发表机构
The Pennsylvania State University（宾夕法尼亚州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对现有路径表示学习未考虑出行者行为与区域相关性的问题，提出TraveL框架，结合区域注意力与K-S检验，在多数据集上的出行时间分布估计等任务中优于现有最优方法。
AI 中文摘要
道路网络的路径表示学习（PRL）因各类与路径相关的应用而受到越来越多的研究关注。现有的PRL工作通常利用道路路段与路径之间的共现关系来学习一个向量作为路径表示，却未探索不同的出行者行为以及路径上的区域相关性。在本研究中，我们提出学习分布表示，这类表示可通过捕捉不同的出行者行为以及道路路段区域内的各类依赖关系，为路径相关应用提供有价值的信息。我们提出了一种新颖的基于Transformer的多视图分布表示学习（TraveL）框架，用于将一条路径与出行开始时间编码为分布表示，该表示可用于解码路径上出行者行为的可能样本。此外，通过分析揭示各类道路路段关系的区域相关性，我们提出了一种区域注意力机制，以在路径中编码这些相关性。同时，我们探索了柯尔莫哥洛夫-斯米尔诺夫（K-S）检验的思路，将采样得到的出行者行为与收集到的真实值进行比较，以促进模型训练。实验结果表明，所提出的TraveL模型在合成数据集和真实世界数据集上均优于现有最优方法：在出行时间分布估计的平均K-S距离上提升了14.7%，在路径相似度预测的平均绝对误差（MAE）上降低了16.7%，在目的地预测的MAE上降低了3.97%。
英文摘要
Path representation learning (PRL) for road networks has received increasing research attention, due to various path-related applications. Existing works on PRL typically exploit the co-occurrence relationship among road segments and paths to learn a vector as the path representation, without exploring the varied traveler behaviors and the regional correlation on the path. In this work, we propose to learn distributional representations, which provide valuable information for use in path-related applications, by capturing the varied traveler behaviors as well as the various dependencies within regions of road segments. We propose a novel Transformer-based Multi-view Distributional Representation Learning (TraveL) framework to encode a path along with a travel starting time to a distributional representation, which can be used to decode possible samples of on-path traveler behavior. Moreover, by analyzing the regional correlation which reveals various road segment relationships, we propose a regional attention to encode these correlations in a path. Also, we explore the idea of Kolmogorov-Smirnov (K-S) test to compare the sampled traveler behavior against the collected ground truth to facilitate training. Experimental results show that the proposed TraveL model outperforms the state-of-the-art methods on both synthetic and real-world datasets, by 14.7% in Mean K-S distance for travel time distribution estimation, 16.7% in Mean Absolute Error (MAE) for path similarity prediction, and 3.97% in MAE for destination prediction.
Comments10 pages
