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Paper Citation Record · LEDGER

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting

As of 8 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2508.16685.

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

pith.paper-citation-record.v1
2508.16685 v1

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T18:19:00.657557Z

measured 52 of 52 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

52 of 52 outbound references displayed

  • verified exact1
  • verified fuzzy41
  • unresolved10
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d757bb20-8dbd-4dd1-b4cc-e4b6673b6b67 · outbound

This paper cites A survey of intelligent transportation systems.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting A survey of intelligent transportation systems

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:08.445356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:18:55.989730Z digest=sha256:a29f888ed8f2c4b93369f6795eeab015c79888be52b3108cb3b2506127a11f7e

Observation 9417a350-784d-4ae3-ad65-b3a7a30da42b · outbound

This paper cites A novel fuzzy deep-learning approach to traffic flow prediction with uncertain spatial--temporal data features.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting A novel fuzzy deep-learning approach to traffic flow prediction with uncertain spatial--temporal data features

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:08.325091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:18:56.118999Z digest=sha256:cab5a5b8048a4f89f52714e7e0e10a441cfeac9735ec8c1e17d8f2e36b439207

Observation 4ffda72a-aeb6-4968-ad85-63ae1de85782 · outbound

This paper cites Multi-range attentive bicomponent graph convolutional network for traffic forecasting.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Multi-range attentive bicomponent graph convolutional network for traffic forecasting

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:08.203281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:18:56.241879Z digest=sha256:d914ca68ede16f1a23d073c33cecd80766c321b54abbb1e1ea77d6570592f583

Observation b6305b77-7a59-45e9-9761-1f87ee67d182 · outbound

This paper cites Enhancing Traffic Flow Prediction using Outlier-Weighted AutoEncoders: Handling Real-Time Changes.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Enhancing Traffic Flow Prediction using Outlier-Weighted AutoEncoders: Handling Real-Time Changes

Reference 4

Resolution
verified exact
local_arxiv, observed 2026-08-05T18:19:00.913117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:18:56.329550Z digest=sha256:d9c27226768c2beb39995027915f04e5e5f16ed40b7bdab5fe2f7b5881da9720

Observation 7b05bf1b-8d36-4c52-9036-9cb9b1f2e630 · outbound

This paper cites A Generalization of Transformer Networks to Graphs.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting A Generalization of Transformer Networks to Graphs

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-05T18:18:56.415493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T18:18:56.415493Z digest=sha256:f685c6d8270f7a102071768e865f00e1c3ce70e4fe2d07906a8d665af6c0bb20

Observation d68032cc-9134-4ffa-9a4e-4c0fe26f0b85 · outbound

This paper cites Adaptive graph spatial-temporal transformer network for traffic forecasting.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Adaptive graph spatial-temporal transformer network for traffic forecasting

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:08.024884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:18:56.524182Z digest=sha256:c0e754778e135498586fbf1a7dcda9b741120b32d9bf740d149b37c326c21ab8

Observation 9874bbff-2f3f-49d8-8818-d6a013c2f48e · outbound

This paper cites Towards the development of intelligent transportation systems.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Towards the development of intelligent transportation systems

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:07.830957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:18:56.607170Z digest=sha256:2759cf0f7cacc89c1875e2aaf30408d66fc9eff67b8134293c5d4f12888e2b51

Observation d5330b5d-f1e4-439b-9ac6-ae8b67f37158 · outbound

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

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Attention based spatial-temporal graph convolutional networks for traffic flow forecasting

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:07.703769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:18:56.674444Z digest=sha256:fe67c5f324928a8c89cd8948e77a864a3de9d37cf138584132efe4e1e51b1333

Observation 9a5088a0-bde0-4038-aeb3-13de1fd87aaf · outbound

This paper cites Explainable traffic flow prediction with large language models.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Explainable traffic flow prediction with large language models

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-05T18:18:56.779312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T18:18:56.779312Z digest=sha256:574fa5d4d43734884464ab3c85f60f86c41e8d91e19f1bb44bc712a86a20039c

