Differentiable Predictive Control for Large-Scale Urban Road Networks

TL;DR

This paper introduces a Differentiable Predictive Control method for large-scale urban traffic networks, significantly reducing computation time and improving traffic flow, with potential to lower emissions and congestion.

Contribution

It presents a novel physics-informed machine learning approach based on MFD and NMFD models, offering faster and more robust traffic control solutions compared to existing methods.

Findings

Achieves 4 orders of magnitude faster computation than state-of-the-art MPC

Improves traffic performance by up to 37%

Demonstrates robustness to traffic pattern changes

Abstract

Transportation is a major contributor to CO2 emissions, making it essential to optimize traffic networks to reduce energy-related emissions. This paper presents a novel approach to traffic network control using Differentiable Predictive Control (DPC), a physics-informed machine learning methodology. We base our model on the Macroscopic Fundamental Diagram (MFD) and the Networked Macroscopic Fundamental Diagram (NMFD), offering a simplified representation of citywide traffic networks. Our approach ensures compliance with system constraints by construction. In empirical comparisons with existing state-of-the-art Model Predictive Control (MPC) methods, our approach demonstrates a 4 order of magnitude reduction in computation time and an up to 37% improvement in traffic performance. Furthermore, we assess the robustness of our controller to scenario shifts and find that it adapts well to changes in traffic patterns. This work proposes more efficient traffic control methods, particularly in large-scale urban networks, and aims to mitigate emissions and alleviate congestion in the future.