View article

Estimating the time of arrival is a crucial task in intelligent transportation systems. The task poses challenges due to the dynamic nature and complex spatio-temporal dependencies of traffic networks. Existing studies have primarily focused on learning the dependencies between adjacent links on a route, often overlooking a deeper understanding of the links within the traffic network. To address this limitation, we propose DeepLink, a novel approach for travel time estimation that leverages a comprehensive understanding of the spatio-temporal dynamics of road segments from different perspectives. DeepLink introduces triplet embedding, enabling the learning of both the topology and potential semantics of the traffic network, leading to an improved understanding of links’ static information. Then, a spatio-temporal dynamic representation learning module integrates the triplet embedding and real-time information …