Traffic prediction is essential for modern urban planning and traffic management, influencing both daily commutes and long-term infrastructure development. This dissertation investigates the use of machine learning algorithms to predict traffic patterns, utilizing two distinct datasets for classification and forecasting. For the classification task, we employ the Traffic Prediction Dataset from Kaggle, using Decision Tree, XGBoost classifier, CNN, LSTM and a Stacking classifier. These models classify traffic conditions with high accuracy, identifying patterns and anomalies in traffic flow. For forecasting the number of vehicles, we use another Kaggle dataset, implementing ARIMA, ARIMAX, SARIMA, and SARIMAX models. These time series models predict vehicle counts with precision, accounting for seasonal and exogenous factors. Our findings reveal that machine learning techniques significantly enhance traffic prediction capabilities, offering valuable insights for reducing congestion and optimizing traffic flow. The classification models provide real-time traffic condition assessments, while the forecasting models aid in long-term traffic planning and infrastructure development. This study underscores the potential of machine learning in transforming traffic management, highlighting its applicability in urban planning, policy-making, and daily traffic operations. The integration of advanced algorithms into traffic prediction systems can lead to more efficient and sustainable urban environments, ultimately improving the quality of life for commuters and residents. Our work contributes to the growing field of intelligent transportation systems, showcasing the effectiveness of machine learning in addressing complex traffic challenges.

Keywords: Traffic prediction, machine learning, urban planning, traffic management, classification, forecasting, Decision Tree, XGBoost, CNN, LSTM, Stacking classifier, ARIMA, ARIMAX, SARIMA, SARIMAX, congestion reduction, traffic flow optimization, intelligent transportation systems.