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Paper Citation Record · LEDGER

DG-Trans: Dual-level Graph Transformer for Spatiotemporal Incident Impact Prediction on Traffic Networks

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2303.12238.

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

pith.paper-citation-record.v1
2303.12238 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T17:54:46.768866Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T19:43:30.792666Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation ca7d43eb-55f4-4ed4-bf90-8e0c9ebe2fe0 · inbound

Multi-Agent Reinforcement Learning in Wireless Distributed Networks for 6G cites this paper.

Multi-Agent Reinforcement Learning in Wireless Distributed Networks for 6G DG-Trans: Dual-level Graph Transformer for Spatiotemporal Incident Impact Prediction on Traffic Networks

Reference 225

Resolution
unresolved
no resolver link, observed 2026-08-08T17:54:46.768866Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T17:54:46.768866Z digest=sha256:1c47cc0d9beeacdf3d2746185e78a03959067776fe708b077b3deb4980ab9dda

Observation 23a417de-8700-466d-9775-da67279bd9fd · inbound

Application and Evaluation of Large Language Models for Forecasting the Impact of Traffic Incidents cites this paper.

Application and Evaluation of Large Language Models for Forecasting the Impact of Traffic Incidents DG-Trans: Dual-level Graph Transformer for Spatiotemporal Incident Impact Prediction on Traffic Networks

Reference 2

Resolution
verified exact
local_arxiv, observed 2026-08-06T19:43:30.865039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T19:43:28.417458Z digest=sha256:aff2fda6db1f1de80be4af87b8054aff2c3109138825136f88e44b2a86998a72