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Paper Citation Record · LEDGER

Exploring Traffic Simulation and Cybersecurity Strategies Using Large Language Models

As of 18 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2506.16699.

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

pith.paper-citation-record.v1
2506.16699 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:41:31.481271Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

21 of 21 outbound references displayed

  • verified exact3
  • verified fuzzy12
  • unresolved6
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4cb40f7c-1695-4a7f-ac3b-0d239c431d02 · outbound

This paper cites En- hancing road safety and cybersecurity in traffic manage- ment systems: Leveraging the potential of reinforcement learning.IEEE Access, 12:9963–9975, 2024.

Exploring Traffic Simulation and Cybersecurity Strategies Using Large Language Models En- hancing road safety and cybersecurity in traffic manage- ment systems: Leveraging the potential of reinforcement learning.IEEE Access, 12:9963–9975, 2024

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:41:32.074172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T23:41:31.373735Z digest=sha256:7501e790d0819c85f946124f1cfe5cd48e03ffab49d9939ea7789063a417008f

Observation 5ccc3575-27d2-4ac4-b3fb-843340734620 · outbound

This paper cites Information fusion-based cybersecu- rity threat detection for intelligent transportation system.

Exploring Traffic Simulation and Cybersecurity Strategies Using Large Language Models Information fusion-based cybersecu- rity threat detection for intelligent transportation system

Reference 2

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verified fuzzy
raw_fallback, observed 2026-08-06T23:41:32.057240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T23:41:31.379577Z digest=sha256:4c8ec5a235a58dc7db7145aae62d3e5e7dd953951ae419b4632d1f52fb650b9e

Observation 87cbe2f1-a65c-4836-bb41-be05076f737d · outbound

This paper cites On the cybersecurity of traffic signal control system with connected vehicles.IEEE Transactions on Intelligent Transportation Systems, 23(9):16267–16279, 2022.

Exploring Traffic Simulation and Cybersecurity Strategies Using Large Language Models On the cybersecurity of traffic signal control system with connected vehicles.IEEE Transactions on Intelligent Transportation Systems, 23(9):16267–16279, 2022

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:41:32.040038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T23:41:31.384481Z digest=sha256:2204ef0379d5a06b512c5ccceb24eede9043a540628f015ff175a31735fc4c4a

Observation 453e35c2-55c0-4bc2-9a78-d64a39204173 · outbound

This paper cites Implications of traffic signal cybersecurity on potential deliberate traffic disruptions.Transportation research part A: policy and practice, 120:58–70, 2019.

Exploring Traffic Simulation and Cybersecurity Strategies Using Large Language Models Implications of traffic signal cybersecurity on potential deliberate traffic disruptions.Transportation research part A: policy and practice, 120:58–70, 2019

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:41:32.021063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T23:41:31.389511Z digest=sha256:4d4f1fcd63fac806fb3ba7efc3869bb40d3fc6ef9ee38c41f815b2325df28eea

Observation ceeb1441-d05f-4b24-a1c9-60943921e8ed · outbound

This paper cites An innovative attack modeling and attack detection approach for a waiting time-based adaptive traffic signal controller.

Exploring Traffic Simulation and Cybersecurity Strategies Using Large Language Models An innovative attack modeling and attack detection approach for a waiting time-based adaptive traffic signal controller

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:41:32.002346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T23:41:31.395755Z digest=sha256:1600e869a5b65eb8db14c99fda7d74d0a394554d26d5053b8c9515aef8af322f

Observation 18f2a163-a8b3-4981-8b1b-7462a33c9940 · outbound

This paper cites Assessing cybersecurity risks and traffic impact in connected autonomous vehicles.

Exploring Traffic Simulation and Cybersecurity Strategies Using Large Language Models Assessing cybersecurity risks and traffic impact in connected autonomous vehicles

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-06T23:41:31.401811Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:41:31.401811Z digest=sha256:102cbb69f0f4e9cae7e50cc33e98cb373350b68183a08d14f60d756bc9c0bbf5

Observation 852a7d33-9470-49ee-9310-8e9121cb36b9 · outbound

This paper cites Reinforcement learning-driven attack on road traffic signal controllers.

