TY - GEN
T1 - PICT
T2 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023
AU - Wu, Wenyu
AU - Shen, Wenyi
AU - Mao, Jiali
AU - Zhao, Lisheng
AU - Cao, Shaosheng
AU - Zhou, Aoying
AU - Zhou, Lin
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - To recognize road intersections using cycling trajectories accurately is vital to the quality of the digital map that cycling navigation apps use. However, the existing approaches mainly identify road intersections based on motor vehicles’ trajectories, and they fail to tackle unique challenges posed by cycling trajectories: (i) Cycling trajectories of minor intersections and their adjacent road segments are quite sparse. (ii) Turning behaviors occur at different areas in intersections of various sizes. To address the above challenges, in this paper, we propose a precision-enhanced road intersection recognition method using cycling trajectories, called PICT. Initially, to enhance the representations of minor intersections, a grid topology representation module is designed to extract intersection topology. Then an intersection inference module based on multi-scale feature learning is put forward to identify the intersections of different scales correctly. Finally, extensive comparative experiments on two real-world datasets demonstrate that PICT significantly outperforms the state-of-the-art methods by 52.13% in the F1-score of intersection recognition.
AB - To recognize road intersections using cycling trajectories accurately is vital to the quality of the digital map that cycling navigation apps use. However, the existing approaches mainly identify road intersections based on motor vehicles’ trajectories, and they fail to tackle unique challenges posed by cycling trajectories: (i) Cycling trajectories of minor intersections and their adjacent road segments are quite sparse. (ii) Turning behaviors occur at different areas in intersections of various sizes. To address the above challenges, in this paper, we propose a precision-enhanced road intersection recognition method using cycling trajectories, called PICT. Initially, to enhance the representations of minor intersections, a grid topology representation module is designed to extract intersection topology. Then an intersection inference module based on multi-scale feature learning is put forward to identify the intersections of different scales correctly. Finally, extensive comparative experiments on two real-world datasets demonstrate that PICT significantly outperforms the state-of-the-art methods by 52.13% in the F1-score of intersection recognition.
KW - Cycling trajectories
KW - Intersection recognition
KW - Multi-scale feature learning
KW - Topology
UR - https://www.scopus.com/pages/publications/85174435765
U2 - 10.1007/978-3-031-43430-3_10
DO - 10.1007/978-3-031-43430-3_10
M3 - 会议稿件
AN - SCOPUS:85174435765
SN - 9783031434297
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 157
EP - 173
BT - Machine Learning and Knowledge Discovery in Databases
A2 - De Francisci Morales, Gianmarco
A2 - Bonchi, Francesco
A2 - Perlich, Claudia
A2 - Ruchansky, Natali
A2 - Kourtellis, Nicolas
A2 - Baralis, Elena
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 18 September 2023 through 22 September 2023
ER -