Paper Citation Record · LEDGER

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study

As of 23 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 2 inbound Pith citation observations for arXiv:2501.03904.

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

pith.paper-citation-record.v1
2501.03904 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:47:58.917652Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:22:46.493635Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T20:56:14.119564Z

Reference resolution

38 of 38 outbound references displayed

verified exact0
verified fuzzy28
unresolved10
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8dfe1069-6f1f-4ba2-812b-1d84d0743ba8 · outbound

This paper cites Adversarial nlp for social network applications: Attacks, defenses, and research directions,.

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study Adversarial nlp for social network applications: Attacks, defenses, and research directions,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:47:59.570276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation cc0887c9-9840-41af-819c-664f6dfc6efe · outbound

This paper cites A deep learning ensemble approach to detecting unknown network attacks,.

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study A deep learning ensemble approach to detecting unknown network attacks,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:47:59.553196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 89d64ed4-bc68-40e1-b2ff-0804d3f717c3 · outbound

This paper cites Enhancing neural text detector robustness with µ attacking and rr-training,.

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study Enhancing neural text detector robustness with µ attacking and rr-training,

Reference 3

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Observation 3acaffc9-81fd-4b96-8189-46020b14c8f3 · outbound

This paper cites Joint 2d-3d breast cancer classification,.

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study Joint 2d-3d breast cancer classification,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:47:59.526160Z

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Observation 28500a8f-8669-4c29-8989-61b95540889f · outbound

This paper cites Self-supervised learning application on covid-19 chest x-ray image classification using masked autoencoder,.

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study Self-supervised learning application on covid-19 chest x-ray image classification using masked autoencoder,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:47:59.510806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 14f59ad6-094c-4a34-b3e4-0367dacccf17 · outbound

This paper cites Simulated quantum mechanics-based joint learning network for stroke lesion segmentation and tici grading,.

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study Simulated quantum mechanics-based joint learning network for stroke lesion segmentation and tici grading,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:47:59.494131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d1b3e50f-c6ad-4ae1-8e84-62e8f7c93843 · outbound

This paper cites Applications of deep machine learning to highway safety and usage assessment,.

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study Applications of deep machine learning to highway safety and usage assessment,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:47:59.477694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6430757d-3929-4e47-a87c-122d4105265e · outbound

This paper cites U2-net: A very-deep convolutional neural network for detecting distracted drivers,.

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study U2-net: A very-deep convolutional neural network for detecting distracted drivers,

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b98f74b5-8409-4c49-bb54-ead447f7e357 · outbound

This paper cites Unveiling roadway hazards: Enhancing fatal crash risk estimation through multiscale satellite imagery and self-supervised cross-matching,.

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study Unveiling roadway hazards: Enhancing fatal crash risk estimation through multiscale satellite imagery and self-supervised cross-matching,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:47:59.443425Z

Source-reported events for the cited work

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Observation b4013c54-40f5-40ba-adcf-00598c961975 · outbound

This paper cites Introducing chatgpt,.

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study Introducing chatgpt,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:47:59.427068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 55a3ae87-f503-4485-a6a6-424ec39b5a98 · outbound

This paper cites GPT-4 Technical Report.

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study GPT-4 Technical Report

Reference 11

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Source-reported events for the cited work

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Observation bda58121-ecb2-4643-a405-2b234c4941de · outbound

This paper cites Study and analysis of chat gpt and its impact on different fields of study,.

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study Study and analysis of chat gpt and its impact on different fields of study,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:47:59.411764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation cac2f785-cbf8-45b8-b3b8-573849ad1513 · outbound

This paper cites Exploring gpt-4’s characteristics through the 5vs of big data: A brief perspective,.

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study Exploring gpt-4’s characteristics through the 5vs of big data: A brief perspective,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:47:59.396264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5f507474-fd3b-46ec-a8de-b9b221441932 · outbound

This paper cites Human-like problem-solving abilities in large language models using chatgpt,.

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study Human-like problem-solving abilities in large language models using chatgpt,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:47:59.380837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation efceb961-b3f2-497d-93b2-65173333a7c4 · outbound

This paper cites Integrating gpt-technologies with decision models for explainability,.

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study Integrating gpt-technologies with decision models for explainability,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:47:59.365262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 94b5fcf7-7d4a-468c-94dd-905904c20af8 · outbound

This paper cites Revolutionizing neurosurgery with gpt-4: a leap forward or ethical conundrum?.

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study Revolutionizing neurosurgery with gpt-4: a leap forward or ethical conundrum?

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:47:59.349718Z

Source-reported events for the cited work

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Observation 951211d9-58cf-4e54-896a-1f195e625884 · outbound

This paper cites Sustainable mass transit: Challenges and opportunities in urban public transportation,.

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study Sustainable mass transit: Challenges and opportunities in urban public transportation,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:47:59.333102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fb2d2a96-461e-4169-bbe1-abc90a7f4dda · outbound

This paper cites Urban smart public transport studies: a review and prospect,.

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study Urban smart public transport studies: a review and prospect,

Reference 18

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation da8d62fb-2c28-4440-a26c-52080d33eaa2 · outbound

This paper cites A systematic overview of transportation equity in terms of accessibility, traffic emissions, and safety outcomes: From conventional to emerging technologies,.

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study A systematic overview of transportation equity in terms of accessibility, traffic emissions, and safety outcomes: From conventional to emerging technologies,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:47:59.299668Z

Source-reported events for the cited work

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Observation f7d557d4-6530-40e1-a0ed-8c175efa7a11 · outbound

This paper cites Agent-based optimizing match between passenger demand and service supply for urban rail transit network with netlogo,.

