Discrete adjoint gradient computation for multiclass traffic flow models on road networks

TL;DR

This paper introduces a discrete adjoint gradient method for optimizing multi-class traffic flow models on road networks, enabling efficient sensitivity analysis and large-scale network optimization.

Contribution

It extends Godunov-type schemes to handle complex junctions and buffers, providing a robust framework for traffic optimization using adjoint sensitivities.

Findings

Numerical simulations show high efficiency with increasing control parameters.

The method effectively handles complex junctions and congestion spillback.

Optimization of travel metrics is achieved with reduced computational cost.

Abstract

This paper applies a discrete adjoint gradient computation method for a multi-class traffic flow model on road networks. Vehicle classes are characterized by their specific velocity functions, which depend on the total traffic density, resulting in a coupled hyperbolic system of conservation laws. The system is discretized using a Godunov-type finite volume scheme based on demand and supply functions, extended to handle complex junction coupling conditions -- such as merges and diverges -- and boundary conditions with buffer lengths to account for congestion spillback. The optimization of different travel-related performance metrics, including total travel time and total travel distance, is formulated as a constrained minimization problem and is accomplished through the use of an adjoint gradient approach, allowing for an efficient computation of sensitivities with respect to the chosen time-dependent control variables. Numerical simulations on a sample network demonstrate the efficiency of the proposed framework, particularly as the number of control parameters increases. This approach provides a robust and computationally efficient solution, making it suitable for large-scale traffic network optimization.