|
[1]CECCATO V, GAUDELET N, GRAF G. Crime and
safety in transit environments: A systematic review of the
English and the French literature, 1970-2020[J]. Public
Transport, 2022, 14(1): 105-153.
[2]SOTO J, OROZCO F M, USECHE S A, et al. Public
transportation and fear of crime at BRT systems:
Approaching to the case of Barranquilla (Colombia)
through integrated choice and latent variable models[J].
Transportation Research Part A: Policy and Practice,
2022, 155: 142-160.
[3]ZHAO X, ZHANG Y, WANG S, et al. Detecting
pickpocketing gangs on bus with smart card data[J].
IEEE Intelligent Transportation Systems Magazine,
2019, 11(3): 181-199.
[4]DU B W, LIU C R, ZHOU W J, et al. Detecting
pickpocket suspects from large-scale public transit
records[J]. IEEE Transactions on Knowledge and Data
Engineering, 2018, 31(3): 465-478.
[5]WANG L, ZHANG Y, ZHAO X, et al. Irregular travel
groups detection based on cascade clustering in urban subway[J].
IEEE
Transactions
on
Intelligent
Transportation Systems, 2020, 21(5): 2216-2225.
[6]ZHAO X, GAO Y, LI Z H, et al. IEF-BT-GCN: A high
precision rail transit passenger flow prediction model
with complex external factors[C]. Edmonton Canada: the
27th IEEE International Conference on Intelligent
Transportation Systems, 2024.
[7]YU W H, HUANG M Q, WU S Y, et al. Ensembled
masked graph autoencoders for link anomaly detection in
a road network considering spatiotemporal features[J].
Information Sciences, 2023, 622: 456-475.
[8]LI Z H, XU H, GAO X L, et al. Fusion attention
mechanism bidirectional LSTM for short-term traffic flow
prediction[J]. Journal of Intelligent Transportation
Systems, 2022, 28(4): 511-524.
[9]HUO G, ZHANG Y, GAO J, et al. CaEGCN: Cross
attention fusion based enhanced graph convolutional
network for clustering[J]. IEEE Transactions on
Knowledge and Data Engineering, 2021, 35(4), 3471-3483.
[10] WANG M, ZHANG Y, ZHAO X, et al. Traffic origin
destination demand prediction via multi-channel
hypergraph
convolutional
networks[J].
IEEE
Transactions on Computational Society Systems, 2024, 11
(4): 5496-5509.
[11] ZHAO X, ZHANG Y, HU Y, et al. Modeling relation
proximity of passengers using public transit smart card
data[J].
IEEE Intelligent Transportation Systems
Magazine, 2022, 12(1): 163-172.
[12] PENG Z, LIU H, JIA Y, et al. Attention-driven graph
clustering network[C]. Beijing: Multimedia Conference,2021.
[13] WANG J, ZHANG Y, WANG L, et al. Multitask
hypergraph convolutional networks: A heterogeneous
traffic prediction framework[J]. IEEE Transactions on
Intelligent Transportation Systems, 2022, 23(10): 18557-18567.
[14] SUN X G, YIN H Z, LIU B, et al. Heterogeneous
hypergraph embedding for graph classification[C]. New
York: Proceedings of the 14th ACM International
Conference on Web Search and Data Mining, 2021.
[15] SHU Z Y, SUN X G, CHENG H, et al. When LLM meets
hypergraph: A sociological analysis on personality via
online social networks[C]. Idaho: Proceedings of the 33rd
ACM International Conference on Information and
Knowledge Management, 2024.
[16] CHU Z X, WANG Y, CUI Q, et al. LLM-guided multi
view hypergraph learning for human-centric explainable
recommendation[J]. Arxiv Preprint Arxiv, 2024:
2401.08217.
[17] KIPF T N, WELLING M. Variational graph auto-encoders
[C]. Barcelona: Conference and Workshop on Neural
Information Processing Systems, 2016.
[18] GUO X F, GAO L, LIU X W, et al. Improved deep
embedded clustering with local structure preservation[C].
Melbourne: International Joint Conference on Artificial
Intelligence, 2017: 1753-1759.
[19] BO D, WANG X, SHI C, et al. Structural deep clustering
network[C]. Taiwan: The Web Conference, 2020.
[20] ZHAO J, SUN Y, GUO J. Robust graph convolutional
clustering with adaptive graph learning[C]. Padua:
International Joint Conference on Neural Networks, 2022.
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