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Paper Citation Record · LEDGER

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback

As of 13 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2505.21349.

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

pith.paper-citation-record.v1
2505.21349 v2

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:35:54.306166Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+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

39 of 39 outbound references displayed

  • verified exact13
  • verified fuzzy1
  • unresolved18
  • parse uncertain0
  • malformed identifier4
  • metadata mismatch3

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation eced6853-73e3-45c0-a574-24ab898f1acc · outbound

This paper cites LLMScenario: Large Language Model Driven Scenario Generation.

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback LLMScenario: Large Language Model Driven Scenario Generation

Reference 9

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Observation c6adb36f-f580-49f1-8d85-9dd39d6b69cd · outbound

This paper cites Purpose in the Machine: Do Traffic Simulators Produce Distributionally Equivalent Outcomes for Reinforcement Learning Applications?.

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback Purpose in the Machine: Do Traffic Simulators Produce Distributionally Equivalent Outcomes for Reinforcement Learning Applications?

Reference 10

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Observation 275b34c5-6d0c-4ab7-9bcf-0184c49ea6b0 · outbound

This paper cites Prompt to transfer: sim-to-real transfer for traffic signal control with prompt learning.

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback Prompt to transfer: sim-to-real transfer for traffic signal control with prompt learning

Reference 13

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doi, observed 2026-08-07T13:35:56.038170Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation b04be91d-e60b-4cec-bb9c-e8b28ba92b0e · outbound

This paper cites AutoReward: Closed-Loop Reward Design with Large Language Models for Autonomous Driving.

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback AutoReward: Closed-Loop Reward Design with Large Language Models for Autonomous Driving

Reference 15

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T13:35:51.571387Z digest=sha256:0d47d6415888c27299225cbade77ec918719aa3cd6d491f35eb407e4047b9554

Observation 963ce164-0986-4940-8999-8be32badb30d · outbound

This paper cites Multi-Agent Multimodal Transportation Simulation for Mega-cities: Application of Los Angeles.

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback Multi-Agent Multimodal Transportation Simulation for Mega-cities: Application of Los Angeles

Reference 16

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doi, observed 2026-08-07T13:35:55.870291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation f6024282-d103-4a33-97ff-8d11fcdb347d · outbound

This paper cites A Demand Modelling Pipeline for an Agent-Based Traffic Simulation of the City of Barcelona.

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback A Demand Modelling Pipeline for an Agent-Based Traffic Simulation of the City of Barcelona

Reference 18

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:35:51.897732Z digest=sha256:e01d4ff3d16d2c51a6dbcddbd1b8da7d393cdeac9a460fef9042ba198c8260fd

Observation 9fc11f0b-869f-46a5-813a-a7890db00c1f · outbound

This paper cites ChatSUMO: Large Language Model for Automating Traffic Scenario Generation in Simulation of Urban MObility.

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback ChatSUMO: Large Language Model for Automating Traffic Scenario Generation in Simulation of Urban MObility

Reference 19

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source=pdf_text observed=2026-08-07T13:35:52.054739Z digest=sha256:48c9f4eab259affd9c61476847d7835bb91743ee409942e33fc71d2cae9bae11

Observation bf5f4772-faaa-4169-9e4c-7e7f3fc716e5 · outbound

This paper cites Effects of fog, snow, and rain on video detection systems at intersections.

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback Effects of fog, snow, and rain on video detection systems at intersections

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T13:35:52.237531Z digest=sha256:506dba49a730207ff39964c1f51bd443e80a22db41e4284534cd896767b695fd

Observation b93563fc-507b-494c-9c28-c7c146edfb09 · outbound

This paper cites Libsignal: an open library for traffic signal control.

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback Libsignal: an open library for traffic signal control

Reference 22

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source=pdf_text observed=2026-08-07T13:35:52.314740Z digest=sha256:013012759bbb1a970c952098cec78c1fa078eb5f56d624f5429ea6790cb607f4

Observation 67cccded-aea1-49b0-bb80-b4d90191b185 · outbound

This paper cites Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control.

