AttentionTTE:估计到达时间的深度学习模型。

IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Frontiers in Artificial Intelligence Pub Date : 2024-08-23 eCollection Date: 2024-01-01 DOI:10.3389/frai.2024.1258086
Mu Li, Yijun Feng, Xiangdong Wu
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引用次数: 0

摘要

在城市智能交通系统中,估算任意路径的旅行时间(ETA)至关重要。以往的研究主要集中在为单个路段或子路段构建复杂的特征系统,而这些系统无法有效地模拟每个路段对其他路段的影响。为解决这一问题,我们提出了一种端到端模型--AttentionTTE。它利用自我注意机制捕捉全局空间相关性,并利用递归神经网络捕捉局部空间相关性的时间依赖性。此外,多任务学习模块整合了全局空间相关性和时间相关性,以估算整个路径和每个局部路径的旅行时间。我们在一个大型轨迹数据集上对我们的模型进行了评估,大量实验结果表明,与其他方法相比,AttentionTTE 实现了最先进的性能。
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AttentionTTE: a deep learning model for estimated time of arrival.

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.

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来源期刊
CiteScore
6.10
自引率
2.50%
发文量
272
审稿时长
13 weeks
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