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RNE: computing shortest paths using road network embedding

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Abstract

Computing the shortest paths and shortest path distances between two vertices on road networks is a core operation in many real-world applications, e.g., finding the closest taxi/hotel. However, existing techniques have several limitations. First, traditional Dijkstra-based methods have long latency and cannot meet the high-performance requirement. Second, existing indexing-based methods either involve huge index sizes or have poor performance. To address these limitations, in this paper we propose a learning-based method RNE which can efficiently compute an approximate shortest-path distance such that (1) the performance is super fast, e.g., taking 60–150 nanoseconds; (2) the error ratio of the approximate results is super small, e.g., below 0.7%; (3) scales well to large road networks, e.g., millions of nodes. The key idea is to first embed the road networks into a low dimensional space for capturing the distance relations between vertices, get an embedded vector for each vertex, and then perform a distance metric (\(L_1\) metric) on the embedded vectors to approximate shortest-path distances. We propose a hierarchical model to represent the embedding, and design an effective method to train the model. We also design a fine-tuning method to judiciously select high-quality training data. In order to identify the shortest path between two vertices (not just the distance), we extend the vertex embedding from RNE and design the RNE+ model, which can output the approximate shortest path with low error and high efficiency. We also propose effective techniques to accelerate the training process of RNE+, including embedding pre-training, negative sampling and model fine-tuning. Extensive experiments on real-world datasets show that RNE and RNE+ significantly outperform the state-of-the-art methods.

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Acknowledgements

This work is supported by NSF of China (61925205, 61632016, 62102215), Huawei, TAL education, China National Postdoctoral Program for Innovative Talents (BX2021155), China Postdoctoral Science Foundation (2021M691784), and Zhejiang Lab’s International Talent Fund for Young Professionals.

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Authors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Tianyu Zhao, Shuai Huang, Yong Wang, Chengliang Chai & Guoliang Li

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  1. Tianyu Zhao
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  2. Shuai Huang
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  3. Yong Wang
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  4. Chengliang Chai
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  5. Guoliang Li
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Correspondence to Guoliang Li.

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Zhao, T., Huang, S., Wang, Y. et al. RNE: computing shortest paths using road network embedding. The VLDB Journal 31, 507–528 (2022). https://doi.org/10.1007/s00778-021-00705-1

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