Stochastic Cell Transmission Models of Traffic Networks
Machine Learning
2023-04-25 v1 Systems and Control
Systems and Control
Machine Learning
Abstract
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.
Keywords
Cite
@article{arxiv.2304.11654,
title = {Stochastic Cell Transmission Models of Traffic Networks},
author = {Zachary Feinstein and Marcel Kleiber and Stefan Weber},
journal= {arXiv preprint arXiv:2304.11654},
year = {2023}
}