{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/gct-tte-graph-convolutional-transformer-for","title":"GCT-TTE: Graph Convolutional Transformer for Travel Time Estimation","arxiv_id":"2306.04324","date":"2023-06-07","proceeding":null,"authors":["Vladimir Mashurov","Vaagn Chopurian","Vadim Porvatov","Arseny Ivanov","Natalia Semenova"],"abstract":"This paper introduces a new transformer-based model for the problem of travel time estimation. The key feature of the proposed GCT-TTE architecture is the utilization of different data modalities capturing different properties of an input path. Along with the extensive study regarding the model configuration, we implemented and evaluated a sufficient number of actual baselines for path-aware and path-blind settings. The conducted computational experiments have confirmed the viability of our pipeline, which outperformed state-of-the-art models on both considered datasets. Additionally, GCT-TTE was deployed as a web service accessible for further experiments with user-defined routes.","url_abs":"https://arxiv.org/abs/2306.04324v2","url_pdf":"https://arxiv.org/pdf/2306.04324v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"gct-tte-graph-convolutional-transformer-for","repo_url":"https://github.com/eighonet/gct-tte","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"travel-time-estimation","task_name":"Travel Time Estimation"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[{"method_slug":null,"method_name":null},{"method_slug":null,"method_name":"Travel"}],"datasets_introduced":[{"slug":"tte-a-o","name":"TTE-A&O","full_name":"Travel Time Estimation: Abakan and Omsk"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/travel-time-estimation-on-tte-a-o","task":"Travel Time Estimation","dataset":"TTE-A&O","model":"GCT-TTE","rank_in_archive_order":1,"of":6,"metrics":{"Root mean square error (RMSE)":"147.89","mean absolute error":"92.26"},"uses_additional_data":false},{"leaderboard":"/sota/travel-time-estimation-on-tte-a-o","task":"Travel Time Estimation","dataset":"TTE-A&O","model":"DeepTTE","rank_in_archive_order":3,"of":6,"metrics":{"Root mean square error (RMSE)":"174.56","mean absolute error":"111.03"},"uses_additional_data":false},{"leaderboard":"/sota/travel-time-estimation-on-tte-a-o","task":"Travel Time Estimation","dataset":"TTE-A&O","model":"WDR","rank_in_archive_order":4,"of":6,"metrics":{"Root mean square error (RMSE)":"190.09","mean absolute error":"97.22"},"uses_additional_data":false},{"leaderboard":"/sota/travel-time-estimation-on-tte-a-o","task":"Travel Time Estimation","dataset":"TTE-A&O","model":"DeepI2T","rank_in_archive_order":5,"of":6,"metrics":{"Root mean square error (RMSE)":"201.33","mean absolute error":"97.99"},"uses_additional_data":false},{"leaderboard":"/sota/travel-time-estimation-on-tte-a-o","task":"Travel Time Estimation","dataset":"TTE-A&O","model":"DeepIST","rank_in_archive_order":6,"of":6,"metrics":{"Root mean square error (RMSE)":"241.29","mean absolute error":"153.88"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}