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Paper Citation Record · LEDGER

Network-Wide Traffic Volume Estimation from Speed Profiles using a Spatio-Temporal Graph Neural Network with Directed Spatial Attention

As of 22 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 2 inbound Pith citation observations for arXiv:2512.13758.

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

pith.paper-citation-record.v1
2512.13758 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T16:29:50.179816Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-31T23:12:17.759955Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T20:03:56.415449Z

Reference resolution

25 of 25 outbound references displayed

  • verified exact1
  • verified fuzzy0
  • unresolved22
  • parse uncertain0
  • malformed identifier2
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 70c92e0e-8a0f-4f1a-a3a9-74f951ccefda · outbound

This paper cites Europe-wide high-spatial resolution air pollution models are improved by including traffic flow estimates on all roads,.

Network-Wide Traffic Volume Estimation from Speed Profiles using a Spatio-Temporal Graph Neural Network with Directed Spatial Attention Europe-wide high-spatial resolution air pollution models are improved by including traffic flow estimates on all roads,

Reference 1

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Observation b941f236-91f7-4ded-80cd-0b7cda6429bb · outbound

This paper cites Empirical macroscopic fundamental diagrams: New insights from loop detector and floating car data,.

Network-Wide Traffic Volume Estimation from Speed Profiles using a Spatio-Temporal Graph Neural Network with Directed Spatial Attention Empirical macroscopic fundamental diagrams: New insights from loop detector and floating car data,

Reference 2

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doi, observed 2026-08-03T16:33:28.461092Z

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

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Observation 5c7c4f2f-3405-4094-b8d2-a804666b8288 · outbound

This paper cites Estimating traffic flow rate on freeways from probe vehicle data and fundamental diagram,.

Network-Wide Traffic Volume Estimation from Speed Profiles using a Spatio-Temporal Graph Neural Network with Directed Spatial Attention Estimating traffic flow rate on freeways from probe vehicle data and fundamental diagram,

Reference 3

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Observation e025e9b9-dc00-4660-a720-2a545b1835e7 · outbound

This paper cites Traffic flow estimation using probe vehicle data,.

Network-Wide Traffic Volume Estimation from Speed Profiles using a Spatio-Temporal Graph Neural Network with Directed Spatial Attention Traffic flow estimation using probe vehicle data,

Reference 4

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Observation 96ef0449-fa02-4d40-af89-5a9aff4f7505 · outbound

This paper cites Multi-models machine learning methods for traffic flow estimation from Floating Car Data,.

Network-Wide Traffic Volume Estimation from Speed Profiles using a Spatio-Temporal Graph Neural Network with Directed Spatial Attention Multi-models machine learning methods for traffic flow estimation from Floating Car Data,

Reference 5

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Observation 3b7f1fa3-6c64-44ed-b7cd-94371175a2f0 · outbound

This paper cites Network topological ef- fects on the macroscopic fundamental diagram,.

Network-Wide Traffic Volume Estimation from Speed Profiles using a Spatio-Temporal Graph Neural Network with Directed Spatial Attention Network topological ef- fects on the macroscopic fundamental diagram,

Reference 6

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source=pdf_text observed=2026-08-03T16:29:48.075326Z digest=sha256:5162d495a40a3ab36020ce9791a1f22e53ca9cf8b528a64e82921aedb1705397

Observation c5b55267-9ac5-43d8-a809-837b6fcee7ef · outbound

This paper cites DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction.

Network-Wide Traffic Volume Estimation from Speed Profiles using a Spatio-Temporal Graph Neural Network with Directed Spatial Attention DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction

Reference 7

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source=pdf_text observed=2026-08-03T16:29:48.203255Z digest=sha256:ed0feb470da9853e89bf48332ad786042ea1041862b9fe643326a6fab965437b

Observation c3b06625-0e82-4b54-ac7d-81c9973b1987 · outbound

This paper cites Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey.

Network-Wide Traffic Volume Estimation from Speed Profiles using a Spatio-Temporal Graph Neural Network with Directed Spatial Attention Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey

Reference 8

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source=pdf_text observed=2026-08-03T16:29:48.283500Z digest=sha256:5fde659811d232ad3dc734b2cad44f8545acac41fe9ad185bcc6f41d47fb324b

Observation ebc9366d-80fe-4be5-88dc-2b24c24aea76 · outbound

This paper cites Spatio-Temporal Graph Neural Networks: A Survey.

Network-Wide Traffic Volume Estimation from Speed Profiles using a Spatio-Temporal Graph Neural Network with Directed Spatial Attention Spatio-Temporal Graph Neural Networks: A Survey

Reference 9

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source=pdf_text observed=2026-08-03T16:29:48.446623Z digest=sha256:ac6f4967bab406b5068c60e13433c3130d542a94ef7de364d3a314231d475841

Observation 003e3d6f-534a-438d-b5cb-41b869270890 · outbound

This paper cites Evaluating the Generalization Ability of Spatiotemporal Model in Urban Scenario.

