Title | ||
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Multi-scale Graph Convolutional Network for Intersection Detection from GPS Trajectories. |
Abstract | ||
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To facilitate the reconstruction of high-quality road networks, intersections as the key locations provide valuable information about the network topology. However, only a few efforts have been made on the data-driven automatic detection of intersections from, e.g., large-scale GPS trajectories. To bridge the gap, we propose a machine learning based intersection detection approach based on large-scale real-world GPS trajectories of drivers from the Grab ride-hailing service. Instead of representing locations with vector descriptors, we innovatively propose a graph representation that models a location together with its local surroundings to improve the descriptiveness of the location descriptors. Moreover, we present a multi-scale graph convolutional network (GCN) to generate robust graph-level descriptors, followed by logistic regression to discriminate intersections from non-intersections. The experimental results show that our proposed multi-scale graph model outperforms the conventional multi-scale vector representation by 8.5%. Appealingly, the proposed graph representation can be considered as a general location descriptor, which can be used in a variety of geo-based applications other than intersection detection for location modeling.
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Year | DOI | Venue |
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2019 | 10.1145/3356471.3365234 | GeoAI@SIGSPATIAL |
Field | DocType | ISBN |
Graph,Computer science,Algorithm,Global Positioning System | Conference | 978-1-4503-6957-2 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
9 |
Name | Order | Citations | PageRank |
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Yifang Yin | 1 | 80 | 16.61 |
Abhinav Sunderrajan | 2 | 0 | 0.34 |
Xiaocheng Huang | 3 | 1 | 1.71 |
Jagannadan Varadarajan | 4 | 176 | 11.47 |
Guanfeng Wang | 5 | 122 | 11.52 |
Dhruva Sahrawat | 6 | 2 | 2.06 |
Ying Zhang | 7 | 625 | 94.27 |
Roger Zimmermann | 8 | 14 | 4.18 |
See-Kiong Ng | 9 | 1 | 0.72 |