STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction | Paper Citation Record

Observation 2b8630f5-0486-4a2b-bc4b-c6faca4648ba · outbound

This paper cites Predicting Severe Injury in Motor Vehicle Crashes,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction Predicting Severe Injury in Motor Vehicle Crashes,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:39:01.533298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b3e29ff9-d8b4-4c88-86ee-26395fa4694c · outbound

This paper cites Spatio-temporal analysis of road traffic crashes by severity,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction Spatio-temporal analysis of road traffic crashes by severity,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:39:01.366815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e106ed9d-cad3-47b2-9052-93f65bdc9935 · outbound

This paper cites An overview of multinomial logistic regression for traffic accident severity classification,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction An overview of multinomial logistic regression for traffic accident severity classification,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:39:01.022855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 76dc2f32-21d1-477b-866e-b4da8a13005f · outbound

This paper cites Ordered probit models for motor vehicle crash injury severity in New Jersey,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction Ordered probit models for motor vehicle crash injury severity in New Jersey,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:39:00.763826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f83e8fee-cdbd-4c70-b5a2-869ca421c4dd · outbound

This paper cites Ensemble methods for accident severity prediction using regression, trees, and forests,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction Ensemble methods for accident severity prediction using regression, trees, and forests,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:39:00.474930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4a0495af-7325-4c3c-b5de-03b050349750 · outbound

This paper cites Using support vector machine models for crash injury severity analysis,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction Using support vector machine models for crash injury severity analysis,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:39:00.219130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 11f278dc-a2d8-4895-ac83-633ca553237d · outbound

This paper cites Transportation network connectivity and accident analysis: A case study of Gainesville, Florida,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction Transportation network connectivity and accident analysis: A case study of Gainesville, Florida,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:39:00.006893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9c3b6cac-6230-4252-9a13-1b0915f387b0 · outbound

This paper cites Spatio -temporal graph convolutional networks: A comprehensive review,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction Spatio -temporal graph convolutional networks: A comprehensive review,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:59.796026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b5a258e3-9f1e-451f-8e62-5d72ff50063b · outbound

This paper cites A survey on graph neural networks,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction A survey on graph neural networks,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:59.605047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 99298c9f-a928-485a-96ce-e04b35aad2fa · outbound

This paper cites Spectral networks and deep locally connected networks on graphs,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction Spectral networks and deep locally connected networks on graphs,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:59.390154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c2b7af68-bbb1-4f90-a9bb-fe924925e9af · outbound

This paper cites Hydrodynamic atmospheric escape in HD 189733 b: Signatures of carbon and hydrogen measured with the Hubble Space Telescope.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction Hydrodynamic atmospheric escape in HD 189733 b: Signatures of carbon and hydrogen measured with the Hubble Space Telescope

Reference 11

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T13:38:50.556590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation fb2a0c9e-b1ea-4fd8-bc75-0d8831067bb4 · outbound

This paper cites Multimodal fusion: Foundations, trends, and challenges,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction Multimodal fusion: Foundations, trends, and challenges,

Reference 12

Resolution: verified exact; doi, observed 2026-08-06T13:38:48.360046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation cb5daca9-62be-4a58-973b-ffe76186114c · outbound

This paper cites Cross-modal learning: Architectures and applications,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction Cross-modal learning: Architectures and applications,

Reference 13

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T13:38:50.273043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation fce3f736-14cf-43e9-9b67-451720968f61 · outbound

This paper cites Spatio -temporal attention networks for traffic flow forecasting,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction Spatio -temporal attention networks for traffic flow forecasting,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T13:38:42.953288Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15b23775-19f1-4c3d-a9ac-61ce593d0b2a · outbound

This paper cites A Discrete Particle Swarm Optimizer for the Design of Cryptographic Boolean Functions.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction A Discrete Particle Swarm Optimizer for the Design of Cryptographic Boolean Functions

Reference 15

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T13:38:49.915652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8bbd4233-22fe-4cbc-90d6-939f86c8ca69 · outbound

