# A Control-Theoretic Approach for Scalable and Robust Traffic Density Estimation using Convex Optimization

## TL;DR

This paper introduces a control-theoretic, convex optimization-based method for real-time, robust traffic density estimation on highways with limited sensors, enhancing traffic management and congestion mitigation.

## Contribution

It develops a generalized traffic flow model, characterizes nonlinearities for Lipschitz continuity, and designs a scalable, robust state estimator using convex optimization techniques.

## Key findings

- Estimator performs well under high disturbances
- Method is scalable to large traffic networks
- Robust to parametric uncertainties and unknown inputs

## Abstract

Monitoring and control of traffic networks represent alternative, inexpensive strategies to minimize traffic congestion. As the number of traffic sensors is naturally constrained by budgetary requirements, real-time estimation of traffic flow in road segments that are not equipped with sensors is of significant importance---thereby providing situational awareness and guiding real-time feedback control strategies. To that end, firstly we build a generalized traffic flow model for stretched highways with arbitrary number of ramp flows based on the Lighthill Whitham Richards (LWR) flow model. Secondly, we characterize the function set corresponding to the nonlinearities present in the LWR model, and use this characterization to design real-time and robust state estimators (SE) for stretched highway segments. Specifically, we show that the nonlinearities from the derived models are locally Lipschitz continuous by providing the analytical Lipschitz constants. Thirdly, the analytical derivation is then incorporated through a robust SE method given a limited number of traffic sensors, under the impact of process and measurement disturbances and unknown inputs. The estimator is based on deriving a convex semidefinite optimization problem. Finally, numerical tests are given showcasing the applicability, scalability, and robustness of the proposed estimator for large systems under high magnitude disturbances, parametric uncertainty, and unknown inputs.