Observation e58161eb-1568-4121-a157-001f25d1bd0b · outbound

This paper cites Short-term prediction of traffic volume in urban arterials.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Short-term prediction of traffic volume in urban arterials

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:07.544248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:18:56.863363Z digest=sha256:1a58dd094d68adae832303ca5b11596f8bcb189ad38e2485149ad4f73f9d59bd

Observation 9ddda0c6-7a55-4c16-8ae5-e46bbc7f6556 · outbound

This paper cites Modern machine learning methods for time series analysis.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Modern machine learning methods for time series analysis

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:07.359909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:18:56.956414Z digest=sha256:cb179f1bfc6f4748270e55dcabf29eaf91a24899fb59eb8e602d74ffcce27d97

Observation 7a658add-92d0-4482-bf2a-ebf950b5ed44 · outbound

This paper cites Learning multiaspect traffic couplings by multirelational graph attention networks for traffic prediction.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Learning multiaspect traffic couplings by multirelational graph attention networks for traffic prediction

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:07.170855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:18:57.025704Z digest=sha256:f0c61fa2743832bcfcfe364f5c08d00535423a940ed374605ab03bcc11dce084

Observation 84edd3f1-413c-41be-98e9-caa00f0c43aa · outbound

This paper cites Sts-ccl: Spatial-temporal synchronous contextual contrastive learning for urban traffic forecasting.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Sts-ccl: Spatial-temporal synchronous contextual contrastive learning for urban traffic forecasting

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:07.020569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:18:57.152351Z digest=sha256:65612164ffc2d244986941e63836b1e2e65e78870f72e5b4343775fa25a87802

Observation 07e3f708-4449-4f20-836c-7291e60f94e9 · outbound

This paper cites Spatial-temporal fusion graph neural networks for traffic flow forecasting.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Spatial-temporal fusion graph neural networks for traffic flow forecasting

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:06.845857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:18:57.248539Z digest=sha256:2ee2526f1d952c33f72a9a10b26b02626fbf2d0f8dc57ec8826ed6e418d70356

Observation 829855d6-a57b-4816-a026-d311e551005a · outbound

This paper cites Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-05T18:18:57.343239Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T18:18:57.343239Z digest=sha256:b65197f79958640431f3b8c6c9bc6dff4070244727ef25b9f1506505577783a3

Observation b9c6ac13-a053-44c9-ba38-afebda466dcc · outbound

This paper cites Intelligent traffic flow prediction and analysis based on internet of things and big data.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Intelligent traffic flow prediction and analysis based on internet of things and big data

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:06.690094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:18:57.465379Z digest=sha256:fbf4d9715d7222675c3210ae9a21a9521e2dfefeb1ed203bf363b91bb4ebd14a

Observation 3f9b1247-e6b8-4541-a676-5bbabcf5dd45 · outbound

This paper cites Physical-virtual collaboration modeling for intra-and inter-station metro ridership prediction.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Physical-virtual collaboration modeling for intra-and inter-station metro ridership prediction

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:06.532927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:18:57.531770Z digest=sha256:630abfb769e22021461c9441d50e830b66dc837cc78d7b08a2222e97de4cb8e5

Observation 55c84117-869a-49b9-acb2-446d1e550753 · outbound

This paper cites Short-term traffic flow prediction with conv-lstm.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Short-term traffic flow prediction with conv-lstm

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:06.344816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:18:57.653789Z digest=sha256:41bc5ef67de827428170218376d199f6007e4c7096591fc82f4e05f11b903952

Observation af57d1b3-43eb-4a8f-b9ed-ff0fba71151f · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Swin transformer: Hierarchical vision transformer using shifted windows

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:06.167871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:18:57.734006Z digest=sha256:c73a99627c37159e1de05ff597b5502427e23a087bc44ff0f434e4dbfe210942

Observation 72eeb47c-1f88-49b2-952f-37a67234f535 · outbound

This paper cites St-trafficnet: A spatial-temporal deep learning network for traffic forecasting.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting St-trafficnet: A spatial-temporal deep learning network for traffic forecasting

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:05.969727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:18:57.806461Z digest=sha256:d8874446d1c9bc3b12d8ebe468923c125516bfa2cc76c3ec1b931ee6cd610f64