Network-Wide Traffic Volume Estimation from Speed Profiles using a Spatio-Temporal Graph Neural Network with Directed Spatial Attention Evaluating the Generalization Ability of Spatiotemporal Model in Urban Scenario

Reference 10

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Observation 94e451f5-c96f-454d-9436-432937793e55 · outbound

This paper cites Inductive representation learning on large graphs,.

Network-Wide Traffic Volume Estimation from Speed Profiles using a Spatio-Temporal Graph Neural Network with Directed Spatial Attention Inductive representation learning on large graphs,

Reference 11

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source=pdf_text observed=2026-08-03T16:29:48.714769Z digest=sha256:67d8474e637d679596f2c3c639b0bd9f31a941678479bc2297a5ec161feba764

Observation 89d1f2c1-66a5-43b5-b2ec-af357fcb3357 · outbound

This paper cites Comparison be- tween inductive and transductive learning in a real citation net- work using graph neural networks,.

Network-Wide Traffic Volume Estimation from Speed Profiles using a Spatio-Temporal Graph Neural Network with Directed Spatial Attention Comparison be- tween inductive and transductive learning in a real citation net- work using graph neural networks,

Reference 12

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source=pdf_text observed=2026-08-03T16:29:48.795069Z digest=sha256:cc0b3754f805813a0b5ae71d2666312b35581149d988aea2758bc4fc7d508b77

Observation d7a3225b-5d52-4e37-b6ba-3752390a543a · outbound

This paper cites Network-Wide Traffic Flow Estimation Across Multiple Cities with Global Open Multi-Source Data: A Large-Scale Case Study in Europe and North America.

Network-Wide Traffic Volume Estimation from Speed Profiles using a Spatio-Temporal Graph Neural Network with Directed Spatial Attention Network-Wide Traffic Flow Estimation Across Multiple Cities with Global Open Multi-Source Data: A Large-Scale Case Study in Europe and North America

Reference 13

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source=pdf_text observed=2026-08-03T16:29:48.894442Z digest=sha256:761220026e716748fddbd12cf6053edca480e022a083b2dfca66821ac066274f

Observation e221cb00-8466-4a98-9635-9fd99666fe54 · outbound

This paper cites Network-wide Freeway Traffic Estimation Using Sparse Sensor Data: A Dirichlet Graph Auto-Encoder Approach.

Network-Wide Traffic Volume Estimation from Speed Profiles using a Spatio-Temporal Graph Neural Network with Directed Spatial Attention Network-wide Freeway Traffic Estimation Using Sparse Sensor Data: A Dirichlet Graph Auto-Encoder Approach

Reference 14

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source=pdf_text observed=2026-08-03T16:29:49.022075Z digest=sha256:145e82ad944b1be2c0c28dd76ba4e81d1052c80d556a2006eb705a4d8b244616

Observation 34afb4a3-30ab-4f36-868f-ee73f09b584c · outbound

This paper cites Urban Network-Wide Traffic V olume Estimation Under Sparse De- ployment of Detectors,.

Network-Wide Traffic Volume Estimation from Speed Profiles using a Spatio-Temporal Graph Neural Network with Directed Spatial Attention Urban Network-Wide Traffic V olume Estimation Under Sparse De- ployment of Detectors,

Reference 15

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source=pdf_text observed=2026-08-03T16:29:49.142064Z digest=sha256:fc6c055fffbd85d7bed4ad60fd9f35f4e80a21746ce68aa690eeace39017fd19

Observation 719f0d53-4af7-458a-b3ed-4168f24436e3 · outbound

This paper cites Network-Wide Traffic Flow Estimation with Insufficient V olume Detection and Crowdsourcing Data,.

Network-Wide Traffic Volume Estimation from Speed Profiles using a Spatio-Temporal Graph Neural Network with Directed Spatial Attention Network-Wide Traffic Flow Estimation with Insufficient V olume Detection and Crowdsourcing Data,

Reference 16

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source=pdf_text observed=2026-08-03T16:29:49.248126Z digest=sha256:f4213ae44f9e6134a3dbcd027f86e3095de4bf2f60907cb72f6f1ef1f5d31c90

Observation 2a77019e-107f-4d69-ba6e-7c452322237e · outbound

This paper cites Towards better traffic volume estimation: Jointly addressing the underdetermination and nonequilibrium problems with correlation-adaptive GNNs.

Network-Wide Traffic Volume Estimation from Speed Profiles using a Spatio-Temporal Graph Neural Network with Directed Spatial Attention Towards better traffic volume estimation: Jointly addressing the underdetermination and nonequilibrium problems with correlation-adaptive GNNs

Reference 17

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source=pdf_text observed=2026-08-03T16:29:49.357613Z digest=sha256:886e951763e0f2854a7944884e3be59340f9f43622b4c0711065ab070c6791e4

Observation a5e6d9b5-b525-4356-921f-1ae8c70af213 · outbound

This paper cites Graph attention networks,.

