Urban Transportation Data Analysis in Bangkok Using Modern Machine Learning

Urban transportation systems in megacities like Bangkok face mounting pressure due to population growth, limited infrastructure, and increasing vehicle usage. In this study, we explore the application of modern machine learning (ML) techniques—including Long Short-Term Memory (LSTM) networks, Graph Neural Networks (GNNs), and Transformer-based models to analyze and predict urban traffic patterns in Bangkok. Using real-world data from traffic sensors, GPS traces, and public transportation records, we construct a data-driven forecasting pipeline that captures complex spatial-temporal dependencies. Our results show that GNN-based models significantly outperform traditional baselines and even LSTM in key metrics, while Transformer models offer complementary advantages. We also discuss the potential for federated learning to support privacy-preserving traffic analytics in Bangkok's decentralized urban infrastructure. This study contributes a scalable, high-accuracy framework for smart urban mobility planning and real-time traffic prediction in Southeast Asian cities.

کلیدواژه ها:

Urban transportation ، machine learning ، traffic prediction ، graph neural networks ، federated learning ، transformer models