Observation 96e20d20-bd03-4165-aaad-28c26c45869c · outbound

This paper cites Neural Network Intelligence , 1 2021.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Neural Network Intelligence , 1 2021

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:05.773902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:18:57.895326Z digest=sha256:d1d2ad212fd7b5e8ac5396787fa676487938304d07ec6faf6b594cc1ba2a0400

Observation 48841247-e9b7-4e9c-a2ba-0fc78c10c42b · outbound

This paper cites An overview of model-driven and data-driven forecasting methods for smart transportation.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting An overview of model-driven and data-driven forecasting methods for smart transportation

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:05.556446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:18:57.994528Z digest=sha256:d80ff62b1b2d2c1343255df62c63f4fc7b46fcf58918159de7cbd7b1dab7d846

Observation 3d139cff-f40f-4c14-93eb-6f8100dba0e2 · outbound

This paper cites Dynamic time warping.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Dynamic time warping

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:05.352500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:18:58.086848Z digest=sha256:68e39a49fdc30373646deaf085712d14e7360b9092b48e2929fe9f1f82bf89a1

Observation 2acc724f-b460-4bc9-8ec0-67cabccd885a · outbound

This paper cites Traffic flow prediction for smart traffic lights using machine learning algorithms.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Traffic flow prediction for smart traffic lights using machine learning algorithms

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:05.134727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:18:58.175686Z digest=sha256:61953e05af2078ef2450b8546634f1fa5ec60706855b26602ba2c9ec1c5afd54

Observation 4ca5a98a-65d9-4bd6-a385-ddf79f36293d · outbound

This paper cites Traffic flow prediction using support vector regression.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Traffic flow prediction using support vector regression

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:04.891085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:18:58.260871Z digest=sha256:18d3c793811f42c1e8ed371546409f696c3e6fe81f9b04e9fff423a38172e6ff

Observation 269c6bca-811b-4648-af19-80f88d9d1ce3 · outbound

This paper cites Prospects and challenges of metaverse application in data-driven intelligent transportation systems.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Prospects and challenges of metaverse application in data-driven intelligent transportation systems

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:04.641282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:18:58.385503Z digest=sha256:2daf0a0b5e014008d5e6743fba8211ea4e7d078700c87eac10b0bc9cd59eebcb

Observation 0dc50c39-99f8-4834-bbc1-c3adbffa531d · outbound

This paper cites Dynamic prediction of traffic volume through kalman filtering theory.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Dynamic prediction of traffic volume through kalman filtering theory

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:04.416249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:18:58.455454Z digest=sha256:e694730718f4149a270595286a533b8b9bf8fab9c8806b6a1297007d4c4cddb9

Observation 1accf739-170e-4dab-b643-ffa201322847 · outbound

This paper cites Adaptive bayesian network for traffic flow prediction.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Adaptive bayesian network for traffic flow prediction

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:04.136481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:18:58.547736Z digest=sha256:86378555f170a3559e0a9a53de861d71c50af1b8e672aa1be049e2af7267b357

Observation ac17dec0-3533-4192-8991-fc01bed5dc14 · outbound

This paper cites Artificial intelligence-based traffic flow prediction: a comprehensive review.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Artificial intelligence-based traffic flow prediction: a comprehensive review

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:03.950105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:18:58.619475Z digest=sha256:1a79e63230afda2797323f176363bc639b902b86ad089647a9e2513c6ad8343d

Observation 544b31eb-1ef5-4cec-b04e-a9f2c01fd52d · outbound

This paper cites Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:03.775921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:18:58.682463Z digest=sha256:733f084fe3dabd3229730e5e3c67eb5120d2f56c49aff6d52ebc35ec2c013d97

Observation d00fba38-8077-41ac-9d76-8900b26caf3e · outbound

This paper cites Sequence to Sequence Learning with Neural Networks.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Sequence to Sequence Learning with Neural Networks

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-05T18:18:58.782763Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T18:18:58.782763Z digest=sha256:0d59728e90ff7055abc533d8be712394e26a6a5d711d635260e2ca868bbd33f3

