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Paper Citation Record · LEDGER

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

As of 17 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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:18:55.989730Z digest=sha256:23709f8e17cbf7248f51dbcc82f1ce692d0d40ec08115b7d4e4d69c0f16e777e

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:18:56.118999Z digest=sha256:e3a080d927ef8baad95141e5a0ba53b6293a920bfa6e0bd651e6256b0ded2f7e

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:18:56.241879Z digest=sha256:ce6747bfae6dfbf1c38e9acbc6e19503a82b916e4e18c8906968da5bf2900c15

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:18:56.329550Z digest=sha256:b1fccd6059666d239ccd6425e80012bddd007917481c6e328b12528e1cfe233e

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

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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:e6377594ea5c5a0f6b19db003a0a3e19e25bff0116039dbad4bf5f563a8029e8

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:18:56.524182Z digest=sha256:d762d48b16d070cdb7f35153a0fbe877d2daedda74b160dc46e238378b331a25

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:18:56.607170Z digest=sha256:b607022283ee8519f9cc1ba46c1d7339e9ebf29e10a82bc8c26389548a17cb12

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:18:56.674444Z digest=sha256:1c0c6da0680cca6d6d12e211def70aa6f1629a31adb50c03a3665e1a6a4a0037

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=sha256:736fcc217cfbbaf2a1ce608a2c0fc9a5915a5c518413f77e4a00b9e9ad57e12f

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:18:56.863363Z digest=sha256:a7a09a98a366e948248d8e76f7f9dbe418a975e68e78a3c56f390dedd251319b

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:18:56.956414Z digest=sha256:1527de7773f0d990c5bf657bfa349e5fa9a59f55432ce4e878e3831da9df1afc

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:18:57.025704Z digest=sha256:397f8177e0ba91bc7dec0c13aecb07ef1fc18f34c95a181f890dee629cc24240

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

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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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:18:57.152351Z digest=sha256:4abefb21009cad32a6e3925c4f199e47231c83486140cbe997578881d878c3e4

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:18:57.248539Z digest=sha256:158e44597644c14acef33535bc4fce58eedc7836bd7e9815bc825b61db5156a2

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=sha256:65153d80418d270c4e975c66472a51dba4c87e483ca41e5415db8597774c78aa

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:18:57.465379Z digest=sha256:8a7bc38f5cf98b331a6ad349bbc5fe0cf0896a656c99458df1ab7d113da07af9

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:18:57.531770Z digest=sha256:a1274a948bb714b2d530fc818ea053e72a3bd04cdf1d1fba4fd3890e15ba01c9

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:18:57.653789Z digest=sha256:58f3dc9596fc13f6f9550bcdb537e0e650510671f42b7fa87b27ba90d351fc0b

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:18:57.734006Z digest=sha256:9e9baa8524e5275697e15ea3d5d3fd0fc65bf359493a58713fa0c9d065ae7d17

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:18:57.806461Z digest=sha256:93cef0665c1c87eac69fc7c451c08b807015ccdd266eedf9e587b430f32835d4

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:18:57.895326Z digest=sha256:5744a81d127da62dc9e4d683adf79ac11666153aa8dea77135112891f71823c4

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:18:57.994528Z digest=sha256:241b4b68d8f3b066241a8319510039ae7fdfdc488aacff2137dd3fc992f40e17

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:18:58.086848Z digest=sha256:0aa514eeee52e07bd5182e372149cd81b3d81e70d27f0029382872c3d8fe2f06

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 2026-08-17T06:30:58.91139+00:00.

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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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:18:58.260871Z digest=sha256:91ec4b929a7fb73ef678de38de156695653c814395faf4b9305951164910a27b

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:18:58.385503Z digest=sha256:540cddda6b712f7f7ec3415ef33778c1baea11f881c3ecdb531ac0adf4a86024

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:18:58.455454Z digest=sha256:d85f900afea626d057939d5ad466e8365569b8472358072007cfd2fa580f6f6d

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:18:58.547736Z digest=sha256:1ef8e9a484d7ff68a7d64e048ae888908f6f5a6522f0ea25b982016b5a0b9032

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:18:58.619475Z digest=sha256:1c6d3364d2f22e9858f8ec0e4dfc05fd5afcbe7b5a62811a82badc4cafbdd094

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:18:58.682463Z digest=sha256:90d3ed9117ba558931323ebed4ef552a54f185ed0705aa74cd192b4e446a871a

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=sha256:9c7400d32e61727b24daa8587f64a7a5a51ef01c2cccae3acfc8fd506c093cc5

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:18:58.877762Z digest=sha256:c988e724c2606aa2f9c44850f94c6e82a9fc68abb00dec425f9dc21d02021ffd

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=sha256:3ad1a3b63a527fe98ed6181b0fcdc688b9b4b3442cb6a9f5300af08fdf5c37da

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:18:59.086526Z digest=sha256:031aa929640b8c3ccb07a662309d2a2c63127050e546e009e5426073622a5e51

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:18:59.184010Z digest=sha256:3abc0fc35a0ba2c051135e4bd790f0f5eafba682ab43144ab5b31342c7f9f4b8

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:18:59.278567Z digest=sha256:fbd75f892994c24d4165469460639ff49ef8873f544b571537e491f82ed84853

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:e446cd97919b2d01bc20d4d38d346215527ac0c9f85ef5aefa5acdd8b06fca04

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:18:59.483358Z digest=sha256:0bd3217da54a34c5a2eb2c5c6a0cccf2890ec392133c1bd161d58f4888650908

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:18:59.612990Z digest=sha256:66df114142b3fd24d7cdee1c3e74a86d896ceb82ad58ed0ebbf0ca8014fbe803

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=sha256:70e1adb664c9936ca22a85b4ed3fdfdeb81abb02ea092945023fb1e1c84abd15

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=sha256:0dcd93ebcf6eeead52cfd3c0ab4847e38ff0360f0fecd5c9ee23e7cccf14453d

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:18:59.879497Z digest=sha256:e9ada26d502492a820cc27960290baa142ea04663000bf947ac19b2d629361a0

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:df5a212fc8e49eea8d7c70615b5f97648595572e993a468137e4e4a4c81b73b9

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:19:00.006996Z digest=sha256:c898aa20ff31b49920696d8f2598b62b7d8646daa1a0401d7e7c0c2758b6bf00

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:19:00.078418Z digest=sha256:62f173d86badb1323e1de5dba3464bdba8d0ab5c22a966321d7c3105dead3aa1

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:19:00.169692Z digest=sha256:03158186a8fa53cd40a8dcc47b5a35e41f26fdb2a1ed9495355e0a73a92ac88b

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:19:00.265786Z digest=sha256:424873f9d90c98edac6836e0984dec8ea28949c620d590f110bc69f6cbadbd43

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:19:00.339334Z digest=sha256:2d3df0b8059fbb0d193764054798582b66ba176a4a341e51e1a6b0305bceaa90

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:19:00.397842Z digest=sha256:56db82c624361374cf86e2f260879486ef8ddc7973d19f46ff494ddb1ab2c96b

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:19:00.478986Z digest=sha256:1e15c7aab63f14f6832bb02f56df2aee8cc07cde06a5d09010091689516b3632

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=sha256:61d84daac6207ee7b84f1bf0040bf0424ddb80c2854f9487c3cb64b31e6e0e6c

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-05T18:19:00.657557Z digest=sha256:dabec954f1b9e075fcf005154ec9a4310f0a7618b30999cd4b6f3c3a7a95c293

Pith citing papers

No inbound Pith citation observations are available.