Cross-View Location Alignment Enhanced Spatial-Topological Aware Dual Transformer for Travel Time Estimation

Abstract

Accurately estimating route travel time is crucial for intelligent transportation systems. Urban road networks and routes can be viewed from spatial and topological perspectives while existing works typically focus on one view and disregard important information from the other perspective. In this paper, we propose TTEFORMER, a novel travel time estimation model. It incorporates an alignment-enhanced spatial-topological aware dual transformer model to adaptively incorporate intra- and inter-view features in the route, guided by cross-view location alignment matrices with clear correspondences between locations in two views. Additionally, we propose a sparsity-aware dual-view traffic feature extraction module to effectively capture temporal traffic state changes. Compared to baseline models, TTEFORMER demonstrates improved performance on the MAPE and MAE metrics for Chengdu and Shanghai datasets, achieving improvements of 8.32%, 7.03%, 8.06% and 9.51% respectively, validating the effectiveness of TTEFORMER in travel time estimation.