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STTG-TTE: spatial–temporal gated multi-modality approach for travel time estimation based on temporal convolutional networks

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Abstract

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.

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Data availability

Chengdu Dataset is publicly available from Didi Chuxing in China at the link: https://outreach.didichuxing.com/. NYC-Bike Dataset is also publicly available from Lyft ride-sharing company in the USA at https://s3.amazonaws.com/tripdata/index.html.

Notes

  1. https://outreach.didichuxing.com/.

  2. https://s3.amazonaws.com/tripdata/index.html.

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Acknowledgement

This work is supported in part by the National Key Research and Development Program of China (no. 2021ZD0112400), and also in part by the Innovation Foundation of Science and Technology of Dalian under Grant 2022JJ12SN052.

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  1. School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China

    Alfateh M. Tag Elsir, Alkilane Khaled & Yanming Shen

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Tag Elsir, A.M., Khaled, A. & Shen, Y. STTG-TTE: spatial–temporal gated multi-modality approach for travel time estimation based on temporal convolutional networks. Neural Comput & Applic 35, 5535–5551 (2023). https://doi.org/10.1007/s00521-022-07977-w

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