Paper Citation Record · LEDGER

Application and Evaluation of Large Language Models for Forecasting the Impact of Traffic Incidents

As of 22 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2507.04803.

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

pith.paper-citation-record.v1
2507.04803 v1

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:43:30.468485Z

measured 18 of 18 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 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

18 of 18 outbound references displayed

verified exact2
verified fuzzy16
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b25a6c64-202c-4eed-b17e-089d043bd8f1 · outbound

This paper cites Overview of traffic incident duration analysis and prediction,.

Application and Evaluation of Large Language Models for Forecasting the Impact of Traffic Incidents Overview of traffic incident duration analysis and prediction,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:43:33.219612Z

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 23a417de-8700-466d-9775-da67279bd9fd · outbound

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

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 2026-08-22T06:32:14.747728+00:00.

Observation a5ecdac0-b37a-4823-955f-03cf70029620 · outbound

This paper cites Predicting the Impact of Traffic Incidents: An Evaluative Analysis,.

Application and Evaluation of Large Language Models for Forecasting the Impact of Traffic Incidents Predicting the Impact of Traffic Incidents: An Evaluative Analysis,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:43:32.992260Z

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 de235234-950a-4cef-94a7-aedd295de3b5 · outbound

This paper cites Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directions,.

Application and Evaluation of Large Language Models for Forecasting the Impact of Traffic Incidents Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directions,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:43:32.828543Z

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 6e86cc5e-02b3-47ee-b252-f37dd342df89 · outbound

This paper cites Arterial incident duration prediction using a bi-level framework of extreme gradient-tree boosting.

Application and Evaluation of Large Language Models for Forecasting the Impact of Traffic Incidents Arterial incident duration prediction using a bi-level framework of extreme gradient-tree boosting

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-08-06T19:43:30.710216Z

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 f3bdf0de-0184-4f6e-9770-82bcfe99314c · outbound

This paper cites A Data -Driven Approach to Estimate and Predict the Traffic Incidents’ Queue Length,.

Application and Evaluation of Large Language Models for Forecasting the Impact of Traffic Incidents A Data -Driven Approach to Estimate and Predict the Traffic Incidents’ Queue Length,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:43:32.694288Z

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 a2359ba3-2c76-4936-a1e2-0bb876c3acf6 · outbound

This paper cites Text analysis in incident duration prediction,.

Application and Evaluation of Large Language Models for Forecasting the Impact of Traffic Incidents Text analysis in incident duration prediction,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:43:32.584393Z

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 2e544b0b-49c4-4adb-a49a-58b8d3c85faa · outbound

This paper cites Mining traffic incidents to forecast impact,.

Application and Evaluation of Large Language Models for Forecasting the Impact of Traffic Incidents Mining traffic incidents to forecast impact,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:43:32.359892Z

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 512b458e-a766-4164-8b26-4c5c5db688c2 · outbound

This paper cites Traffic accident duration prediction using text mining and ensemble learning on expressways,.

Application and Evaluation of Large Language Models for Forecasting the Impact of Traffic Incidents Traffic accident duration prediction using text mining and ensemble learning on expressways,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:43:32.220803Z

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 96b4fb25-1b45-4b8b-a276-eb33ec3763e2 · outbound

This paper cites Traffic incident duration prediction using BERT representation of text,.

Application and Evaluation of Large Language Models for Forecasting the Impact of Traffic Incidents Traffic incident duration prediction using BERT representation of text,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:43:32.101271Z

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 91e2c0cb-06f9-4daa-bb0a-9308d9214d95 · outbound

This paper cites Enhancing Traffic Incident Management with Large Language Models: A Hybrid Machine Learning Approach for Severity Classification,.

Application and Evaluation of Large Language Models for Forecasting the Impact of Traffic Incidents Enhancing Traffic Incident Management with Large Language Models: A Hybrid Machine Learning Approach for Severity Classification,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:43:31.949451Z

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 62f036e0-18a3-456c-a1cd-9b4f39272fc6 · outbound

This paper cites A methodological approach for estimating temporal and spatial extent of delays caused by freeway accidents,.

Application and Evaluation of Large Language Models for Forecasting the Impact of Traffic Incidents A methodological approach for estimating temporal and spatial extent of delays caused by freeway accidents,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:43:31.845585Z

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 b19c9357-91d9-4f01-b5d8-f7a60f571f39 · outbound

This paper cites Forecasting spatiotemporal impact of traffic incidents on road networks,.

Application and Evaluation of Large Language Models for Forecasting the Impact of Traffic Incidents Forecasting spatiotemporal impact of traffic incidents on road networks,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:43:31.691902Z

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 09de38ce-d241-4f6f-9bc1-3f9e73e8dc94 · outbound

This paper cites Data Curation Alone Can Stabilize In-context Learning,.

Application and Evaluation of Large Language Models for Forecasting the Impact of Traffic Incidents Data Curation Alone Can Stabilize In-context Learning,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:43:31.567070Z

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 2570ec72-a6e5-46ff-bad2-cb3d291f5194 · outbound

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

Application and Evaluation of Large Language Models for Forecasting the Impact of Traffic Incidents Diffusion Convolutional Recurrent Neural Network: Data -Driven Traffic Forecasting,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:43:31.474309Z

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 f69f3c72-fbb3-4dc7-81fd-cfdd92d975aa · outbound

This paper cites (30 April 2025).

Application and Evaluation of Large Language Models for Forecasting the Impact of Traffic Incidents (30 April 2025)

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:43:31.366599Z

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 89a048a4-a075-4fc1-aa21-793aa14f7407 · outbound

This paper cites A comparative study of machine learning algorithms to predict road accident severity,.

Application and Evaluation of Large Language Models for Forecasting the Impact of Traffic Incidents A comparative study of machine learning algorithms to predict road accident severity,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:43:31.111210Z

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 7f3ad2de-9ef4-45bf-b4df-7221adb8fdd7 · outbound

This paper cites Robust Prompt Optimization for Large Language Models Against Distribution Shifts,.

Application and Evaluation of Large Language Models for Forecasting the Impact of Traffic Incidents Robust Prompt Optimization for Large Language Models Against Distribution Shifts,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:43:30.986010Z

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.

Pith citing papers

No inbound Pith citation observations are available.