Abstract: Traffic congestion is a global problem in urban areas that creates longer travel times, increased fuel consumption, and elevated levels of pollution. Traffic congestion occurs because of the exponential growth of vehicles along with a finite number of roadways and the inability to manage traffic effectively. This paper studies the question: How well can traffic type factors be used as a predictor for determining the severity of traffic con-gestion? To answer this question, we present a new methodology to perform clustering and classification based on various types of traffic indicators. In addition, traffic indicators (such as size of roadway, speed of vehicles, number of vehicles, and level of traffic flow) are categorized by using two distinct classifications: homogeneous and heterogeneous. Using these categories, we then apply a modified version of the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to do clustering of traffic indicators. The resultant label from the clustering process is then used to develop a prediction model that will provide information regarding the level of traffic congestion along a selected roadway. Results from our experiments were conducted using an actual dataset and demonstrate that our proposed method produced an accuracy rate of 93% with 92%precision and recall, and therefore, outperforming other current methodologies used for predicting traffic congestion. Overall, these findings indicate that incorporating an analysis of traffic type factors into the clustering and classification methodology can result in more accurate predictions of traffic congestion.

Chaimae Kanzouai, Abderrahim Zannou, Soukaina BOUAROUROU, El Habib Nfaoui and Abdelhak Boulaalam. “A Novel Approach for Urban Traffic Congestion Prediction”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.11 (2025). http://dx.doi.org/10.14569/IJACSA.2025.01611102

@article{Kanzouai2025,
title = {A Novel Approach for Urban Traffic Congestion Prediction},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.01611102},
url = {http://dx.doi.org/10.14569/IJACSA.2025.01611102},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {11},
author = {Chaimae Kanzouai and Abderrahim Zannou and Soukaina BOUAROUROU and El Habib Nfaoui and Abdelhak Boulaalam}
}