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control

Reference 24

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source=pdf_text observed=2026-08-07T13:35:52.554757Z digest=sha256:2ebc8797cbbfc5e5abd3496eba67900224806aaca1bc70293e3c67f91d60f3d8

Observation a6d9c11c-5dce-41ee-b483-98cce7cb70d4 · outbound

This paper cites Advanced Traffic Demand Generation in SUMO: ML-based Prediction of Flow Rate based on Real-world Measured Datasets.

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback Advanced Traffic Demand Generation in SUMO: ML-based Prediction of Flow Rate based on Real-world Measured Datasets

Reference 25

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source=pdf_text observed=2026-08-07T13:35:52.638539Z digest=sha256:cfe1272ce65f275e7264b27f4c88511b141eaf0c2511c2167773d21585471a71

Observation b29c02e0-1f2c-4a6a-a3eb-192454f38d93 · outbound

This paper cites Vehicular traffic simulation in the city of Turin from raw data.

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback Vehicular traffic simulation in the city of Turin from raw data

Reference 26

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 929fe85c-cb15-4021-9a5b-fa8ee41c8cda · outbound

This paper cites Reflexion: Language Agents with Verbal Reinforcement Learning.

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback Reflexion: Language Agents with Verbal Reinforcement Learning

Reference 28

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source=pdf_text observed=2026-08-07T13:35:53.045711Z digest=sha256:48ca7d3cd1418b2ff01d1b88d7d2be698dd324f24159d2e44116c7206f4ca78e

Observation 688d09d9-137b-4da3-91f5-86167abde0c0 · outbound

This paper cites Optimizing Autonomous Driving for Safety: A Human-Centric Approach with LLM-Enhanced RLHF.

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback Optimizing Autonomous Driving for Safety: A Human-Centric Approach with LLM-Enhanced RLHF

Reference 29

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source=pdf_text observed=2026-08-07T13:35:53.159604Z digest=sha256:eb6d54ce824b8966380512433a13355c195f7a5f5aa0a9a655e599dfd21edb75

Observation e29683a5-5a25-4928-9897-39734dea3686 · outbound

This paper cites Vehicle Counting using Computer Vision: A Survey.

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback Vehicle Counting using Computer Vision: A Survey

Reference 30

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T13:35:53.297468Z digest=sha256:e4ab78ded31938145b73a8b23631ef7927217f08a0cd3e17999a20548915e988

Observation e9564c40-7576-43db-92a1-7fddbc95dac5 · outbound

This paper cites LLM-Assisted Light: Leveraging Large Language Model Capabilities for Human-Mimetic Traffic Signal Control in Complex Urban Environments.

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback LLM-Assisted Light: Leveraging Large Language Model Capabilities for Human-Mimetic Traffic Signal Control in Complex Urban Environments

Reference 32

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source=pdf_text observed=2026-08-07T13:35:53.478380Z digest=sha256:ea419479e90e4a2f156181fe8e1b6b022d0c1337e8c5f96623d5af2082778416

Observation b9ae32bf-5cdd-46fb-b748-97a102e6c047 · outbound

This paper cites Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 34

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source=pdf_text observed=2026-08-07T13:35:53.691081Z digest=sha256:6d66fa7eef8d5b18fb44695ed8ca60f6d1d151dbb7241b4eda67f7caf27385ab

Observation ed923797-a35b-4c08-aac2-90e8807ac941 · outbound

This paper cites Hierarchically and cooperatively learning traffic signal control.

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback Hierarchically and cooperatively learning traffic signal control

Reference 36

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T13:35:53.905721Z digest=sha256:806119a5d5a1d7ae3818adbb2c807d04ebefc3212e152eec6b058de31c3cd8fb

Observation 22fbf369-f7ae-466a-9c9d-254f1cf5bf84 · outbound

This paper cites CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario.

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario

Reference 37

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source=pdf_text observed=2026-08-07T13:35:54.019164Z digest=sha256:4aaad444b7294d53bb85a5fb579513a4bc1fa61b24c0548c2758e15bad84297a

Observation e3b57be3-2bfa-4cd7-8e1e-ce0e0790985a · outbound

This paper cites Learning Phase Competition for Traffic Signal Control.

