Stochastic Cell Transmission Models of Traffic Networks
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Abstract (arXiv)
We introduce a rigorous framework for stochastic cell transmission models for general traffic networks. The performance of traffic systems is evaluated based on preference functionals and acceptable designs. The numerical implementation combines simulation, Gaussian process regression, and a stochastic exploration procedure. The approach is illustrated in two case studies.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 10 of its 59 references that are in the map. First posted on arXiv 23 Apr 2023.
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10 of 59 references.
References · 10
- 2018Gaussian Processes and Kernel Methods: A Review on Connections and Equivalencescited by 192
- 2020Bayesian Neural Networks: An Introduction and Surveycited by 131
- 2019Uniform Error Bounds for Gaussian Process Regression with Application to Safe Controlcited by 64
- 2020A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challengescited by 52
- 2016Practical heteroskedastic Gaussian process modeling for large simulation experimentscited by 39
- 2019Approximate Inference for Fully Bayesian Gaussian Process Regressioncited by 31
- 2018Evaluating Gaussian Process Metamodels and Sequential Designs for Noisy Level Set Estimationcited by 10
- 2021Uniform Error and Posterior Variance Bounds for Gaussian Process Regression with Application to Safe Controlcited by 7
- 2016How priors of initial hyperparameters affect Gaussian process regression modelscited by 3
- 2016Analysis of a Stochastic Switched Model of Freeway Traffic Incidentscited by 1
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