This paper cites A multi -modal attention neural network for traffic flow prediction by capturing long -short term sequence correlation,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction A multi -modal attention neural network for traffic flow prediction by capturing long -short term sequence correlation,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:59.209301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 037accac-5b70-423c-9485-e4428b05ed5e · outbound

This paper cites CNN-based models for accident severity prediction using road imagery,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction CNN-based models for accident severity prediction using road imagery,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:59.052416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 74188300-3415-4559-b64f-636e221d15d9 · outbound

This paper cites LSTM networks for temporal dependencies in accident occurrence,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction LSTM networks for temporal dependencies in accident occurrence,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:58.841438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation dfb1ca88-3352-4b36-bbbc-07a272ac8bd6 · outbound

This paper cites Graph attention networks,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction Graph attention networks,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:58.700859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c98f65b8-bcf1-42d1-bc82-14b816e9cdb3 · outbound

This paper cites Graph convolutional networks for accident prediction,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction Graph convolutional networks for accident prediction,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:58.550939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e262cf05-7698-4874-bae5-dd9e39c9d208 · outbound

This paper cites Accident hotspot identification using graph-based models,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction Accident hotspot identification using graph-based models,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:58.244338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2d32a5ca-c4c6-4282-8548-64817f9054b0 · outbound

This paper cites Road network simplification preserving topology,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction Road network simplification preserving topology,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:57.856968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 398e2962-5417-4b9f-8dde-1d018d2f9c9b · outbound

This paper cites A density -based algorithm for discovering clusters in large spatial databases with noise,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction A density -based algorithm for discovering clusters in large spatial databases with noise,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:57.530045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation cea3ccb9-1040-4dee-bb73-1e7401ac0964 · outbound

This paper cites The network analysis of urban streets: A dual approach,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction The network analysis of urban streets: A dual approach,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:57.372563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b419c35f-7fe6-4857-8459-e97c56f6bccf · outbound

This paper cites Applying social network analysis to road networks,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction Applying social network analysis to road networks,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:57.148286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9f1104c7-3aee-4955-9edf-ece67c92569c · outbound

This paper cites Algebraic connectivity of graphs,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction Algebraic connectivity of graphs,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:56.959868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 80eaf5ad-aa17-4831-beea-7e443e7abdf1 · outbound

This paper cites Feature engineering for temporal data: Methodologies and industrial applications,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction Feature engineering for temporal data: Methodologies and industrial applications,

Reference 27

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T13:38:49.551969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d09ec8a8-3354-413d-85c3-46b9de3adf9b · outbound

This paper cites Inductive representation learning on large graphs,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction Inductive representation learning on large graphs,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:56.705515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8dd43554-3f12-4947-a34d-c6a75ac33aaf · outbound

This paper cites Deep sparse rectifier neural networks,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction Deep sparse rectifier neural networks,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:56.421156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9f2ce125-4f3f-45d9-b475-5b7877d1afa9 · outbound

This paper cites Deep residual learning for image recognition,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction Deep residual learning for image recognition,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:56.194377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 61f06e09-8d06-4e8a-934a-653c4b2628fb · outbound

This paper cites Layer Normalization.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction Layer Normalization

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T13:38:45.328723Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ccda74fe-3352-4d49-83ac-36699fdabcfe · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction Batch normalization: Accelerating deep network training by reducing internal covariate shift,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:55.965597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation acfb9996-ebc7-43d4-a329-b22b2121b029 · outbound

This paper cites Attention is all you need,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction Attention is all you need,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:55.753312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 30bb5386-d840-49ce-b2e6-c7cd1420325c · outbound

This paper cites Dropout: A simple way to prevent neural networks from overfitting,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction Dropout: A simple way to prevent neural networks from overfitting,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:55.606734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e8d8ca17-1472-492c-9e69-951aebde5d2e · outbound

This paper cites Focal loss for dense object detection,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction Focal loss for dense object detection,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:55.340010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ed356072-ccce-4aa6-913e-e7781737274c · outbound

