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Paper Citation Record · LEDGER

TPLLM: A Traffic Prediction Framework Based on Pretrained Large Language Models

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2403.02221.

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

pith.paper-citation-record.v1
2403.02221 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T19:18:01.544592Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T13:35:46.203120Z

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 b65d7f28-5dd6-46dc-8130-b2b1317175b5 · inbound

Large Language Models (LLMs) as Traffic Control Systems at Urban Intersections: A New Paradigm cites this paper.

Large Language Models (LLMs) as Traffic Control Systems at Urban Intersections: A New Paradigm TPLLM: A Traffic Prediction Framework Based on Pretrained Large Language Models

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-12T19:18:01.544592Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T19:18:01.544592Z digest=sha256:05af157b8a39bee8434031623fe9d9ff2863d8441dc5dce85d2959905639a32b

Observation 6e7f3939-4f6f-484a-b383-0c93541138ac · inbound

TransCompressor: LLM-Powered Multimodal Data Compression for Smart Transportation cites this paper.

TransCompressor: LLM-Powered Multimodal Data Compression for Smart Transportation TPLLM: A Traffic Prediction Framework Based on Pretrained Large Language Models

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-12T13:40:45.856309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:40:45.856309Z digest=sha256:accf856690e2c186a176b1613d2265a8a7f8a9a835ac10de065095d801c00ca0

Observation 616f027e-1a8c-4eda-8e16-7861928344ad · inbound

BIGCity: A Universal Spatiotemporal Model for Unified Trajectory and Traffic State Data Analysis cites this paper.

BIGCity: A Universal Spatiotemporal Model for Unified Trajectory and Traffic State Data Analysis TPLLM: A Traffic Prediction Framework Based on Pretrained Large Language Models

Reference 81

Resolution
unresolved
no resolver link, observed 2026-08-12T04:54:41.145799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T04:54:41.145799Z digest=sha256:6c99a3335a00e1f500a5baeed5d4b8264c92bedc70c825e95ae41e0ac098357f

Observation 9612d458-2ebf-4a0d-8a8b-b21f847e6b96 · inbound

Embracing Large Language Models in Traffic Flow Forecasting cites this paper.

Embracing Large Language Models in Traffic Flow Forecasting TPLLM: A Traffic Prediction Framework Based on Pretrained Large Language Models

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-11T15:25:05.018601Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T15:25:05.018601Z digest=sha256:fbd6bd1c95bb9725c03e759ddf9cbc83c62825d0fc8fcf0c23c8276e5a28ecb9

Observation 088bc2e7-02fd-483a-b242-cc05d8d8fefe · inbound

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

Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directions TPLLM: A Traffic Prediction Framework Based on Pretrained Large Language Models

Reference 131

Resolution
unresolved
no resolver link, observed 2026-08-10T21:37:04.648163Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T21:37:04.648163Z digest=sha256:d673d496342da70f722b525022c49e44d823ed9bc8912e55b580323fe999c871

Observation 15e5ece3-1a65-4325-966e-4d829c9a3a67 · inbound

Unraveling Spatio-Temporal Foundation Models via the Pipeline Lens: A Comprehensive Review cites this paper.

Unraveling Spatio-Temporal Foundation Models via the Pipeline Lens: A Comprehensive Review TPLLM: A Traffic Prediction Framework Based on Pretrained Large Language Models

Reference 184

Resolution
unresolved
no resolver link, observed 2026-08-07T11:49:47.176124Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:49:47.176124Z digest=sha256:312bd85efea8c414054e8e626109ef9e901a9cd63c53a14e6b166fcacbeba493

Observation 98bc831e-3e03-46d0-8aa6-1761515dcb7d · inbound

From Time Series Analysis to Question Answering: A Survey in the LLM Era cites this paper.

From Time Series Analysis to Question Answering: A Survey in the LLM Era TPLLM: A Traffic Prediction Framework Based on Pretrained Large Language Models

Reference 86

Resolution
verified exact
arxiv_id, observed 2026-05-19T09:32:15.618490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-19T09:31:55.829045Z digest=sha256:4f772f87d044fd1a920c2c0e78083d651e8fca1bc4ea28c725bf3b947d1c0799

Observation 09e772be-a0eb-467e-9270-b0681cc4fc05 · inbound

Earth Science Foundation Models: From Perception to Reasoning and Discovery cites this paper.

Earth Science Foundation Models: From Perception to Reasoning and Discovery TPLLM: A Traffic Prediction Framework Based on Pretrained Large Language Models

Reference 225

Resolution
verified exact
arxiv_id, observed 2026-05-14T22:08:03.359088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-14T22:07:40.242567Z digest=sha256:64de65af3c59d15fe52f1ecbd3de16cda21a55c344fb61c021bb2017a2005819

Observation 8640368d-7c48-4302-a195-0f470d84a4f9 · inbound

Earth Science Foundation Models: From Perception to Reasoning and Discovery cites this paper.

Earth Science Foundation Models: From Perception to Reasoning and Discovery TPLLM: A Traffic Prediction Framework Based on Pretrained Large Language Models

Reference 225

Resolution
verified exact
arxiv_id, observed 2026-07-01T13:35:46.204825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-30T23:07:21.558834Z digest=sha256:7677bc7ffd815f0e429cdc0ae736086d853e850f83ce096049929523347dc1d4