{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,22]],"date-time":"2025-11-22T11:38:45Z","timestamp":1763811525945,"version":"3.41.2"},"reference-count":40,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T00:00:00Z","timestamp":1724371200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>Estimating travel time (ETA) for arbitrary paths is crucial in urban intelligent transportation systems. Previous studies primarily focus on constructing complex feature systems for individual road segments or sub-segments, which fail to effectively model the influence of each road segment on others. To address this issue, we propose an end-to-end model, AttentionTTE. It utilizes a self-attention mechanism to capture global spatial correlations and a recurrent neural network to capture temporal dependencies from local spatial correlations. Additionally, a multi-task learning module integrates global spatial correlations and temporal dependencies to estimate the travel time for both the entire path and each local path. We evaluate our model on a large trajectory dataset, and extensive experimental results demonstrate that AttentionTTE achieves state-of-the-art performance compared to other methods.<\/jats:p>","DOI":"10.3389\/frai.2024.1258086","type":"journal-article","created":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T13:38:08Z","timestamp":1724420288000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["AttentionTTE: a deep learning model for estimated time of arrival"],"prefix":"10.3389","volume":"7","author":[{"given":"Mu","family":"Li","sequence":"first","affiliation":[]},{"given":"Yijun","family":"Feng","sequence":"additional","affiliation":[]},{"given":"Xiangdong","family":"Wu","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2024,8,23]]},"reference":[{"key":"B1","doi-asserted-by":"crossref","DOI":"10.1109\/ICSMC.2006.385244","article-title":"\u201cApplication of the ARIMA models to urban roadway travel time prediction-a case study,\u201d","volume-title":"2006 IEEE International Conference on Systems, Man and Cybernetics. 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