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Travel time forecasting has become a core component of smart transportation systems, which assists both travelers and traffic organizers with route planning, travel schedule adjustments, ride-sharing, navigation applications, and efficient traffic management. However, timely and accurate travel time forecasting still remains a critical challenge owing to the complex nonlinear and dynamic fluctuations of spatial–temporal dependencies. Also, spatial sparseness is a big issue in traffic forecasting, since adopting the implicit interactions between the close traffic regions leads to superficial characterization of spatio-temporal dependences. In this paper, we propose a new deep learning-based framework (STTG-TTE) that addresses these drawbacks and improves the travel time estimation. First, we build a geo-hashing algorithm for the data sparsity issue that incorporates fluctuations of nearby and distant traffic situations in terms of spatio-temporal dependencies. Second, a new spatio-temporal correlation modeling method is proposed to fully leverage large-scale spatial and temporal traffic patterns using temporal convolutional networks integrated with a gated multi-modality mechanism. Then, for external factors' representation, a new dual-gated Res-Net multi-modality-based module is proposed. Finally, we fuse these representations of multi-components dynamically and utilize the transformer model, which is conducive to learning intersections among these multiple factors for obtaining accurate prediction results. Experiments on two large-scale real-world traffic datasets from two different urban regions (Chengdu taxi-datsets and NYC-Bike datasets) demonstrate that the proposed model is superior to state-of-the-art baseline models. [ABSTRACT FROM AUTHOR]