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Paper Citation Record · LEDGER

Strengthening the Case for a Bayesian Approach to Car-following Model Calibration and Validation using Probabilistic Programming

As of 16 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:1908.02427.

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

pith.paper-citation-record.v1
1908.02427 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T14:50:11.482750Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

24 of 24 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 899bf487-86bb-4a56-9725-7c35c520e4b1 · outbound

This paper cites Trends of transportation simulation and modeling based on a selection of exploratory advanced research projects: Workshop summary report,.

Strengthening the Case for a Bayesian Approach to Car-following Model Calibration and Validation using Probabilistic Programming Trends of transportation simulation and modeling based on a selection of exploratory advanced research projects: Workshop summary report,

Reference 1

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Observation 723cd2fe-c7a5-4f81-8163-d50b59885132 · outbound

This paper cites A case for online traffic simulation: Systematic procedure to calibrate car-following models using vehicle data,.

Strengthening the Case for a Bayesian Approach to Car-following Model Calibration and Validation using Probabilistic Programming A case for online traffic simulation: Systematic procedure to calibrate car-following models using vehicle data,

Reference 2

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Observation 71c3d75e-4750-4317-8e6c-12a555b363ab · outbound

This paper cites Improving the efficacy of car-following models with a new stochastic parameter estimation and calibration method,.

Strengthening the Case for a Bayesian Approach to Car-following Model Calibration and Validation using Probabilistic Programming Improving the efficacy of car-following models with a new stochastic parameter estimation and calibration method,

Reference 3

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Observation 376c8551-9cc5-48e7-8efb-7ac58a168396 · outbound

This paper cites Combining field data and computer simulations for calibration and prediction,.

Strengthening the Case for a Bayesian Approach to Car-following Model Calibration and Validation using Probabilistic Programming Combining field data and computer simulations for calibration and prediction,

Reference 4

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Observation fe777667-89e3-4c60-bcc4-4c85c8362029 · outbound

This paper cites Simple, Distributed, and Accelerated Probabilistic Programming.

Strengthening the Case for a Bayesian Approach to Car-following Model Calibration and Validation using Probabilistic Programming Simple, Distributed, and Accelerated Probabilistic Programming

Reference 5

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This paper cites Assessing uncertainties in traffic simulation: A key component in model calibration and validation,.

Strengthening the Case for a Bayesian Approach to Car-following Model Calibration and Validation using Probabilistic Programming Assessing uncertainties in traffic simulation: A key component in model calibration and validation,

Reference 6

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Source-reported events for the cited work

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Observation e057a84c-cd94-4258-b42f-8b82dd94230b · outbound

This paper cites Statistical inverse analysis for a network microsimulator,.

Strengthening the Case for a Bayesian Approach to Car-following Model Calibration and Validation using Probabilistic Programming Statistical inverse analysis for a network microsimulator,

Reference 7

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Source-reported events for the cited work

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Observation bbc9ea07-b10f-40cd-87c3-28c6c578f951 · outbound

This paper cites A cross-entropy method and probabilistic sensitivity analysis framework for calibrating microscopic traffic models,.

Strengthening the Case for a Bayesian Approach to Car-following Model Calibration and Validation using Probabilistic Programming A cross-entropy method and probabilistic sensitivity analysis framework for calibrating microscopic traffic models,

Reference 8

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Source-reported events for the cited work

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Observation fd14b671-1a66-415b-8189-ba5eba6f72fe · outbound

This paper cites Congested traffic states in empirical observations and microscopic simulations,.

Strengthening the Case for a Bayesian Approach to Car-following Model Calibration and Validation using Probabilistic Programming Congested traffic states in empirical observations and microscopic simulations,

Reference 9

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Source-reported events for the cited work

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Observation e2f6bd8f-14e3-4ec9-bb87-44d215b3c535 · outbound

This paper cites A simplified car-following theory: a lower order model,.

Strengthening the Case for a Bayesian Approach to Car-following Model Calibration and Validation using Probabilistic Programming A simplified car-following theory: a lower order model,

Reference 10

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Source-reported events for the cited work

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Observation b1fb6215-e182-452e-99f5-fcfa41042e76 · outbound

This paper cites A survey of probabilistic models using the bayesian programming methodology as a unifying framework,.

Strengthening the Case for a Bayesian Approach to Car-following Model Calibration and Validation using Probabilistic Programming A survey of probabilistic models using the bayesian programming methodology as a unifying framework,

Reference 11

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Source-reported events for the cited work

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Observation 3728b651-eda4-414e-980b-a471be28a9d2 · outbound

This paper cites A maximization technique occurring in the statistical analysis of probabilistic functions of markov chains,.

