Résumé

in recent years, traffic flow prediction has presented challenges in the management of transportation systems. It is a crucial part of Intelligent Transportation Systems (ITS). The complexities of various transportation data, spatial and temporal dependencies on road networks, and multimodalities, such as public transit, pedestrian flow, and bike sharing, make it a challenging task to forecast traffic flow accurately. Numerous works have been introduced to address these challenges, but few have simultaneously considered these factors, resulting in limited success. In this study, a model is proposed to integrate Graph Convolutional Networks (GCN) and Bidirectional Long Short-Term Memory (BiLSTM). This model utilizes the advantages of GCN in handling spatial data and capturing dependencies in road networks, combined with BiLSTM's capability in learning temporal dynamics. The proposed model can extract comprehensive features from various transportation data and effectively capture the spatial-temporal dependencies. By merging these features, it aims to generate more accurate and robust traffic flow predictions. This method addresses the limitations of existing methods that fail to consider spatial-temporal dependencies and multimodalities, leading to improved prediction accuracy and efficiency