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study Agent-based optimizing match between passenger demand and service supply for urban rail transit network with netlogo,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:47:59.281471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation efd822d0-9095-4334-aa59-f601f9c78b78 · outbound

This paper cites Flexible route optimization for demand-responsive public transit service,.

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study Flexible route optimization for demand-responsive public transit service,

Reference 21

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Source-reported events for the cited work

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Observation 6c15b49a-f8ff-4f45-95c5-28e6ec6ffad5 · outbound

This paper cites To thrive, san antonio must enhance, redefine transportation,.

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study To thrive, san antonio must enhance, redefine transportation,

Reference 22

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation dfa6957f-a8d7-4721-8c8f-7ca9aa58b3ec · outbound

This paper cites San antonio needs to fund public transit,.

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study San antonio needs to fund public transit,

Reference 23

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 333f3f04-3cfb-4b1a-a99e-072b88be44ca · outbound

This paper cites Plato Dialogue System: A Flexible Conversational AI Research Platform.

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study Plato Dialogue System: A Flexible Conversational AI Research Platform

Reference 24

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Source-reported events for the cited work

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Observation f825f7b8-b7b6-497a-a9d1-7d8139e46143 · outbound

This paper cites Gpt (generative pre-trained transformer)–a comprehensive review on enabling technologies, potential applications, emerging challenges, and future directions,.

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study Gpt (generative pre-trained transformer)–a comprehensive review on enabling technologies, potential applications, emerging challenges, and future directions,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:47:59.207012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d16a1963-e7eb-4150-9a76-ea6c903f1e5a · outbound

This paper cites Bus bunching and bus bridging: What can we learn from generative ai tools like chatgpt?.

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study Bus bunching and bus bridging: What can we learn from generative ai tools like chatgpt?

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:47:59.190469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0e438fe1-95ad-41ac-b338-72699b21fbcd · outbound

This paper cites Advanced learning technolo- gies for intelligent transportation systems: Prospects and challenges,.

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study Advanced learning technolo- gies for intelligent transportation systems: Prospects and challenges,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:47:59.172680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 37690ebe-adf5-4bb8-990f-2e00c133cfe4 · outbound

This paper cites The Role of LLMs in Sustainable Smart Cities: Applications, Challenges, and Future Directions.

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study The Role of LLMs in Sustainable Smart Cities: Applications, Challenges, and Future Directions

Reference 28

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Observation d11c25a7-87c1-481b-b95a-07a1c0fc7991 · outbound

This paper cites From Text to Transformation: A Comprehensive Review of Large Language Models' Versatility.

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study From Text to Transformation: A Comprehensive Review of Large Language Models' Versatility

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-10T21:47:58.870031Z

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Observation 64fb6eae-9de2-4c86-a1c3-9d6292f442aa · outbound

This paper cites TrafficSafetyGPT: Tuning a Pre-trained Large Language Model to a Domain-Specific Expert in Transportation Safety.

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study TrafficSafetyGPT: Tuning a Pre-trained Large Language Model to a Domain-Specific Expert in Transportation Safety

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-10T21:47:58.875269Z

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Observation 4fb60f01-6bce-4cc2-9b82-053d605e24c5 · outbound

This paper cites Chatgpt for gtfs: benchmarking llms on gtfs semantics and retrieval,.

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study Chatgpt for gtfs: benchmarking llms on gtfs semantics and retrieval,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:47:59.155669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 291572c5-9def-4408-9952-44354198032e · outbound

This paper cites ChatGPT is on the Horizon: Could a Large Language Model be Suitable for Intelligent Traffic Safety Research and Applications?.

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study ChatGPT is on the Horizon: Could a Large Language Model be Suitable for Intelligent Traffic Safety Research and Applications?

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-10T21:47:58.885261Z

Source-reported events for the cited work

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This paper cites Attention is all you need,.

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study Attention is all you need,

Reference 33

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd389775-d3df-4579-9d4b-ebee5c299b5a · outbound

This paper cites Improving language understanding by generative pre-training,.

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study Improving language understanding by generative pre-training,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-10T21:47:58.897371Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43e3f310-fc62-4bb8-81c3-b4b23900d7b3 · outbound

This paper cites Better language models and their implications,.

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study Better language models and their implications,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:47:59.119015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 24dfe6df-a2ee-4246-8d27-d84ef6bdcdc1 · outbound

This paper cites Language models are few-shot learners,.

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study Language models are few-shot learners,

Reference 36

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Observation aea2cbcb-04b0-4c93-93a5-eddbf072cc63 · outbound

This paper cites An introduction to large language models: Prompt engineering and p-tuning,.

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study An introduction to large language models: Prompt engineering and p-tuning,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:47:59.091318Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 110b8783-e170-4484-aa19-2f64ebab3081 · outbound

This paper cites What is prompt engineering?.

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study What is prompt engineering?

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:47:59.070054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

Observation bfef07c4-bdc4-4f2b-a49a-23c9e007ef0f · inbound

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis cites this paper.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-15T23:22:46.493635Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c7efa47-eab6-4cfa-a710-7fee97ca8c70 · inbound

Large Language Models in Transportation Systems Management and Operations: From Text Reasoning to Multi-modal Decision Support cites this paper.

Large Language Models in Transportation Systems Management and Operations: From Text Reasoning to Multi-modal Decision Support Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-07-01T20:56:14.121230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.