Reconsideration of optimization for reduction of traffic congestion

TL;DR

This paper explores reformulating quantum annealer-based traffic congestion optimization to improve route selection, focusing solely on congestion reduction through a novel cost function with dead zones and constraints.

Contribution

It introduces a new cost function formulation for quantum annealing that emphasizes traffic congestion reduction, improving upon previous formulations.

Findings

Reduced traffic congestion in simulations

Effective route optimization using the new cost function

Demonstrated advantages of the reformulated approach

Abstract

One of the most impressive applications of a quantum annealer was optimizing a group of Volkswagen to reduce traffic congestion using a D-Wave system. A simple formulation of a quadratic term was proposed to reduce traffic congestion. This quadratic term was useful for determining the shortest routes among several candidates. The original formulation produced decreases in the total lengths of car tours and traffic congestion. In this study, we reformulated the cost function with the sole focus on reducing traffic congestion. We then found a unique cost function for expressing the quadratic function with a dead zone and an inequality constraint.