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback Learning Phase Competition for Traffic Signal Control

Reference 38

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source=pdf_text observed=2026-08-07T13:35:54.127352Z digest=sha256:8ab965075919c6b162cf0beacb9bec6f6dc944e8a7db443aba997f338e57e300

Observation f389f27c-df88-4859-ba1f-935803f4cd40 · outbound

This paper cites Fine-Tuning Language Models from Human Preferences.

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback Fine-Tuning Language Models from Human Preferences

Reference 40

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source=pdf_text observed=2026-08-07T13:35:54.306166Z digest=sha256:656364642eecc2b28752cf8369d75b0388130c4c5c1a7db699a1cafd932f3147

Observation a923a329-1b3f-410d-aeec-4fd07d195b4d · outbound

This paper cites A real-time computer vision system for vehicle tracking and traffic surveillance.

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback A real-time computer vision system for vehicle tracking and traffic surveillance

Reference 1991

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation cbca92db-b88f-41f3-93a5-3bdcf86e5665 · outbound

This paper cites Evaluation of Stop Bar Video Detection Accuracy at Signalized Intersections.

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback Evaluation of Stop Bar Video Detection Accuracy at Signalized Intersections

Reference 2005

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T13:35:52.887156Z digest=sha256:35c8571c87c14a4fdeb2672165a345c531851f9b2288bedf681008f2b17bd1f2

Observation 346cf357-776a-4f5e-b147-30469bd8b56a · outbound

This paper cites Models, Traffic Models, Simulation, and Traffic Simulation.

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback Models, Traffic Models, Simulation, and Traffic Simulation

Reference 2010

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T13:35:50.242924Z digest=sha256:e9ec05bbb68f0daf7042dcce5c0a9167807749078468edc2a7a60d40fc43298e

Observation 9b3ff556-4573-4932-ab8b-fa7ec725233b · outbound

This paper cites Non-Local Means Denoising.

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback Non-Local Means Denoising

Reference 2011

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T13:35:50.768225Z digest=sha256:00187279f723e05af1efe16ad78f5fdb7b303c91f8c7f376967ec11d2056cd55

Observation f8ff7588-c3b5-4167-9aee-9f426b84187e · outbound

This paper cites Identifying chronic splashover errors at freeway loop detectors.

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback Identifying chronic splashover errors at freeway loop detectors

Reference 2012

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T13:35:51.765851Z digest=sha256:7251b98d35053306b6c5618394f47c3d062528817406ea9b3aa307413d88c6fd

Observation e803cf20-27db-44d0-819e-1ccb1424b201 · outbound

This paper cites Length-Based Vehicle Classification Schemes and Length Bin Boundaries.

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback Length-Based Vehicle Classification Schemes and Length Bin Boundaries

Reference 2013

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T13:35:53.801148Z digest=sha256:26905d24d3eef8b8e12816ba4c740c0faf94e381f0b8df8e313e42dcfeb5cf54

Observation 7768a46e-9621-4107-a0f7-8806c254276f · outbound

This paper cites Traffic Simulation for All: A Real World Traffic Scenario from the City of Bologna.

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback Traffic Simulation for All: A Real World Traffic Scenario from the City of Bologna

Reference 2014

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T13:35:50.537407Z digest=sha256:b62d8c547d91cc53a0e35dc1bb93889c8141a6d90ca1c48b5198894b1f867700

Observation 97924dbc-fe29-41c0-ab52-dbca74753a07 · outbound

This paper cites Luxembourg SUMO Traffic (LuST) Scenario: 24 hours of mobility for vehicular networking research.

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback Luxembourg SUMO Traffic (LuST) Scenario: 24 hours of mobility for vehicular networking research

Reference 2015

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raw_fallback, observed 2026-08-07T13:35:58.917316Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T13:35:51.154301Z digest=sha256:b2dea5610c294be50db27519a56d6038874f63639ad5d39ebb4939c40032bf36

Observation f0591423-032c-4fab-82da-a5c264187039 · outbound

This paper cites CVXPY: A Python-embedded modeling language for convex optimization.

