RoPT: Route-Planning Model with Transformer

Abstract

With the increasing aggravations of urban traffic jams, intelligent route planning to reduce traffic time is becoming increasingly critical for drivers. However, traditional route-planning methods, such as graph search and Recurrent Neural Network (RNN)-based methods, struggle to capture the complex dynamics of road networks. Specifically in A*-type methods, routes should be searched instantly on the whole graph for the sake of dynamic changes in edge time consumption. As for RNN-based methods, their shortcomings in capturing long-distance sequence dependencies makes them unsuitable for route planning in metropolises with long routes. Therefore, to better adapt to the complexity of urban traffic, in this paper, an innovative route-planning model called Route Planning with Transformer (RoPT) is proposed. This model is based on the fusion of Graph Convolutional Networks (GCNs) and a Transformer, which uses GCNs for capturing complex spatial dependencies between the current intersection and the destination in a road network. Depending on the self-attention mechanism of the Transformer, the long-distance temporal dependencies between intersections could also be captured effectively. With comprehensive experiments on two real-world traffic datasets, the Porto dataset and the Chengdu dataset, it is demonstrated that RoPT outperforms the best methods, to the best of our knowledge. Moreover, the latent features learned from RoPT are more interpretable.

Keywords

• route planning
• transformer
• traffic
• spatial–temporal data
• GCN
• attention mechanism