Abstract: It is very important for traffic management to be able to correctly recognize traffic trends from large historical traffic data, particularly the congestion pattern and road collisions. This can be used to reduce congestion, improve protection, and increase the accuracy of traffic forecasting. Choosing the correct and effective text mining methodology helps speed up and reduces the time and effort needed to retrieve valuable knowledge and information for future prediction and decision-making processes. Modeling collisions or accident risk has also been an important aspect of traffic management and road safety, as it helps recognize problems and causes that contribute to a higher risk of accidents, promotes treatment delivery, and reduces crashes to save more lives and avoid road congestion. Therefore, this work-study proposed a model that relies on the different text mining methodology to determine clearly what circumstances affect and who is involved more in an accident. Using different classification and machine learning techniques applied to get the optimum classifiers used in this model. The experimental results on real-world datasets demonstrate that the proposed models outperform Prayag Tiwari’s Research Work related to the Leeds UK Dataset.

Ahmed Ibrahim Naguib, Hala Abdel-Galil and Sayed AbdelGaber, “A Model for Traffic Management based on Text Mining Techniques” International Journal of Advanced Computer Science and Applications(IJACSA), 11(12), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0111280

@article{Naguib2020,
title = {A Model for Traffic Management based on Text Mining Techniques},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0111280},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0111280},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {12},
author = {Ahmed Ibrahim Naguib and Hala Abdel-Galil and Sayed AbdelGaber}
}