PREDICTIVE MODELING FOR URBAN TRAFFIC OPTIMIZATION ENHANCING MOBILITY AND REDUCING CONGESTION COSTS

Abstract

The ongoing increase in urban populations has intensified the persistent issue of traffic congestion, negatively impacting the quality of life by increasing commute times, compromising road safety, and deteriorating local air quality. As a result, identifying and forecasting traffic congestion patterns has become critical, positioning Traffic Congestion Prediction (TCP) as a rapidly growing field of study. Recent advancements in Machine Learning (ML), Artificial Intelligence (AI), and Internet of Things (IoT) sensor technologies have further underscored the importance of TCP in the development of Intelligent Transportation Systems (ITS).This review paper explores advanced TCP methodologies, with a particular emphasis on innovative forecasting techniques and technologies vital to the ITS domain. We provide a comprehensive overview of statistical and machine learning approaches, including hybrid and ensemble models, that are commonly used for TCP. Additionally, the paper discusses various forecasting techniques and elaborates on performance evaluation metrics from both regression and classification perspectives.To provide a practical framework, a standardized step-bystep methodology often adopted in TCP problems is presented. As part of the implementation, a KNearest Neighbors (KNN) classifier was employed as the proposed predictive model. Comparative evaluation revealed that KNN outperformed the Logistic Regression model in terms of both accuracy and robustness in predicting traffic congestion events. The results validate the potential of data-driven approaches to enhance proactive traffic management and informed decision-making in the context of smart cities