Exploring Traffic Simulation and Cybersecurity Strategies Using Large Language Models Reinforcement learning-driven attack on road traffic signal controllers

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:41:31.972612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T23:41:31.407297Z digest=sha256:b32b5624a9d184984546e7a4fa1a9dfe8aff3ba111a152d851b88d1a244f4680

Observation 406e64c5-aed0-4c87-b64e-0d2f8faf3688 · outbound

This paper cites Cybersecurity-focused anomaly detection in connected autonomous vehicles using machine learning.

Exploring Traffic Simulation and Cybersecurity Strategies Using Large Language Models Cybersecurity-focused anomaly detection in connected autonomous vehicles using machine learning

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:41:31.955401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T23:41:31.412504Z digest=sha256:2e445984f7f25f0d6eda321451c384122f24b3512a6a105b4a00d7e052351ab2

Observation a86a6020-01ea-4649-b82f-3411a769273d · outbound

This paper cites Evaluating cybersecurity risks of cooperative ramp merging in mixed traffic environ- ments.IEEE Intelligent Transportation Systems Maga- zine, 14(6):52–65, 2022.

Exploring Traffic Simulation and Cybersecurity Strategies Using Large Language Models Evaluating cybersecurity risks of cooperative ramp merging in mixed traffic environ- ments.IEEE Intelligent Transportation Systems Maga- zine, 14(6):52–65, 2022

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:41:31.938777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T23:41:31.417284Z digest=sha256:1e3bb0135d6e860092c84e5559f59edcd4182b9b0bda0c32f2dd558886298e56

Observation 579d725c-5cad-4a68-a978-d70a1c45ca12 · outbound

This paper cites an unresolved cited work.

Exploring Traffic Simulation and Cybersecurity Strategies Using Large Language Models Unresolved cited work

Reference 10

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unresolved
raw_fallback, observed 2026-08-06T23:41:31.921772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T23:41:31.422987Z digest=sha256:ad58b22d1825abea6ea7403dd692628cb1f62c20a02395f730c618d0f52ae8b9

Observation 426af8dd-95e7-49af-a644-c23a3b315997 · outbound

This paper cites Mistralbsm: Leveraging mistral-7b for vehicular networks misbehav- ior detection.arXiv preprint arXiv:2407.18462, 2024.

Exploring Traffic Simulation and Cybersecurity Strategies Using Large Language Models Mistralbsm: Leveraging mistral-7b for vehicular networks misbehav- ior detection.arXiv preprint arXiv:2407.18462, 2024

Reference 11

Resolution
verified exact
raw_fallback, observed 2026-08-06T23:41:31.822566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T23:41:31.427876Z digest=sha256:783dc0a1b6414b656f7e6f8e673a247066251bb6f91e22786f92618ca2b9da95

Observation e4f52e90-ebe3-41c7-a839-4fd43682481e · outbound

This paper cites LASER: Script Execution by Autonomous Agents for On-demand Traffic Simulation.

Exploring Traffic Simulation and Cybersecurity Strategies Using Large Language Models LASER: Script Execution by Autonomous Agents for On-demand Traffic Simulation

Reference 12

Resolution
verified exact
local_arxiv, observed 2026-08-06T23:41:31.734677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T23:41:31.432464Z digest=sha256:f927687ca1dd2161d7ea654db6b5f127435882f84843bdd1ab576dbd148e9dde

Observation d48973cf-a504-40d1-98c0-c022ef3939f0 · outbound

This paper cites Realistic Corner Case Generation for Autonomous Vehicles with Multimodal Large Language Model.

Exploring Traffic Simulation and Cybersecurity Strategies Using Large Language Models Realistic Corner Case Generation for Autonomous Vehicles with Multimodal Large Language Model

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-06T23:41:31.437528Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:41:31.437528Z digest=sha256:8554e29230f5728f6214f09a1096f0eb19ab0335c41f87b93a3262c28c97ebbd

Observation dd11d92f-887e-4a1d-8993-14f77924d762 · outbound

This paper cites iLLM-TSC: Integration reinforcement learning and large language model for traffic signal control policy improvement.

