In recent years, the diffusion of GPS-equipped devices has resulted in the generation of vast amounts of spatio-temporal data. This data represents a fundamental resource to conduct analysis on transportation networks. It is therefore of great interest to identify models capable of distinguishing and classifying trajectories to facilitate decision-making processes. For example, in traffic management, trajectory classification may assist in differentiating between different types of transit modes, aiding in congestion prediction and emissions monitoring. However, many existing algorithms necessitate a complex feature engineering process and domain knowledge. In this context, this thesis proposes a neural network-based approach, which eliminates the need for complicated hand-crafted features, using Gramian angular fields and leveraging possibly pre-trained convolutional neural networks.