliminate the number of road accidents, which is essential for transitioning to smart cities. Using big data and KDD techniques could significantly improve current traffic accident management practices, informing the development of new policies and regulations to improve road safety and reduce economic and environmental impacts. This paper presents a promising solution for addressing traffic accident management, contributing to safer and sustainable urban environments.


Alzyoud, Faisal and Sharman, Nesreen AL and Al-Roosan, Thamer and Alsalah, Yahya (2019). Smart accident management in jordan using cup carbon simulation. European Journal of Scientific Research, 152(2), 128-135, 2019.

Yan, Ying and Zhang, Shen and Tang, Jinjun and Wang, Xiaofei (2017). Understanding characteristics in multivariate traffic flow time series from complex network structures, Physica A: Statistical Mechanics and its Applications, 477, 149-160, 2017.

Li, Ming and Song, Guohua and Cheng, Ying and Yu, Lei (2015). Identification of prior factors influencing the mode choice of short-distance travel, Discrete Dynamics in Nature and Society, 2015, 2015.

Al-Rousan, Taleb M and Umar, Abdullahi A and Al-Omari, Aslam A (2021). Characteristics of crashes caused by distracted driving on rural and suburban roadways in Jordan, Infrastructures, 6(8), 107, 2021.

Iyanda, Ayodeji E and Osayomi, Tolulope (2021). Is there a relationship between economic indicators and road fatalities in Texas? A multiscale geographically weighted regression analysis, GeoJournal, 86(6), 2787-2807, 2021.

Timotius, Elkana (2021). The implications of digital transformation on developing human resources in business practices in Indonesia: Ana..., International Journal of Business, Economics, and Management, 2021.

Fayyad, Usama M and Piatetsky-Shapiro, Gregory and Smyth, and Padhraic (1996). Knowledge discovery and data mining: Towards a unifying framework, KDD, 96, 82-88, 1996.

Saraswat, Deepak (2017). Knowledge discovery with a hybrid data mining approach, Agra, 2017.

Tavallaee, Mahbod and Bagheri, Ebrahim and Lu, Wei and Ghorbani, Ali A (2009). A detailed analysis of the kdd cup 99 data set, 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, 1-6, 2009.

Guarino, Nicola and Oberle, Daniel and Staab, Steffen (2009). What is an ontology? Handbook on Ontologies, 1-17, 2009.

Abuhammad, Huthaifa and Everson, Richard (2018). Emotional faces in the wild: Feature descriptors for emotion classification, International Conference Image Analysis and Recognition, 164-174, 2018.

Younis, Mohammed Chachan and Abuhammad, Huthaifa (2021). A hybrid fusion framework to multi-modal bio metric identification, Multimedia Tools and Applications, 1-24, 2021.

Singh, Jaskaran and Singla, Varun (2015). Big data: tools and technologies in big data, International Journal of Computer Applications, 112(15), 2015.

Cunningham, Hamish (2002). Gate, a general architecture for text engineering, Computers and the Humanities, 36(2), 223-254, 2002.

Ngo, Duy Hoa and Bellahsene, Zohra (2012). Yam++: A multi-strategy based approach for ontology matching task, International Conference on Knowledge Engineering and Knowledge Management, 421-425, 2012.

Jirkovský, Vaclav and Obitko, Marek and Novák, Petr and Kadera, Petr (2014). Big data analysis for sensor time-series in automation, Proceedings of the 2014 IEEE Emerging Technology and Factory Automation (ETFA), 1-8, 2014.

Ahrens, James and Hendrickson, Bruce and Long, Gabrielle and Miller, Steve and Ross, Rob and Williams, Dean (2011). Data-intensive science in the US DOE: case studies and future challenges, Computing in Science & Engineering, 13(6), 14-24, 2011.

vost and Fawcett, Tom (2013). Data science and its relationship to big data and data driven decision making, Big Data, 1(1), 51-59, 2013.

Bamiah, S. N. and Brohi, Sarfraz N. and Rad, Babak Bashari (2018). Big data technology in education: Advantages, implementations, and challenges, Journal of Engineering Science and Technology, 13, 229-241, 2018.

Zheng, Kangning and Zhang, Zuopeng and Song, Bin (2020). E-commerce logistics distribution mode in big-data context: A case analysis of JD. COM, Industrial Marketing Management, 86, 154-162, 2020.

Zhu, Xingquan and Davidson, Ian, eds. (2007). Knowledge Discovery and Data Mining: Challenges and Realities: Challenges and Realities, Igi Global,

Al-Janabi, Samaher (2021). Overcoming the Main Challenges of Knowledge Discovery through Tendency to the Intelligent Data Analysis, 2021 International Conference on Data Analytics for Business and Industry (ICDABI), IEEE, 2021.

Bhatia, P. (2019). Introduction to Data Mining, Data Mining and Data Warehousing: Principles and Practical Techniques, 17-27, Cambridge: Cambridge University Press, doi:10.1017/9781108635592.00, 2019.

Asrin, Fauzan et al. (2020). Knowledge Data Discovery (Frequent Pattern Growth): The Association Rules for Evergreen Activities on Computer Monitoring, International Conference on Intelligent and Fuzzy Systems, Springer, Cham, 2020.

Al Zyadat, W Jum'ah and Alzyoud, Faisal Y and Alhroob, Aysh M and Samawi, Venus (2018). Securitizing big data characteristics used tall array and mapreduce, International Journal of Engineering & Technology, 7(4), 5633-5639, 2018.

Arbuckle, James and Wothke, Werner (2004). Structural equation modeling using AMOS: An introduction, EB, 2004

Schumacker, Randall E and Lomax, Richard G (2004). A beginner's guide to structural equation modeling, Psychology Press,

Othman, Suad Mohammed et al. (2018). Intrusion detection model using machine learning algorithm on Big Data environment, Journal of Big Data, 5(1), 1-12, 2018.

Yang, Yang and Yuan, Zhenzhou and Meng, Ran (2022). Exploring traffic crash occurrence mechanism toward cross-area freeways via an improved data mining approach, Journal of Transportation Engineering, Part A: Systems, 148(9), 04022052, 2022.

Yang, Yang et al. (2022). A parallel FP-growth mining algorithm with load balancing constraints for traffic crash data, International Journal of Computers Communications & Control, 17(4), 2022.

Yang, Yang et al. (2022). Predicting freeway traffic crash severity using the XGBoost-Bayesian network model with consideration of features interaction, Journal of Advanced Transportation, 2022, 2022.

Tarawneh, Monther and AlZyoud, Fiasal and Sharab, Yousef (2023). Artificial Intelligence Traffic Analysis Framework for Smart Cities, Computing Conference, IEEE, 2023.

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