Paper Citation Record · LEDGER

Learning Traffic Anomalies from Generative Models on Real-Time Observations

As of 10 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 1 inbound Pith citation observation for arXiv:2502.01391.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.01391 v5

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T15:32:55.599138Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T22:13:53.503637Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-10T05:30:23.456663Z

Reference resolution

25 of 25 outbound references displayed

verified exact1
verified fuzzy14
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

0
pith, observed 2026-08-10T05:30:23.456663Z

Outbound references

Observation 775096cc-e312-4573-b51e-3d9dbed40ad3 · outbound

This paper cites T-gcn: A temporal graph convolutional network for traffic prediction,.

Learning Traffic Anomalies from Generative Models on Real-Time Observations T-gcn: A temporal graph convolutional network for traffic prediction,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-09T15:32:55.483760Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24f6f6b6-346c-4059-950a-2b4871bc8cde · outbound

This paper cites Deep learning on traffic prediction: Methods, analysis, and future directions,.

Learning Traffic Anomalies from Generative Models on Real-Time Observations Deep learning on traffic prediction: Methods, analysis, and future directions,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-09T15:32:55.489361Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e70c4abc-ae99-482d-aa9d-3d9f889e1733 · outbound

This paper cites Information metrics for improved traffic model fidelity through sensitivity analysis and data assimilation,.

Learning Traffic Anomalies from Generative Models on Real-Time Observations Information metrics for improved traffic model fidelity through sensitivity analysis and data assimilation,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:32:55.950484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a589f326-f524-43b8-b95f-04a3ac7b5e36 · outbound

This paper cites A deep learning-based framework for road traffic prediction,.

Learning Traffic Anomalies from Generative Models on Real-Time Observations A deep learning-based framework for road traffic prediction,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:32:55.936352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 766c71c4-0097-473c-bea9-2a48a7b5db33 · outbound

This paper cites Applications of deep learning in traffic management: A review,.

Learning Traffic Anomalies from Generative Models on Real-Time Observations Applications of deep learning in traffic management: A review,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:32:55.922556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1ebc4e8d-cda7-4cd0-9502-65151d8ab515 · outbound

This paper cites Stochastic description of traffic flow,.

Learning Traffic Anomalies from Generative Models on Real-Time Observations Stochastic description of traffic flow,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:32:55.908464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation abf54273-dd53-42d7-aeab-1e35d42749e5 · outbound

This paper cites Change detection from a street image pair using cnn features and superpixel segmentation.

Learning Traffic Anomalies from Generative Models on Real-Time Observations Change detection from a street image pair using cnn features and superpixel segmentation

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:32:55.893874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 18a273b1-eebc-4f9a-86ff-66236653b7d4 · outbound

This paper cites Automatic traffic anomaly detection on the road network with spatial-temporal graph neural network representation learning,.

Learning Traffic Anomalies from Generative Models on Real-Time Observations Automatic traffic anomaly detection on the road network with spatial-temporal graph neural network representation learning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:32:55.878059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 02e28e54-5135-4d00-9f91-f4b782bf5bc1 · outbound

This paper cites Traffic flow prediction with big data: A deep learning approach,.

Learning Traffic Anomalies from Generative Models on Real-Time Observations Traffic flow prediction with big data: A deep learning approach,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:32:55.848529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2243c039-8f29-4f82-b252-6ea180580eff · outbound

This paper cites Graph convolutional adversarial networks for spatiotem- poral anomaly detection,.

Learning Traffic Anomalies from Generative Models on Real-Time Observations Graph convolutional adversarial networks for spatiotem- poral anomaly detection,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:32:55.833730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 90647970-bede-425d-99e0-a3d359aaa3a4 · outbound

This paper cites A comprehensive survey on graph neural networks,.

Learning Traffic Anomalies from Generative Models on Real-Time Observations A comprehensive survey on graph neural networks,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:32:55.819289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6aff5f9d-d144-428c-a9a4-e54352ccae3e · outbound

This paper cites Long short-term memory,.

Learning Traffic Anomalies from Generative Models on Real-Time Observations Long short-term memory,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-09T15:32:55.540333Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef7ca951-3203-45fd-8f40-9870202675b9 · outbound

This paper cites Traffic demand and longer term forecasting from real-time observations,.

