基于字典学习的可解释轨迹表示
Explainable Trajectory Representation through Dictionary Learning
AI总结：
针对现有轨迹表示缺乏可解释性、存储分析效率低的问题，提出基于字典学习的可解释轨迹表示框架，提取路径元构建稀疏多尺度表示，经理论分析与真实数据集验证，在重构效果及下游任务中表现更优。
摘要：
路网下的轨迹表示学习能够深化我们对车辆交通模式的理解，为众多下游应用提供助力。现有采用经典机器学习或深度学习的方法将轨迹嵌入为稠密向量，这类表示缺乏可解释性，且在下游任务中存储与分析效率较低。本文提出了一种基于字典学习的可解释轨迹表示学习框架。针对路网上的轨迹集合，该框架提取出由常用子路径构成的紧凑型字典，这些子路径被称为“pathlets（路径元）”，可通过简单拼接最优重构每条轨迹。生成的表示天然具备稀疏性，且承载了强空间语义信息。本文对所提算法开展了理论分析，给出了最优字典估计误差的概率界。论文还提出了分层字典学习方案，以保障算法在大型路网上的可扩展性，最终得到多尺度的轨迹表示。研究在两个大规模真实出租车数据集上对该框架进行了评估。与已有工作相比，本文方法学习到的字典更紧凑，对新轨迹的重构率更优。研究还验证了该方法在行程时间预测、数据压缩等下游任务中的良好性能。
英文摘要：
Trajectory representation learning on a network enhances our understanding of vehicular traffic patterns and benefits numerous downstream applications. Existing approaches using classic machine learning or deep learning embed trajectories as dense vectors, which lack interpretability and are inefficient to store and analyze in downstream tasks. In this paper, an explainable trajectory representation learning framework through dictionary learning is proposed. Given a collection of trajectories on a network, it extracts a compact dictionary of commonly used subpaths called "pathlets", which optimally reconstruct each trajectory by simple concatenations. The resulting representation is naturally sparse and encodes strong spatial semantics. Theoretical analysis of our proposed algorithm is conducted to provide a probabilistic bound on the estimation error of the optimal dictionary. A hierarchical dictionary learning scheme is also proposed to ensure the algorithm's scalability on large networks, leading to a multi-scale trajectory representation. Our framework is evaluated on two large-scale real-world taxi datasets. Compared to previous work, the dictionary learned by our method is more compact and has better reconstruction rate for new trajectories. We also demonstrate the promising performance of this method in downstream tasks including trip time prediction task and data compression.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
