Quantum Computing Approaches for Large-Scale Urban Traffic Flow Optimization and Route Planning

Abstract

ABSTRACT Urban traffic congestion is an increasingly critical challenge facing modern megacities, causing substantial economic losses, elevated greenhouse gas emissions, and severe operational inefficiencies in transportation infrastructure. Classical computing paradigms, including Integer Linear Programming (ILP) and conventional dynamic routing algorithms, suffer from severe combinatorial explosion when scaling to high-density, multi intersection urban environments. Quantum computing has emerged as a transformative computational paradigm capable of solving complex non convex optimization problems with exponential speedups. This review paper delivers a comprehensive technical analysis of quantum computing methodologies tailored for large-scale urban traffic flow optimization and dynamic route planning. We evaluate Quadratic Unconstrained Binary Optimization (QUBO) mathematical formulations, Quantum Annealing (QA) implementations on D-Wave architectures, and gate-based Variational Quantum Algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE). Furthermore, we examine hybrid quantum-classical frameworks, signal phase optimization, and vehicle routing algorithms under real-time constraints. Benchmark comparative evaluations reveal that quantum annealing reduces computation time for global congestion minima by several orders of magnitude compared to classical branch-and-bound solvers in networks exceeding 500 intersections. Finally, we discuss noisy intermediate-scale quantum (NISQ) device limitations, error mitigation techniques, and actionable future directions for real-world deployment in smart transportation systems. 

KEYWORDS: Quantum Computing, Urban Traffic Flow, Route Planning, Quadratic Unconstrained Binary Optimization (QUBO), Quantum Approximate Optimization Algorithm (QAOA), Quantum Annealing, Intelligent Transportation Systems (ITS).