Road network representation learning: a dual graph-based approach

Road network is a critical infrastructure powering many applications including transportation, mobility and logistics in real life. To leverage the input of a road network across these different applications, it is necessary to learn the representations of the roads in the form of vectors, which is...

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Main Authors: Zhang, Liang, Long, Cheng
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2023
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Online Access:https://hdl.handle.net/10356/170906
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1709062023-10-06T15:36:05Z Road network representation learning: a dual graph-based approach Zhang, Liang Long, Cheng School of Computer Science and Engineering Engineering::Computer science and engineering Road Network Graph Neural Network Road network is a critical infrastructure powering many applications including transportation, mobility and logistics in real life. To leverage the input of a road network across these different applications, it is necessary to learn the representations of the roads in the form of vectors, which is named road network representation learning (RNRL). While several models have been proposed for RNRL, they capture the pairwise relationships/connections among roads only (i.e., as a simple graph), and fail to capture among roads the high-order relationships (e.g., those roads that jointly form a local region usually have similar features such as speed limit) and long-range relationships (e.g., some roads that are far apart may have similar semantics such as being roads in residential areas). Motivated by this, we propose to construct a hypergraph, where each hyperedge corresponds to a set of multiple roads forming a region. The constructed hypergraph would naturally capture the high-order relationships among roads with hyperedges. We then allow information propagation via both the edges in the simple graph and the hyperedges in the hypergraph in a graph neural network context. In addition, we introduce different pretext tasks based on both the simple graph (i.e., graph reconstruction) and the hypergraph (including hypergraph reconstruction and hyperedge classification) for optimizing the representations of roads. The graph reconstruction and hypergraph reconstruction tasks are conventional ones and can capture structural information. The hyperedge classification task can capture long-range relationships between pairs of roads that belong to hyperedges with the same label. We call the resulting model HyperRoad. We further extend HyperRoad to problem settings when additional inputs of road attributes and/or trajectories that are generated on the roads are available. We conduct extensive experiments on two real datasets, for five downstream tasks, and under four problem settings, which demonstrate that our model achieves impressive improvements compared with existing baselines across datasets, tasks, problem settings, and performance metrics.CCS Concepts: • Information systems → Data mining; • Urban computing; • Spatial-temporal systems; Ministry of Education (MOE) Submitted/Accepted version This study is supported under the RIE2020 Industry Alignment Fund Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in kind contribution from Singapore Telecommunications Limited Singtel, through Singtel Cognitive, and Artificial Intelligence Lab for Enterprises (SCALE@NTU). This research is also supported by the Ministry of Education, Singapore, under its Academic Research Fund (Tier 2 Award MOE-T2EP20221-0013). 2023-10-06T05:45:29Z 2023-10-06T05:45:29Z 2023 Journal Article Zhang, L. & Long, C. (2023). Road network representation learning: a dual graph-based approach. ACM Transactions On Knowledge Discovery From Data, 17(9), 121-. https://dx.doi.org/10.1145/3592859 1556-4681 https://hdl.handle.net/10356/170906 10.1145/3592859 2-s2.0-85168799426 9 17 121 en MOE-T2EP20221-0013 ACM Transactions on Knowledge Discovery from Data © 2023 The owner/author(s). Publication rights licensed to ACM. All rights reserved. This article may be downloaded for personal use only. Any other use requires prior permission of the copyright holder. The Version of Record is available online at http://doi.org/10.1145/3592859. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering
Road Network
Graph Neural Network
spellingShingle Engineering::Computer science and engineering
Road Network
Graph Neural Network
Zhang, Liang
Long, Cheng
Road network representation learning: a dual graph-based approach
description Road network is a critical infrastructure powering many applications including transportation, mobility and logistics in real life. To leverage the input of a road network across these different applications, it is necessary to learn the representations of the roads in the form of vectors, which is named road network representation learning (RNRL). While several models have been proposed for RNRL, they capture the pairwise relationships/connections among roads only (i.e., as a simple graph), and fail to capture among roads the high-order relationships (e.g., those roads that jointly form a local region usually have similar features such as speed limit) and long-range relationships (e.g., some roads that are far apart may have similar semantics such as being roads in residential areas). Motivated by this, we propose to construct a hypergraph, where each hyperedge corresponds to a set of multiple roads forming a region. The constructed hypergraph would naturally capture the high-order relationships among roads with hyperedges. We then allow information propagation via both the edges in the simple graph and the hyperedges in the hypergraph in a graph neural network context. In addition, we introduce different pretext tasks based on both the simple graph (i.e., graph reconstruction) and the hypergraph (including hypergraph reconstruction and hyperedge classification) for optimizing the representations of roads. The graph reconstruction and hypergraph reconstruction tasks are conventional ones and can capture structural information. The hyperedge classification task can capture long-range relationships between pairs of roads that belong to hyperedges with the same label. We call the resulting model HyperRoad. We further extend HyperRoad to problem settings when additional inputs of road attributes and/or trajectories that are generated on the roads are available. We conduct extensive experiments on two real datasets, for five downstream tasks, and under four problem settings, which demonstrate that our model achieves impressive improvements compared with existing baselines across datasets, tasks, problem settings, and performance metrics.CCS Concepts: • Information systems → Data mining; • Urban computing; • Spatial-temporal systems;
author2 School of Computer Science and Engineering
author_facet School of Computer Science and Engineering
Zhang, Liang
Long, Cheng
format Article
author Zhang, Liang
Long, Cheng
author_sort Zhang, Liang
title Road network representation learning: a dual graph-based approach
title_short Road network representation learning: a dual graph-based approach
title_full Road network representation learning: a dual graph-based approach
title_fullStr Road network representation learning: a dual graph-based approach
title_full_unstemmed Road network representation learning: a dual graph-based approach
title_sort road network representation learning: a dual graph-based approach
publishDate 2023
url https://hdl.handle.net/10356/170906
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