This paper cites Decoupled weight decay regularization,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction Decoupled weight decay regularization,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:55.045755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 201ef1e6-b40f-49f4-a9c7-0b0635ee3b23 · outbound

This paper cites SGDR: Stochastic gradient descent with warm restarts,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction SGDR: Stochastic gradient descent with warm restarts,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:54.734795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 756a4921-a21e-4213-a7bf-2933f16dfd4f · outbound

This paper cites On the difficulty of training recurrent neural networks,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction On the difficulty of training recurrent neural networks,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:54.484281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 92ab523c-f1a1-4815-8268-8b6ec461e5a9 · outbound

This paper cites Fatality Analysis Reporting System (FARS),.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction Fatality Analysis Reporting System (FARS),

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:54.245490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c256dfce-8eb7-43a1-84c1-dea1976cfc1e · outbound

This paper cites Bangladesh Road Accident Database,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction Bangladesh Road Accident Database,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:54.020776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c2d24199-f16b-4567-8bd8-caff9ccf7287 · outbound

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

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction Graph neural networks: A review of methods and applications,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:53.743014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation de6d71fa-769e-4c99-b18a-61612302cea0 · outbound

This paper cites Spatial -temporal synchronous graph convolutional networks for traffic forecasting,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction Spatial -temporal synchronous graph convolutional networks for traffic forecasting,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:53.452794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 952b40f1-d61d-46f1-a66d-d9d8b304f5e2 · outbound

This paper cites Tuning transduction from hidden observables to optimize information harvesting.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction Tuning transduction from hidden observables to optimize information harvesting

Reference 43

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T13:38:49.224745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7812d9f2-8219-4eb7-b3b3-a94dbd1c49f0 · outbound

This paper cites A spatial-temporal graph gated transformer for traffic forecasting,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction A spatial-temporal graph gated transformer for traffic forecasting,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:53.175965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation be74cbe8-fc8e-40eb-9a6f-ac53d9480849 · outbound

This paper cites RELEAD: Resilient Localization with Enhanced LiDAR Odometry in Adverse Environments.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction RELEAD: Resilient Localization with Enhanced LiDAR Odometry in Adverse Environments

Reference 45

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T13:38:48.964692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation eebdb00c-0a63-4b4f-9424-e3cab3660684 · outbound

This paper cites Evaluation: From precision, recall and F -measure to ROC, informedness, markedness and correlation,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction Evaluation: From precision, recall and F -measure to ROC, informedness, markedness and correlation,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:52.829331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 802f7cce-e8eb-4685-ae3b-0ca428a8cfae · outbound

This paper cites A systematic analysis of performance measures for classification tasks,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction A systematic analysis of performance measures for classification tasks,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:52.526592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7d4d52d4-51e5-4b95-96c7-e033c03ef7fa · outbound

This paper cites The balanced accuracy and its posterior distribution,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction The balanced accuracy and its posterior distribution,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:52.256531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2c9716ca-630b-46ce-b8bf-ec218e4bedde · outbound

This paper cites The precision -recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction The precision -recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:51.934804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1dee517d-5cd3-40da-a144-f1302271db79 · outbound

This paper cites An introduction to ROC analysis,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction An introduction to ROC analysis,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:51.688972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ff6b18a3-f9d5-452f-bc8e-95bed01273d5 · outbound

This paper cites A coefficient of agreement for nominal scales,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction A coefficient of agreement for nominal scales,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:51.425939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b3a722e7-86c6-471e-a586-19429b45ef8d · outbound

This paper cites Public crash databases: Bias assessment and enhancement strategies,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction Public crash databases: Bias assessment and enhancement strategies,

Reference 52

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T13:38:48.659465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c5330b71-b10d-46ae-9838-572147169a09 · outbound

This paper cites A study of cross -validation and bootstrap for accuracy estimation and model selection,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction A study of cross -validation and bootstrap for accuracy estimation and model selection,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:51.182800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c12d3036-378a-4e72-bfbb-b8d2e60840b7 · outbound

This paper cites Multiple comparisons among means,.

STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction Multiple comparisons among means,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:38:50.825502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers