Paper Citation Record · LEDGER

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting

As of 13 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2411.14046.

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

pith.paper-citation-record.v1
2411.14046 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:41:30.231370Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

45 of 45 outbound references displayed

verified exact0
verified fuzzy36
unresolved9
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e714406b-1cb6-42f8-ac91-bfbaad5cedee · outbound

This paper cites A survey of road traffic congestion measures towards a sustainable and resilient transportation system,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting A survey of road traffic congestion measures towards a sustainable and resilient transportation system,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:31.049684Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 449dbb9b-97c5-41d8-bfb8-b6a3d74959f3 · outbound

This paper cites Attention based spatial- temporal graph convolutional networks for traffic flow forecasting,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Attention based spatial- temporal graph convolutional networks for traffic flow forecasting,

Reference 2

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Source-reported events for the cited work

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Observation a96eade8-e739-4aa9-b6b2-a4b248874d4b · outbound

This paper cites Deep learning models for traffic flow prediction in autonomous vehicles: A review, solutions, and challenges,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Deep learning models for traffic flow prediction in autonomous vehicles: A review, solutions, and challenges,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:31.020719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e24ca048-d6eb-4ead-90d2-98ea0f359bdb · outbound

This paper cites Enlstm-wpeo: Short-term traf- fic flow prediction by ensemble lstm, nnct weight integration, and population extremal optimization,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Enlstm-wpeo: Short-term traf- fic flow prediction by ensemble lstm, nnct weight integration, and population extremal optimization,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:31.004013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 618749b7-dc31-43d7-a0e4-3c76c8d4a8dc · outbound

This paper cites Urbanfm: Inferring fine-grained urban flows,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Urbanfm: Inferring fine-grained urban flows,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:30.986536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e63a5b8c-a716-4017-a2b5-1af5d67221e9 · outbound

This paper cites Gman: A graph multi-attention network for traffic prediction,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Gman: A graph multi-attention network for traffic prediction,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T15:41:29.997698Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6fc691f6-fecc-4065-b8ca-a192ae251818 · outbound

This paper cites Federated machine learning: Concept and applications,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Federated machine learning: Concept and applications,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T15:41:30.004000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14107ce5-ff22-47b3-b8f6-396f53050793 · outbound

This paper cites Privacy-preserving traffic flow prediction: A federated learning approach,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Privacy-preserving traffic flow prediction: A federated learning approach,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:30.940433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c23baf42-ddc2-4645-a7db-550ddc52e00e · outbound

This paper cites Fastgnn: A topological information protected federated learning approach for traffic speed fore- casting,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Fastgnn: A topological information protected federated learning approach for traffic speed fore- casting,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:30.921577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 77dded58-172d-4866-bf60-dc7e93e22f68 · outbound

This paper cites Cross-node federated graph neural network for spatio-temporal data modeling,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Cross-node federated graph neural network for spatio-temporal data modeling,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:30.902782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation af3a0f5b-7f7d-497b-af32-8a9cdfa521b4 · outbound

This paper cites Learning under concept drift: A review,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Learning under concept drift: A review,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T15:41:30.027783Z

Source-reported events for the cited work

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Observation 94c78a2d-c383-42ce-b72a-e4e22ceb484c · outbound

This paper cites Online learning: A compre- hensive survey,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Online learning: A compre- hensive survey,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:30.869213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 847bb688-38b7-4e18-9367-5a0fcb45c97b · outbound

This paper cites Online spatio-temporal correlation-based federated learning for traffic flow forecasting,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Online spatio-temporal correlation-based federated learning for traffic flow forecasting,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:30.852874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b7052392-4e5d-45ce-bfe2-f68bc021a6dd · outbound

This paper cites Time series analysis,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Time series analysis,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:30.838051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 148c36ab-e663-45d0-ac75-82c939613435 · outbound

This paper cites Combining kohonen maps with arima time series models to forecast traffic flow,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Combining kohonen maps with arima time series models to forecast traffic flow,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:30.822882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9cef9f99-e893-4091-9cf4-f671963cd475 · outbound

This paper cites Application of subset autoregressive in- tegrated moving average model for short-term freeway traffic volume forecasting,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Application of subset autoregressive in- tegrated moving average model for short-term freeway traffic volume forecasting,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:30.804688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7b7c495d-be84-4d9e-829c-de1f261b4eae · outbound

This paper cites Traffic flow prediction using lstm with feature enhancement,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Traffic flow prediction using lstm with feature enhancement,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:30.784914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 90d4036e-b3e9-46c6-85eb-8affb9999e94 · outbound

This paper cites Short-term traffic flow prediction method for urban road sections based on space–time analysis and gru,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Short-term traffic flow prediction method for urban road sections based on space–time analysis and gru,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:30.762975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0107e778-3b0d-482d-805a-cf01b048bbee · outbound

This paper cites Ssgru: A novel hybrid stacked gru- based traffic volume prediction approach in a road network,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Ssgru: A novel hybrid stacked gru- based traffic volume prediction approach in a road network,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:30.745770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8cd9ff6d-4ec1-4b0b-ae05-10b6a999cf1e · outbound

This paper cites A short-term traffic speed prediction model based on lstm networks,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting A short-term traffic speed prediction model based on lstm networks,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:30.727489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d99b3105-3251-4852-a4d3-1b439b00f012 · outbound

