MMformer: Transformer-Based Trajectory Map-Matching Model for Large-Scale Road Networks

Numerous trajectory data mining algorithms rely on rich map information for improved effectiveness, necessitating essential trajectory preprocessing steps, such as map matching. Current deep models are restricted to small road networks and cannot adapt to or learn large-scale, variable, and noisy trajectories. From a data-driven perspective, we effectively applied and developed a transformer function and proposed a transformer-based map matching (MMformer) for large-scale road networks. This model does not require maps and trajectories to be gridded, and directly runs on the vectors of trajectory points. The decoder module can learn, gather, and store the connections of road segments; therefore, the outputs of the decoder have a graph-like inductive bias. Trajectory point vector, including coming direction, enhances encoder and decoder performance. After experimenting with different embedding modules, the trajectory point vectors were embedded using a single linear layer. Extensive experiments demonstrated that MMformer can perform the map-matching task for large-scale road networks, and its map-matching accuracy on large-scale road networks is 10% higher than that of existing models on small road networks.