Movement paths are used widely in intelligent transportation and smart city applications. To serve such applications, path representation learning aims to provide compact representations of paths that enable efficient and accurate operations when used for different downstream tasks such as path ranking and travel cost estimation. In many cases, it is attractive that the path representation learning is lightweight and scalable; in resource-limited environments and under green computing limitations, it is essential. Yet, existing path representation learning studies focus on accuracy and pay at most secondary attention to resource consumption and scalability. We propose a lightweight and scalable path representation learning framework, termed LightPath, that aims to reduce resource consumption and achieve scalability without affecting accuracy, thus enabling broader applicability. More specifically, we first propose a sparse auto-encoder that ensures that the framework achieves good scalability with respect to path length. Next, we propose a relational reasoning framework to enable faster training of more robust sparse path encoders. We also propose global-local knowledge distillation to further reduce the size and improve the performance of sparse path encoders. Finally, we report extensive experiments on two real-world datasets to offer insight into the efficiency, scalability, and effectiveness of the proposed framework.

翻译：运动路径广泛应用于智能交通和智慧城市应用。为支撑此类应用，路径表示学习旨在为路径提供紧凑的表示，使其在路径排序、出行成本估计等不同下游任务中实现高效且准确的操作。在许多情况下，路径表示学习需具备轻量化与可扩展性；在资源受限环境及绿色计算条件下，这更成为刚性需求。然而，现有路径表示学习研究主要关注准确性，对资源消耗和可扩展性至多给予次要关注。本文提出一个轻量级可扩展路径表示学习框架LightPath，旨在不牺牲准确性的前提下降低资源消耗并实现可扩展性，从而增强其普适性。具体而言，我们首先提出稀疏自编码器，确保框架在路径长度维度具有良好的可扩展性。其次提出关系推理框架，实现更鲁棒的稀疏路径编码器的高效训练。同时采用全局-局部知识蒸馏方法进一步减小稀疏路径编码器规模并提升性能。最后，我们在两个真实世界数据集上开展大量实验，深入揭示所提框架的效力、可扩展性与有效性。