Data-Driven Variable Speed Limit Design for Highways via Distributionally Robust Optimization

Abstract

This paper introduces an optimization problem (P) and a solution strategy to design variable-speed-limit controls for a highway that is subject to traffic congestion and uncertain vehicle arrival and departure. By employing a finite data-set of samples of the uncertain variables, we aim to find a data-driven solution that has a guaranteed out-of-sample performance. In principle, such formulation leads to an intractable problem (P) as the distribution of the uncertainty variable is unknown. By adopting a distributionally robust optimization approach, this work presents a tractable reformulation of (P) and an efficient algorithm that provides a suboptimal solution that retains the out-of-sample performance guarantee. A simulation illustrates the effectiveness of this method.

Comment: 10 pages, 2 figures, submitted to ECC 2019

Original document

The different versions of the original document can be found in:

• http://arxiv.org/abs/1810.11385

• http://arxiv.org/pdf/1810.11385

• http://xplorestaging.ieee.org/ielx7/8778424/8795605/08796026.pdf?arnumber=8796026,

http://dx.doi.org/10.23919/ecc.2019.8796026

• https://arxiv.org/abs/1810.11385v1,

https://dblp.uni-trier.de/db/conf/eucc/eucc2019.html#LiFM19,

https://arxiv.org/pdf/1810.11385.pdf,

http://export.arxiv.org/abs/1810.11385,

http://export.arxiv.org/pdf/1810.11385,

https://aps.arxiv.org/abs/1810.11385,

https://tw.arxiv.org/abs/1810.11385,

https://ru.arxiv.org/abs/1810.11385,

https://ru.arxiv.org/pdf/1810.11385,

https://aps.arxiv.org/pdf/1810.11385,

https://il.arxiv.org/abs/1810.11385,

https://academic.microsoft.com/#/detail/2898314220