Observation c8f21cd5-66b7-45e1-a712-7f22fa5da7f0 · outbound

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

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Combining kohonen maps with arima time series models to forecast traffic flow

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:03.519902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:18:58.877762Z digest=sha256:2bf70ea17729cf6c190fa6a82a70b9a1d5c2d3cc42f4019cd43978f7b69743d7

Observation f0acc66b-3ef4-4eaf-922c-1a7be5c8b872 · outbound

This paper cites Attention is all you need.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Attention is all you need

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-05T18:18:58.999207Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T18:18:58.999207Z digest=sha256:f99d02f5871e64e4a83ffcdf5d995474ee1a4fa1e32c9bc5998c845d250978dc

Observation 31f30a59-393c-4e0c-a298-7a3bd4293284 · outbound

This paper cites Traffic flow prediction based on bp neural network.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Traffic flow prediction based on bp neural network

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:03.368669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:18:59.086526Z digest=sha256:8b2ec94cc0a4ea03ce655a7510ce550c238a0d99c0af20b2ee934870931eedad

Observation a2d7c9fe-c625-4005-a99b-a5baa8aef8ec · outbound

This paper cites Modeling and forecasting vehicular traffic flow as a seasonal arima process: Theoretical basis and empirical results.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Modeling and forecasting vehicular traffic flow as a seasonal arima process: Theoretical basis and empirical results

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:03.141886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:18:59.184010Z digest=sha256:eaed15802760aad41ffa4c3bf07c6e33630fccef88f3795147268be28a81cf13

Observation 54c8c042-fbcf-4d4d-a587-5e0b025465d8 · outbound

This paper cites Multi-scale spatio-temporal attention networks for network-scale traffic learning and forecasting.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Multi-scale spatio-temporal attention networks for network-scale traffic learning and forecasting

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:02.967583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:18:59.278567Z digest=sha256:178a32e31376a18e85a4a2303d46c806657f7fc3777b03cac630b60b1e27ee1c

Observation 5b6e9311-3deb-47fb-a22d-4c68db9de8f9 · outbound

This paper cites Graph WaveNet for Deep Spatial-Temporal Graph Modeling.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Graph WaveNet for Deep Spatial-Temporal Graph Modeling

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-05T18:18:59.407540Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T18:18:59.407540Z digest=sha256:70308c2342d814328c7f3ef6e860b6ff71fab45abe9863d702c462fa9ada561d

Observation f25a61d4-93b5-46c4-95b5-414575a3c05d · outbound

This paper cites Spatiotemporal synchronous dynamic graph attention network for traffic flow forecasting.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Spatiotemporal synchronous dynamic graph attention network for traffic flow forecasting

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:02.764677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:18:59.483358Z digest=sha256:328ea1e06bdad48dfe52168327db71fda9763dd3e4876339811f59b98c692a49

Observation 39556834-9884-4632-acc4-57e9e5775508 · outbound

This paper cites Overview of machine learning-based traffic flow prediction.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Overview of machine learning-based traffic flow prediction

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:02.628384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:18:59.612990Z digest=sha256:f37208f85b28637c554eabf941ed75df6becf8294fb6f36d1a1c16ae5db8e7c5

Observation 04e67416-51da-4d2e-990b-49de0e040aa4 · outbound

This paper cites Spatial-Temporal Transformer Networks for Traffic Flow Forecasting.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Spatial-Temporal Transformer Networks for Traffic Flow Forecasting

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-05T18:18:59.694678Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T18:18:59.694678Z digest=sha256:3f7f491168b12583a972fc7a20f8dcd607c37e127a8357f710da3d6cb02dd287

Observation 8c67b7c2-f862-4f08-9877-6643e8f22d45 · outbound

This paper cites Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-05T18:18:59.774522Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T18:18:59.774522Z digest=sha256:e5c427d600d7f1d73e894614406e51ede8021042461048418a5071b0a8ee3b84

Observation bec1ebfa-faba-46b8-9ea2-84463c3ef746 · outbound

This paper cites Do transformers really perform badly for graph representation? Advances in neural information processing systems , 34:28877--28888, 2021.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Do transformers really perform badly for graph representation? Advances in neural information processing systems , 34:28877--28888, 2021

