Semantic-Enhanced Representation Learning for Road Networks with Temporal Dynamics

arXiv:2403.11495v1 Announce Type: new
Abstract: In this study, we introduce a novel framework called Toast for learning general-purpose representations of road networks, along with its advanced counterpart DyToast, designed to enhance the integration of temporal dynamics to boost the performance of various time-sensitive downstream tasks. Specifically, we propose to encode two pivotal semantic characteristics intrinsic to road networks: traffic patterns and traveling semantics. To achieve this, we refine the skip-gram module by incorporating auxiliary objectives aimed at predicting the traffic …

abstract advanced arxiv boost cs.ai cs.lg dynamics encode framework general integration networks novel performance representation representation learning semantic study tasks temporal type