Network-Wide Traffic Volume Estimation from Speed Profiles using a Spatio-Temporal Graph Neural Network with Directed Spatial Attention Graph attention networks,

Reference 18

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source=pdf_text observed=2026-08-03T16:29:49.503351Z digest=sha256:a547311e9059def0663fe01ba04936993b0cf383c22faed94217ff696cf9028e

Observation 48b12f94-8fb6-4634-ba93-28418153e8da · outbound

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

Network-Wide Traffic Volume Estimation from Speed Profiles using a Spatio-Temporal Graph Neural Network with Directed Spatial Attention Semi-supervised classification with graph convolutional networks,

Reference 19

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source=pdf_text observed=2026-08-03T16:29:49.630886Z digest=sha256:a463a5c9a0aa464d607e72c17c460d2c3fb2dcc49f05c6da643aca9c778437ed

Observation 86284ec3-aa85-4b0e-b02e-f9df7beb162f · outbound

This paper cites Finite State Graphon Games with Applications to Epidemics.

Network-Wide Traffic Volume Estimation from Speed Profiles using a Spatio-Temporal Graph Neural Network with Directed Spatial Attention Finite State Graphon Games with Applications to Epidemics

Reference 20

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source=pdf_text observed=2026-08-03T16:29:49.741073Z digest=sha256:054c94a07fd3fea524e590b2cda7c60fbae45554a979a3b349af0b2cd695e121

Observation 14f7475a-0d9c-4d88-92cf-8b0af9cc5989 · outbound

This paper cites Representation Learning on Heterophilic Graph with Directional Neighborhood Attention.

Network-Wide Traffic Volume Estimation from Speed Profiles using a Spatio-Temporal Graph Neural Network with Directed Spatial Attention Representation Learning on Heterophilic Graph with Directional Neighborhood Attention

Reference 21

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source=pdf_text observed=2026-08-03T16:29:49.854892Z digest=sha256:3d425e9b7b089dda3cfbfd57790d71e2774fea871e5c9886e006e41cb892dce4

Observation bba09cb2-0c0b-4f5b-97e4-ca6f913d83fa · outbound

This paper cites Spatio-temporal Graph Con- volutional Networks: A Deep Learning Framework for Traffic Fore- casting.

Network-Wide Traffic Volume Estimation from Speed Profiles using a Spatio-Temporal Graph Neural Network with Directed Spatial Attention Spatio-temporal Graph Con- volutional Networks: A Deep Learning Framework for Traffic Fore- casting

Reference 22

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source=pdf_text observed=2026-08-03T16:29:49.958162Z digest=sha256:d60753737cae5f1a8f29e1ffc033c195b86e04e9f14825d57bfa9d1cf3f319f6

Observation c37783e0-3de5-4686-b0e0-8196f974bf0f · outbound

This paper cites Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting.

Network-Wide Traffic Volume Estimation from Speed Profiles using a Spatio-Temporal Graph Neural Network with Directed Spatial Attention Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting

Reference 23

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Observation 76acdc8e-a0b2-4b8b-99f9-1055fa37cc03 · outbound

This paper cites PeMS: California Freeway Traffic Data.

Network-Wide Traffic Volume Estimation from Speed Profiles using a Spatio-Temporal Graph Neural Network with Directed Spatial Attention PeMS: California Freeway Traffic Data

Reference 24

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source=pdf_text observed=2026-08-03T16:29:50.175636Z digest=sha256:f7337b626fe8494afc0ad1879ecfef0e0736e12670f67f16b41727759e6716ab

Observation 74b81f57-f258-47a0-9fc8-a17fb72ba2ec · outbound

This paper cites an unresolved cited work.

Network-Wide Traffic Volume Estimation from Speed Profiles using a Spatio-Temporal Graph Neural Network with Directed Spatial Attention Unresolved cited work

Reference 25

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source=pdf_text observed=2026-08-03T16:29:50.179816Z digest=sha256:249f3b59f7260ae0a3abcc63d5de5a2153ce4e24ead6516fc4952b776eb0a8b3

Pith citing papers

Observation 61f59801-9df2-45c1-bdea-b0973f1e53b1 · inbound

Selecting New Measurement Locations to Diversify Traffic-Pattern Coverage: A Real-World Evaluation for Total Traffic Volume Estimation cites this paper.

Selecting New Measurement Locations to Diversify Traffic-Pattern Coverage: A Real-World Evaluation for Total Traffic Volume Estimation Network-Wide Traffic Volume Estimation from Speed Profiles using a Spatio-Temporal Graph Neural Network with Directed Spatial Attention

Reference 22

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arxiv_id, observed 2026-07-28T02:23:22.451311Z

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.

source=pdf_text observed=2026-06-29T19:55:12.763334Z digest=sha256:fc986aff59f8f7cf8c3af4a0ed5159739d5044ed0a00f99da01324c00c1d77cd

Observation 0991e046-ed40-4ac9-ab67-f9484a48deaf · inbound

Capacity-Aware Deep Learning for Generalizable Traffic Volume Estimation Across Links and Cities cites this paper.

Capacity-Aware Deep Learning for Generalizable Traffic Volume Estimation Across Links and Cities Network-Wide Traffic Volume Estimation from Speed Profiles using a Spatio-Temporal Graph Neural Network with Directed Spatial Attention

Reference 11

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