Strengthening the Case for a Bayesian Approach to Car-following Model Calibration and Validation using Probabilistic Programming A maximization technique occurring in the statistical analysis of probabilistic functions of markov chains,

Reference 12

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This paper cites Probabilistic program- ming in python using pymc3,.

Strengthening the Case for a Bayesian Approach to Car-following Model Calibration and Validation using Probabilistic Programming Probabilistic program- ming in python using pymc3,

Reference 13

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Observation 50fabf30-dc5c-4534-adc3-58eee904ba7f · outbound

This paper cites A probabilistic model checking analysis of vehicular ad-hoc networks,.

Strengthening the Case for a Bayesian Approach to Car-following Model Calibration and Validation using Probabilistic Programming A probabilistic model checking analysis of vehicular ad-hoc networks,

Reference 14

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Source-reported events for the cited work

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Observation 90f35b09-313b-4e47-abde-7bc503252198 · outbound

This paper cites A behavioural car-following model for computer simula- tion,.

Strengthening the Case for a Bayesian Approach to Car-following Model Calibration and Validation using Probabilistic Programming A behavioural car-following model for computer simula- tion,

Reference 15

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This paper cites Barcelo, Fundamentals of Traffic Simulation , 01 2010.

Strengthening the Case for a Bayesian Approach to Car-following Model Calibration and Validation using Probabilistic Programming Barcelo, Fundamentals of Traffic Simulation , 01 2010

Reference 16

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Observation 6cea3ed2-a775-4f46-87c4-7d21a2a13874 · outbound

This paper cites Flow: Deep reinforcement learning for control in sumo,.

Strengthening the Case for a Bayesian Approach to Car-following Model Calibration and Validation using Probabilistic Programming Flow: Deep reinforcement learning for control in sumo,

Reference 17

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Observation 3da895f3-bd1b-4624-9029-d4e2ca18089a · outbound

This paper cites Gelman, Prior Distribution.

Strengthening the Case for a Bayesian Approach to Car-following Model Calibration and Validation using Probabilistic Programming Gelman, Prior Distribution

Reference 19

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Source-reported events for the cited work

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Observation c36f515e-8a30-4ee5-8301-e49bd037c593 · outbound

This paper cites A simple and global optimization al- gorithm for engineering problems: Differential evolution algorithm,.

Strengthening the Case for a Bayesian Approach to Car-following Model Calibration and Validation using Probabilistic Programming A simple and global optimization al- gorithm for engineering problems: Differential evolution algorithm,

Reference 20

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Observation 0dea32bb-c11f-454e-8a4d-e34b1fc3c729 · outbound

This paper cites Asymptotic equivalence of bayes cross validation and widely applicable information criterion in singular learning theory,.

Strengthening the Case for a Bayesian Approach to Car-following Model Calibration and Validation using Probabilistic Programming Asymptotic equivalence of bayes cross validation and widely applicable information criterion in singular learning theory,

Reference 21

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Observation 4b3cc7be-87f2-4422-bd93-1690a018b3f8 · outbound

This paper cites Practical bayesian model evaluation using leave-one-out cross-validation and waic,.

Strengthening the Case for a Bayesian Approach to Car-following Model Calibration and Validation using Probabilistic Programming Practical bayesian model evaluation using leave-one-out cross-validation and waic,

Reference 22

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Source-reported events for the cited work

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Observation 704bc100-75f8-4f3e-878d-0921c2e0afe0 · outbound

This paper cites Methods to explore driving behavior heterogeneity using shrp2 naturalistic driving study trajectory-level driving data,.

Strengthening the Case for a Bayesian Approach to Car-following Model Calibration and Validation using Probabilistic Programming Methods to explore driving behavior heterogeneity using shrp2 naturalistic driving study trajectory-level driving data,

Reference 23

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Strengthening the Case for a Bayesian Approach to Car-following Model Calibration and Validation using Probabilistic Programming Statistical model criticism using kernel two sample tests,

Reference 24

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Observation c9f61192-5017-4916-a897-6d0c5f6acba1 · outbound

This paper cites Available: http://dl.acm.org/citation.cfm?id=2969239.

Strengthening the Case for a Bayesian Approach to Car-following Model Calibration and Validation using Probabilistic Programming Available: http://dl.acm.org/citation.cfm?id=2969239

Reference 837

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Source-reported events for the cited work

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