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

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Diffusion convolutional recurrent neural network: Data-driven traffic forecasting,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:30.707257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation cfa1ae49-07ac-4bf8-a400-772e83d3c635 · outbound

This paper cites Personalized federated learning for cross-city traffic prediction,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Personalized federated learning for cross-city traffic prediction,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:30.687202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 38567448-6050-40ce-81f1-1bc7bb83538a · outbound

This paper cites Short-term traffic flow prediction based on graph convolutional networks and federated learning,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Short-term traffic flow prediction based on graph convolutional networks and federated learning,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:30.668180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation babe18d0-e23a-4263-aa31-2f63e79f4a47 · outbound

This paper cites Multilevel federated learning-based intelligent traffic flow forecasting for transportation network management,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Multilevel federated learning-based intelligent traffic flow forecasting for transportation network management,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:30.645750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c1fc1f5e-3d36-4191-a214-5096c784112e · outbound

This paper cites Stfl: A spatial-temporal federated learning framework for graph neural networks,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Stfl: A spatial-temporal federated learning framework for graph neural networks,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:30.625880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2ee3b05b-eaf8-4c8d-8dc8-716f8ded17c8 · outbound

This paper cites Fedstn: Graph representation driven federated learning for edge computing enabled urban traffic flow prediction,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Fedstn: Graph representation driven federated learning for edge computing enabled urban traffic flow prediction,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:30.609761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a4590c1f-2f23-41f3-a063-f402c94c500e · outbound

This paper cites Fedgtp: Exploiting inter-client spatial dependency in federated graph- based traffic prediction,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Fedgtp: Exploiting inter-client spatial dependency in federated graph- based traffic prediction,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:30.591043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 68ca0343-4acd-4785-a2ec-1c59f1c3e9bd · outbound

This paper cites A concept drift-tolerant case-base editing technique,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting A concept drift-tolerant case-base editing technique,

Reference 28

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Source-reported events for the cited work

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Observation a20f496f-c76c-41dd-8595-3536c299a8e5 · outbound

This paper cites Detecting change in data streams,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Detecting change in data streams,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:30.560552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c253d934-1949-4c97-9d5c-f8a72915eacc · outbound

This paper cites Concept drift detection based on equal density estimation,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Concept drift detection based on equal density estimation,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:30.538826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6ba1f550-d149-4665-974c-553fd0287dd1 · outbound

This paper cites An incremental change detection test based on density difference estimation,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting An incremental change detection test based on density difference estimation,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:30.519425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1562859e-e2ed-4eb0-b34a-a1c08d8d0205 · outbound

This paper cites Learning with drift detection,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Learning with drift detection,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:30.496668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0aa42526-bfe7-4ff6-88f9-db7757921747 · outbound

This paper cites Early drift detection method,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Early drift detection method,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:30.474482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6a19348e-57d3-4208-9075-7e0b7f2840e8 · outbound

This paper cites Detecting concept drift using statistical testing,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Detecting concept drift using statistical testing,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:30.456292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9f48538f-cfc7-48ff-b029-103ce6b216c4 · outbound

This paper cites Communication-efficient learning of deep networks from decentralized data,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Communication-efficient learning of deep networks from decentralized data,

Reference 35

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Source-reported events for the cited work

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Observation bb5f1bc2-8359-4f18-bb67-e2be774eda7f · outbound

This paper cites Kullback-leibler divergence,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Kullback-leibler divergence,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-12T15:41:30.170006Z

Source-reported events for the cited work

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Observation 5c478949-636f-4749-a5f0-93d910b9b67c · outbound

This paper cites Learning phrase representations using RNN encoder–decoder for statistical machine translation,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Learning phrase representations using RNN encoder–decoder for statistical machine translation,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:30.409967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 72826a8b-cac3-45bd-99f5-71d7e796db8e · outbound

This paper cites Semi-supervised classification with graph convolutional networks,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Semi-supervised classification with graph convolutional networks,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:30.388804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9bbb52eb-6a3c-4cdb-9229-918933af2dd2 · outbound

This paper cites Svm-based models for predicting wlan traffic,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Svm-based models for predicting wlan traffic,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:30.370949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 040678d9-9895-4780-8edd-26f938bc4c99 · outbound

This paper cites Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:30.353789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c1bb7cb5-a7a9-4ba7-8773-6a51567e093b · outbound

This paper cites Spatio-temporal meta-graph learning for traffic forecasting,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Spatio-temporal meta-graph learning for traffic forecasting,

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-12T15:41:30.204677Z

Source-reported events for the cited work

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Observation 280b1ee5-b146-4c51-bdb8-07dde243033d · outbound

This paper cites Strictly proper scoring rules, prediction, and estimation,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Strictly proper scoring rules, prediction, and estimation,

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-12T15:41:30.212694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e83c085a-0b4f-43ac-b70f-148378ddc1fb · outbound

This paper cites Generalizing the theta method for automatic forecasting,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Generalizing the theta method for automatic forecasting,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:30.312879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f25a3fc8-324f-4683-813f-6c73f01cbae7 · outbound

This paper cites Pruning convolutional neural networks for resource efficient inference,.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting Pruning convolutional neural networks for resource efficient inference,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:30.296588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4a18b992-6deb-4a2a-b590-1452428dd272 · outbound

This paper cites What’s the backward-forward flop ratio for neural networks?.

REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting What’s the backward-forward flop ratio for neural networks?

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:30.279238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

No inbound Pith citation observations are available.