Learning Traffic Anomalies from Generative Models on Real-Time Observations Traffic demand and longer term forecasting from real-time observations,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:32:55.795066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bcbd7e12-71e2-4695-9d24-8dc863c1495e · outbound

This paper cites Spatio-temporal graph convolutional net- works: A deep learning framework for traffic forecasting,.

Learning Traffic Anomalies from Generative Models on Real-Time Observations Spatio-temporal graph convolutional net- works: A deep learning framework for traffic forecasting,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-09T15:32:55.549196Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4e44609-faf0-4a2d-a2fb-0e4feb5fb682 · outbound

This paper cites Traffic data recon- struction via adaptive spatial-temporal correlations,.

Learning Traffic Anomalies from Generative Models on Real-Time Observations Traffic data recon- struction via adaptive spatial-temporal correlations,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:32:55.771217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ce4f97f9-a009-4b76-b0b3-da320d092651 · outbound

This paper cites Diffusion convolutional recurrent neural network: Data-driven traffic forecasting,.

Learning Traffic Anomalies from Generative Models on Real-Time Observations Diffusion convolutional recurrent neural network: Data-driven traffic forecasting,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-09T15:32:55.576325Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 423b3110-7f62-4604-a0f0-e4bfb955d7a2 · outbound

This paper cites Graph neural networks: A review of methods and applications,.

Learning Traffic Anomalies from Generative Models on Real-Time Observations Graph neural networks: A review of methods and applications,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:32:55.747388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c61dcfe0-6b9b-4eb1-a00a-f22d2ac45537 · outbound

This paper cites Forecasting travel speed in the rainfall days to develop suitable variable speed limits control strategy for less driving risk,.

Learning Traffic Anomalies from Generative Models on Real-Time Observations Forecasting travel speed in the rainfall days to develop suitable variable speed limits control strategy for less driving risk,

Reference 20

Resolution: verified exact; doi, observed 2026-08-09T15:32:55.634843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2818a645-23cd-4c1a-a3b3-d1bbfa200cfe · outbound

This paper cites Generative adversarial nets,.

Learning Traffic Anomalies from Generative Models on Real-Time Observations Generative adversarial nets,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:32:55.733506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d2764707-4270-41db-8f4f-334372d4440b · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Learning Traffic Anomalies from Generative Models on Real-Time Observations Adam: A Method for Stochastic Optimization

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-09T15:32:55.594305Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80a37af1-84f3-4b08-aba2-ae50c6ac258e · outbound

This paper cites YOLOv5 by Ultralytics,.

Learning Traffic Anomalies from Generative Models on Real-Time Observations YOLOv5 by Ultralytics,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-09T15:32:55.599138Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a04315da-7bcc-4bca-8198-59c7e53aee85 · outbound

This paper cites Latent space conditioning for improved classification and anomaly detection.

Learning Traffic Anomalies from Generative Models on Real-Time Observations Latent space conditioning for improved classification and anomaly detection

Reference 2019

Resolution: metadata mismatch; local_arxiv, observed 2026-08-09T15:32:55.693587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fb8cd450-4f46-4ed1-8bb7-d39a860b0187 · outbound

This paper cites A Comprehensive Survey on Graph Neural Networks.

Learning Traffic Anomalies from Generative Models on Real-Time Observations A Comprehensive Survey on Graph Neural Networks

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-09T15:32:55.535511Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b82c8ec3-872e-4e5f-8c26-602522bdec6c · outbound

This paper cites Available: https://onlinelibrary.wiley.com/doi/abs/10.

Learning Traffic Anomalies from Generative Models on Real-Time Observations Available: https://onlinelibrary.wiley.com/doi/abs/10

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:32:55.863378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 19c54830-a710-4c93-9492-161161095cd3 · outbound

This paper cites Improved Anomaly Detection through Conditional Latent Space VAE Ensembles.

Learning Traffic Anomalies from Generative Models on Real-Time Observations Improved Anomaly Detection through Conditional Latent Space VAE Ensembles

Reference 2024

Resolution: metadata mismatch; local_arxiv, observed 2026-08-09T15:32:55.672392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

Observation c5a9ad35-63df-44ef-8964-1bac91cd9dfb · inbound

Real-Time Analysis of Unstructured Data with Machine Learning on Heterogeneous Architectures cites this paper.

Real-Time Analysis of Unstructured Data with Machine Learning on Heterogeneous Architectures Learning Traffic Anomalies from Generative Models on Real-Time Observations

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-05T22:19:11.762209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.