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback CVXPY: A Python-embedded modeling language for convex optimization

Reference 2016

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:35:51.501508Z digest=sha256:a60cf441d787ca523c456dcdae626c1b9758efa3e366b8f1c4aaebab8646696f

Observation d03e7177-e4c6-4816-9b37-c2943fe5139e · outbound

This paper cites Towards multimodal mobility simulation of C-ITS: The Monaco SUMO traffic scenario.

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback Towards multimodal mobility simulation of C-ITS: The Monaco SUMO traffic scenario

Reference 2017

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T13:35:51.302590Z digest=sha256:3fbe5333361d7ea0b8f28ab00dd70429933b5c4df635a8c7dbe04c520066aaca

Observation ff3c4bf4-315b-4097-9563-e997cbf2a7d3 · outbound

This paper cites Microscopic Traffic Simulation using SUMO.

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback Microscopic Traffic Simulation using SUMO

Reference 2018

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source=pdf_text observed=2026-08-07T13:35:49.957927Z digest=sha256:1a41079754b2dd5dbf42863d84cb3759ce76884fef4dd9ba382642724b463d3f

Observation 235ed8af-63ac-40a9-b3c1-6a1d1e25d533 · outbound

This paper cites A Comparative Study of State-of-the-Art Deep Learning Algorithms for Vehicle Detection.

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback A Comparative Study of State-of-the-Art Deep Learning Algorithms for Vehicle Detection

Reference 2019

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doi, observed 2026-08-07T13:35:54.987113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T13:35:53.414769Z digest=sha256:26f8501e624279e5351065c5e224efad14854e97ded467953049cb03e2573e8b

Observation 79ca5fb3-e404-4b18-83df-5f8dc0af5cd7 · outbound

This paper cites InTAS - The Ingolstadt Traffic Scenario for SUMO.

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback InTAS - The Ingolstadt Traffic Scenario for SUMO

Reference 2020

Resolution
malformed identifier
raw_fallback, observed 2026-08-07T13:35:59.801040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T13:35:52.146486Z digest=sha256:dc243a5a17b85ef4521c794aa482aa18a1517c6af3f5bfc70d0af4ff1ef14f6c

Observation e68b91f7-0e22-469d-8957-937d48e8d482 · outbound

This paper cites Program Synthesis with Large Language Models.

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback Program Synthesis with Large Language Models

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-07T13:35:50.107890Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:35:50.107890Z digest=sha256:247fdb3a77fda4adcf2f902898d3c5fd6df8f5d20d4de4f035d3cf3d1830aff9

Observation c270ace7-32ce-4843-b273-1b98d5e10e7c · outbound

This paper cites BoT-SORT: Robust Associations Multi-Pedestrian Tracking.

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback BoT-SORT: Robust Associations Multi-Pedestrian Tracking

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-07T13:35:49.846818Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:35:49.846818Z digest=sha256:7c4423963f1bf018bfd74b9be31dd973513c5bf885a8ab7d7727e124beb13fa3

Observation 46d29196-9bed-4cec-8d1b-e3cda2ae1915 · outbound

This paper cites Simulation-Based Origin-Destination Matrix Reduction: A Case Study of Helsinki City Area.

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback Simulation-Based Origin-Destination Matrix Reduction: A Case Study of Helsinki City Area

Reference 2023

Resolution
verified exact
doi, observed 2026-08-07T13:35:56.471356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T13:35:50.659129Z digest=sha256:d21fa94172b783a401e7c46ae93800c235802ebac0a52c5313f9a2136cb05f54

Observation e35fa9b4-ffa0-4052-b9dc-3333e5dd2e44 · outbound

This paper cites A Decision-Language Model (DLM) for Dynamic Restless Multi-Armed Bandit Tasks in Public Health.

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback A Decision-Language Model (DLM) for Dynamic Restless Multi-Armed Bandit Tasks in Public Health

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-07T13:35:50.384746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:35:50.384746Z digest=sha256:53c5ed4521be4d8624108c837f958721b7beb37ef13fff28e30b31bb6c359162

Observation 54784332-c987-4e67-9995-76f17752f78b · outbound

This paper cites BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions.

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-07T13:35:54.213962Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:35:54.213962Z digest=sha256:5ef5954f07e3e013b9cfc37b060c6646681f13925b3f3c42c67c3600791142d6

Pith citing papers

No inbound Pith citation observations are available.