# Distributionally Robust Optimisation in Congestion Control

## TL;DR

This paper extends a congestion control model to incorporate distributionally robust optimization, accounting for uncertainty in driver responses to real-time travel information, aiming to improve traffic management strategies.

## Contribution

It introduces a distributionally robust approach to congestion control that considers uncertainty in driver responses, enhancing the robustness of traffic management models.

## Key findings

- Robust optimization over driver response distributions improves traffic flow.
- Incorporating response uncertainty leads to more resilient congestion control strategies.
- The model accounts for variability in travel time responses, enhancing decision-making.

## Abstract

The effects of real-time provision of travel-time information on the behaviour of drivers are considered. The model of Marecek et al. [arXiv:1406.7639, Int. J. Control 88(10), 2015] is extended to consider uncertainty in the response of a driver to an interval provided per route. Specifically, it is suggested that one can optimise over all distributions of a random variable associated with the driver's response with the first two moments fixed, and for each route, over the sub-intervals within the minimum and maximum in a certain number of previous realisations of the travel time per the route.