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:02.453925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:18:59.879497Z digest=sha256:a61b8f5f6a25f60d30124c93ce2d55f52f899089252271dae95985f514d943e4

Observation 066a5b5f-78b4-4755-9f8c-1b457d9ad935 · outbound

This paper cites Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-05T18:18:59.945263Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T18:18:59.945263Z digest=sha256:2e64ee34df26ff4cb08526948ca8e424de7f3ba57113cc068b308ed46d24eabe

Observation 216becbd-01f0-4bf2-9e9d-902e8fa1fa6a · outbound

This paper cites Sthsgcn: Spatial-temporal heterogeneous and synchronous graph convolution network for traffic flow prediction.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Sthsgcn: Spatial-temporal heterogeneous and synchronous graph convolution network for traffic flow prediction

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:02.261678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:19:00.006996Z digest=sha256:1a3246fde654193b33d1562dfdb3e1742b43eda206ab5effc0f1b825da22fa00

Observation 934ea034-ec55-4e0b-8f40-89c9989acf6d · outbound

This paper cites Dynamic spatial-temporal memory augmentation network for traffic prediction.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Dynamic spatial-temporal memory augmentation network for traffic prediction

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:02.072715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:19:00.078418Z digest=sha256:1d5119698088b627dfb8136e3622217e483ecc16832ac446314d43f3456eb305

Observation e4c501b5-bd98-4382-a49c-1d633c859cfe · outbound

This paper cites Data-driven intelligent transportation systems: A survey.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Data-driven intelligent transportation systems: A survey

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:01.894914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:19:00.169692Z digest=sha256:26c48c15dcc8ff56191d9362f8a4a8e47698f264047f60445eaed9c4c5e5d076

Observation af4e7982-684a-4f7e-a5d2-9630bdb7f965 · outbound

This paper cites Urban traffic flow congestion prediction based on a data-driven model.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Urban traffic flow congestion prediction based on a data-driven model

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:01.722840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:19:00.265786Z digest=sha256:2c280431f86bfabee8580116b43ca0af835458ddb767e7093278d98b27827321

Observation 0b66b4b6-3141-462c-b337-7c6116e9cdb4 · outbound

This paper cites An improved k-nearest neighbor model for short-term traffic flow prediction.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting An improved k-nearest neighbor model for short-term traffic flow prediction

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:01.583885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:19:00.339334Z digest=sha256:f939327768f82a729ca588159f5b94f326ad4ad1744ca0ab5d38b584d0d61e85

Observation aa08c772-2730-42f2-9142-bcc9bd0bcf8a · outbound

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

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting T-gcn: A temporal graph convolutional network for traffic prediction

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:01.411024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:19:00.397842Z digest=sha256:a139db18e95d454d7ea0124d996179a7c4db4009c13651d4bd64c051d0a27be5

Observation 6a23e58f-78f4-4514-861c-bfb525f9eee8 · outbound

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

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Gman: A graph multi-attention network for traffic prediction

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:01.280264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:19:00.478986Z digest=sha256:8028b78aa98445a0245da505292974cea8ef6298adebe1db3074ec72afa94be1

Observation 43788a73-b6c6-4270-bb70-1ca97a8179d0 · outbound

This paper cites Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-05T18:19:00.561233Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T18:19:00.561233Z digest=sha256:305563d7acf2a2311b6a9eea4cf8a6291f2c5d4893a43b45504abdafad6acde0

Observation ef8c0e09-81d1-4eac-a9e8-93c73f36a6b1 · outbound

This paper cites Kst-gcn: A knowledge-driven spatial-temporal graph convolutional network for traffic forecasting.

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting Kst-gcn: A knowledge-driven spatial-temporal graph convolutional network for traffic forecasting

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:19:01.123451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:19:00.657557Z digest=sha256:493353256ae73cb1280e443b600c3ebc87bd458ea3841d8d32fcf6b4fd32a3c8

Pith citing papers

No inbound Pith citation observations are available.