Rahul Biju supervised by Dr. Deepak Gangadharan  received his Master of Science  in Computer Science and Engineering (CSE). Here’s a summary of his research work on Prediction and Generation Models for Traffic Flow Forecasting:

Traffic flow prediction is a critical component of intelligent transportation systems, with direct implications for congestion mitigation, safety enhancement, and efficient travel planning. Given the complex spatio-temporal nature of traffic data, a wide range of hybrid deep learning models—such as CNN-LSTM, ConvLSTM, and Temporal Convolutional Networks (TCNs)—have been explored to capture spatial, temporal, and periodic dependencies effectively. In this work, we perform a comprehensive comparison of deep learning models with and without periodicity to evaluate their prediction accuracies. To address the challenge of hyperparameter tuning in these models, we propose a Genetic Algorithm (GA)-based optimization framework for CNN-LSTM, ConvLSTM, and a novel GA-TCN architecture, all of which demonstrate improved performance on benchmark datasets. Furthermore, we introduce a purely temporal deep learning model named Grid LSTM-based Attention Modelling for Traffic Flow Prediction(GLSTM-A), which leverages a combination of Grid LSTM for long-term dependencies, a standard LSTM for recent trends, and a custom attention mechanism to automatically prioritize significant temporal features. GLSTM-A exhibits superior prediction accuracy and memory efficiency compared to existing temporal models such as TCN, LSTM, and Bi-LSTM. Finally, to tackle the challenge posed by limited long-term traffic datasets, we develop TrafficFlowGAN—a GAN-based time-series data generation framework that effectively captures temporal patterns through a joint supervised and adversarial learning process. The synthetic data generated by TrafficFlowGAN closely resembles real-world traffic patterns, thereby enhancing the robustness and accuracy of downstream prediction models. Extensive experimental evaluations and ablation studies validate the efficiency of the proposed models across various traffic forecasting scenarios.

July 2025