AttentionTTE: a deep learning model for estimated time of arrival

• DOI: 10.3389/frai.2024.1258086
• Publication Date: 2024-08-23T13:38:08Z
• Authors: Mu Li; Yijun Feng; Xiangdong Wu
• Keywords: self-attention; Artificial Intelligence; Electronic computers. Computer science; 0502 economics and business; transformer; 0202 electrical engineering, electronic engineering, information engineering; TTE; deep learning; time serial data; QA75.5-76.95

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.