DeepTrafficSense: A Dual-Perspective Framework for Road Travel Time Estimation

Road travel time estimation (RTTE) is crucial for route planning, especially in urban connected vehicle environments. However, current methods struggle to extract the latent traffic semantics embedded in trajectory data, limiting their ability to provide fine-grained travel time predictions for individual road segments compared to overall routes. In this study, we introduce DeepTrafficSense, a comprehensive RTTE framework that takes into account the heterogeneity and temporal variability of road segment conditions. It integrates a heterogeneous dynamic graph (HDG) and a trajectory motion transformer (TMT) to model macro traffic dynamics and micro vehicle motion patterns. The HDG captures the temporal evolution of traffic states across road segments using asynchronous memory updates and adaptive feature aggregation, effectively addressing challenges such as irregular trajectory sampling and stale memory states. The TMT, designed for variable-length multivariate sequences, employs self- and cross-attention mechanisms to fuse endogenous (e.g., speed) and exogenous (e.g., acceleration) motion features. Additionally, a type-aware gating mechanism dynamically adjusts the contributions of the HDG and TMT, enhancing prediction accuracy for both intersections and regular road segments. Experiments on large-scale datasets from Chengdu and Xi’an demonstrate that DeepTrafficSense significantly outperforms state-of-the-art models in RTTE tasks.