Unraveling Urban Mobility: Gramian Angular Fields for Trajectory Classification

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. Therefore, we combine these tools to tackle the challenge of multiclass trajectory classification. We demonstrate the effectiveness of our method on an imbalanced dataset simulated with SUMO by classifying different means of transportation – private car, taxi, bus, pedestrian, motorcycle, bicycle – achieving good results in terms of accuracy and F1 score. Our approach is indeed a viable way to harness the power of convolutional neural networks for the task of trajectory classification.