Machine Learning-Based Traffic Flow Prediction Using Real-Time Data Analytics

Thesis Abstract

Urban traffic congestion remains a persistent challenge in many metropolitan areas, resulting in increased travel times, elevated emissions, and economic losses. Accurate prediction of traffic flow patterns is essential for effective traffic management and urban planning. Traditional models often lack the capability to adapt dynamically to real-time variations, thereby limiting their usefulness in making timely decisions. This study aims to develop and evaluate a machine learning framework for predicting traffic flow using real-time data analytics, enhancing the accuracy and responsiveness of traffic management systems. The primary objectives are to (1) identify relevant real-time data sources, including sensor data, GPS trajectories, and social media feeds; (2) design a hybrid machine learning model that integrates several algorithms such as Long Short-Term Memory (LSTM), Random Forest, and Support Vector Regression (SVR) for traffic flow prediction; (3) compare the performance of the proposed model against traditional statistical forecasting models, such as ARIMA; and (4) assess the model’s robustness and generalizability across different urban contexts. The research adopts a quantitative research design with a post-positivist paradigm, emphasizing empirical analysis and predictive validity. The study population comprises traffic sensors, GPS-enabled vehicle datasets, and social media feeds collected from a major city, involving a sample size of approximately 50,000 traffic data points accumulated over a period of six months. Data collection instruments include sensor networks installed along key arterial roads, GPS tracking devices embedded in taxis and rideshare vehicles, and APIs for social media platforms providing relevant geotagged mentions. Data preprocessing involves cleaning, normalization, feature extraction, and temporal aggregation to prepare the datasets for analysis. To ensure the validity and reliability of the instruments, pilot testing was conducted, and data quality checks were implemented throughout data collection. Analytical techniques involve the application of machine learning algorithms—namely LSTM recurrent neural networks, Random Forest classifiers, and Support Vector Regression models—using Python-based libraries such as TensorFlow and scikit-learn. The models are trained, validated, and tested through k-fold cross-validation and evaluated based on root mean square error (RMSE), mean absolute error (MAE), and R-squared metrics. Comparative analysis against ARIMA models is conducted to determine the superior predictive framework. Moreover, hyperparameter tuning and model optimization are performed to enhance prediction accuracy. Expected findings include that the proposed hybrid machine learning model outperforms traditional statistical methods, demonstrating lower error metrics and higher robustness in dynamic urban environments. It is anticipated that integrating multiple data sources enhances prediction precision, especially during peak hours and unexpected traffic disruptions. The study also expects to identify key features influencing traffic flow and discern temporal patterns pertinent to urban traffic dynamics. This research contributes significantly to the literature on intelligent transportation systems by demonstrating the feasibility and effectiveness of machine learning techniques in real-time traffic prediction. It advances existing knowledge by integrating heterogeneous data sources within a unified predictive framework and providing empirically validated models applicable to diverse urban settings. The findings are expected to inform policy formulation and operational strategies in traffic management agencies, fostering more adaptive and data-driven urban mobility solutions. In conclusion, this study recommends the adoption of integrated machine learning models for traffic prediction to improve the responsiveness of urban transportation systems. It further suggests avenues for future research, including the incorporation of emerging data sources such as connected vehicle data and the exploration of deep learning architectures for multi-modal transportation prediction. Such developments could significantly enhance the predictive capacity and operational efficiency of intelligent traffic control systems, ultimately contributing to more sustainable and efficient urban mobility.