Exploring Traffic Simulation and Cybersecurity Strategies Using Large Language Models iLLM-TSC: Integration reinforcement learning and large language model for traffic signal control policy improvement

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-06T23:41:31.442791Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:41:31.442791Z digest=sha256:afc78bb480795a68368026c131ef48f3fa006cbaff529b40e6859a7b5f33093f

Observation 1a695fa7-e1dd-4296-8305-b6866e088b9c · outbound

This paper cites From sands to mansions: Enabling au- tomatic full-life-cycle cyberattack construction with llm.

Exploring Traffic Simulation and Cybersecurity Strategies Using Large Language Models From sands to mansions: Enabling au- tomatic full-life-cycle cyberattack construction with llm

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-06T23:41:31.448034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:41:31.448034Z digest=sha256:af50d2313613aee04110a188d114dd32bbaad2732d2043bc2cd21fd6718eddea

Observation dcb113cf-fe13-4347-874a-9b694c7b3c69 · outbound

This paper cites VulnBot: Autonomous Penetration Testing for A Multi-Agent Collaborative Framework.

Exploring Traffic Simulation and Cybersecurity Strategies Using Large Language Models VulnBot: Autonomous Penetration Testing for A Multi-Agent Collaborative Framework

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-06T23:41:31.453447Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:41:31.453447Z digest=sha256:e568c2e3147653c0a5a244e7c4d9b2f0e73d0edd7a5337b529d50755f821580f

Observation 5d50a7db-a6ec-4114-be7d-5f5fb23cf02c · outbound

This paper cites Leveraging large language models for dynamic scenario building targeting enhanced cyber-threat detection and security training.

Exploring Traffic Simulation and Cybersecurity Strategies Using Large Language Models Leveraging large language models for dynamic scenario building targeting enhanced cyber-threat detection and security training

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:41:31.900683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T23:41:31.459283Z digest=sha256:22f8c8269acfdc366932defaabe1b31f51e77fce56197499c3ddcc944f0adb4d

Observation fa8b6872-b28e-4704-ba76-b17ea24b9a2a · outbound

This paper cites Pentest-ai, an llm-powered multi-agents framework for penetration testing automation leveraging mitre attack.

Exploring Traffic Simulation and Cybersecurity Strategies Using Large Language Models Pentest-ai, an llm-powered multi-agents framework for penetration testing automation leveraging mitre attack

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:41:31.879088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T23:41:31.464851Z digest=sha256:987ef509c96217cae62fb92a53b5f2fc33be05286506699ac73d12792de93a78

Observation 0f393b0e-bb66-49a7-8f6c-d73e5a44fba3 · outbound

This paper cites Multimodal Road Network Generation Based on Large Language Model.

Exploring Traffic Simulation and Cybersecurity Strategies Using Large Language Models Multimodal Road Network Generation Based on Large Language Model

Reference 19

Resolution
verified exact
local_arxiv, observed 2026-08-06T23:41:31.534186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T23:41:31.470138Z digest=sha256:1d9d11c097f6df638d3858838d0271db4f4dd3657e203ba28f0aaa7f39382b94

Observation 84e6a124-58c0-4eef-a8ac-bd7bc9d7ca12 · outbound

This paper cites Chatsumo: Large language model for automating traffic scenario generation in simulation of urban mobility.IEEE Trans- actions on Intelligent Vehicles, 2024.

Exploring Traffic Simulation and Cybersecurity Strategies Using Large Language Models Chatsumo: Large language model for automating traffic scenario generation in simulation of urban mobility.IEEE Trans- actions on Intelligent Vehicles, 2024

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:41:31.861865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T23:41:31.475508Z digest=sha256:9f44f0d0e57e577ce2027a1ee21d213e7b09a2f36dd209d5be209b09e529545f

Observation 34dea8c7-8943-47f4-95b3-eaffa216bfed · outbound

This paper cites Large language model-assisted arterial traffic signal control.

Exploring Traffic Simulation and Cybersecurity Strategies Using Large Language Models Large language model-assisted arterial traffic signal control

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:41:31.842107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T23:41:31.481271Z digest=sha256:23c101706d87b833434dcd6c26c3ddd6d71e6d9036f49464b02d1cffe2b01fc0

Pith citing papers

No inbound Pith citation observations are available.