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Learning Embeddings of Intersections on Road Networks

Authors:

Meng-xiang Wang,

Wang-Chien Lee,

Ge YuAuthors Info & Claims

SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Pages 309 - 318

https://doi.org/10.1145/3347146.3359075

Published: 05 November 2019 Publication History

Abstract

Road network is a basic component of intelligent transportation systems (ITS) in smart city. Informative representation of road networks is important as it is essential to a wide variety of ITS applications. In this paper, we propose a neural network representation learning model, namely Intersection of Road Network to Vector (IRN2Vec), to learn embeddings of road intersections that encode rich information in a road network by exploring geo-locality and intrinsic properties of intersections and moving behaviors of road users. In addition to model design, several issues unique to IRN2Vec, including data preparation for model training and various relationships among intersections, are examined. We evaluate the learned embeddings via extensive experiments on three real-world datasets using three downstream test cases, including prediction of traffic signals and crossings on intersections and travel time estimation. Experimental results show that the proposed IRN2Vec outperforms three existing methods, DeepWalk, LINE and Node2vec, in terms of F1-score in predicting traffic signals (22.21% to 23.84%) and crossings (8.65% to 11.65%), and mean absolute error (MAE) in travel time estimation (9.87% to 19.28%).

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Cited By

Wang MLee WLiu NFu QWan FYu G(2025)A Data-Driven Deep Learning Framework for Prediction of Traffic Crashes at Road IntersectionsApplied Sciences10.3390/app1502075215:2(752)Online publication date: 14-Jan-2025
https://doi.org/10.3390/app15020752
Chen RLei JYao HLi TLi S(2025)Anchor-Enhanced Geographical Entity Representation LearningIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.332982236:1(924-938)Online publication date: Jan-2025
https://doi.org/10.1109/TNNLS.2023.3329822
Lee WLauw H(2024)Latent Representation Learning for Geospatial EntitiesACM Transactions on Spatial Algorithms and Systems10.1145/366347410:4(1-31)Online publication date: 2-May-2024
https://dl.acm.org/doi/10.1145/3663474
Show More Cited By

Index Terms

Learning Embeddings of Intersections on Road Networks
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Learning latent representations
      2. Neural networks
  2. Modeling and simulation
    1. Model development and analysis

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cover image ACM Conferences

SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2019

648 pages

ISBN:9781450369091

DOI:10.1145/3347146

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Published: 05 November 2019

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SIGSPATIAL '19

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SIGSPATIAL

SIGSPATIAL '19: 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 5 - 8, 2019

IL, Chicago, USA

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SIGSPATIAL '19 Paper Acceptance Rate 34 of 161 submissions, 21%;

Overall Acceptance Rate 257 of 1,238 submissions, 21%

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Cited By

Wang MLee WLiu NFu QWan FYu G(2025)A Data-Driven Deep Learning Framework for Prediction of Traffic Crashes at Road IntersectionsApplied Sciences10.3390/app1502075215:2(752)Online publication date: 14-Jan-2025
https://doi.org/10.3390/app15020752
Chen RLei JYao HLi TLi S(2025)Anchor-Enhanced Geographical Entity Representation LearningIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.332982236:1(924-938)Online publication date: Jan-2025
https://doi.org/10.1109/TNNLS.2023.3329822
Lee WLauw H(2024)Latent Representation Learning for Geospatial EntitiesACM Transactions on Spatial Algorithms and Systems10.1145/366347410:4(1-31)Online publication date: 2-May-2024
https://dl.acm.org/doi/10.1145/3663474
Lin YWan HGuo SHu JJensen CLin Y(2024)Pre-Training General Trajectory Embeddings With Maximum Multi-View Entropy CodingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.334751336:12(9037-9050)Online publication date: Dec-2024
https://doi.org/10.1109/TKDE.2023.3347513
Zhang YWang PWang BWang XZhao ZZhou ZBai LWang Y(2024)Adaptive and Interactive Multi-Level Spatio-Temporal Network for Traffic ForecastingIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.339297525:10(14070-14086)Online publication date: Oct-2024
https://doi.org/10.1109/TITS.2024.3392975
Huang HHu ZWang YLu ZWen X(2024)Intersec2vec-TSC: Intersection Representation Learning for Large-Scale Traffic Signal ControlIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.334015325:7(7044-7056)Online publication date: Jul-2024
https://doi.org/10.1109/TITS.2023.3340153
Zhang HChen YZhang XJiang QLi LZheng BSun W(2024)Modeling Route Representation With Mixed-Scale Hierarchical TransformerICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10446095(5295-5299)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10446095
Wu LTang YZhang PZhou Y(2023)Spatio-Temporal Heterogeneous Graph Neural Networks for Estimating Time of TravelElectronics10.3390/electronics1206129312:6(1293)Online publication date: 8-Mar-2023
https://doi.org/10.3390/electronics12061293
Jin GYan HLi FHuang JLi Y(2023)Spatio-temporal Dual Graph Neural Networks for Travel Time EstimationACM Transactions on Spatial Algorithms and Systems10.1145/362781910:3(1-22)Online publication date: 28-Oct-2023
https://dl.acm.org/doi/10.1145/3627819
Zhang LLong C(2023)Road Network Representation Learning: A Dual Graph-based ApproachACM Transactions on Knowledge Discovery from Data10.1145/359285917:9(1-25)Online publication date: 15-Jun-2023
https://dl.acm.org/doi/10.1145/3592859
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