Current Challenges and Issues in Car Traffic Forecasting

Many methods have been proposed for predicting vehicular traffic using various machine learning approaches that are highly dependent on the available traffic data. However, achieving accurate and scalable traffic forecasting remains a major challenge due to dynamic traffic conditions, data complexity, and variations in transportation patterns. In this work, we present a data-driven traffic forecasting methodology that utilizes Passive Aggressive Classifier, Stochastic Gradient Descent (SGD) Classifier, Extra Trees Classifier, and Gradient Boosting Machine (GBM) to effectively analyze and learn traffic patterns from historical transportation data, improving prediction accuracy and consistency. The proposed system classifies traffic conditions into three categories: Low, Medium, and High traffic levels. By utilizing significant traffic-related features, the model provides reliable and efficient predictions for real-time traffic analysis and supports better understanding of traffic behavior for transportation planning and traffic management. Additionally, the system focuses on enhancing model performance, reducing unnecessary data complexity, minimizing overfitting, and improving generalization capability on unseen traffic data. The proposed framework can efficiently handle large-scale traffic datasets, ensuring scalability and stable performance, ultimately supporting intelligent decision-making in modern car traffic forecasting systems.

Keywords: Car Traffic Forecasting, Passive Aggressive Classifier, SGD Classifier, Extra Trees Classifier, Gradient Boosting Machine, Traffic Prediction, Machine Learning, Traffic Classification, Data Analysis.