Abstract Traffic optimization remains a significant challenge in urban planning and transportation management. While efficient traffic optimization is crucial for enhancing urban mobility, reducing congestion, and promoting environmental sustainability, traditional computational methods often struggle with the complex, dynamic nature of traffic systems. Recent advances in quantum computing, particularly quantum annealing, offer promising new techniques that could revolutionize traffic flow optimization. This work systematically reviews the literature, starting with search term formulation and ending with the final set of articles. These articles are categorized into three groups: (1) traffic signal control, (2) traffic flow optimization, and (3) routing problems optimization (including vehicle routing problem and traveling salesman problem). The review critically examines current studies on quantum annealing-based traffic optimization, focusing on contributions, methods, solvers, problem suitability, key findings, benchmark fairness, and limitations. It identifies key challenges and provides recommendations for future research. Insights from this work offer researchers and practitioners a concise overview of current challenges and future directions in traffic optimization.
Abstract Traffic optimization remains a significant challenge in urban planning and transportation management. While efficient traffic optimization is crucial for enhancing urban mobility, reducing congestion, and promoting environmental sustainability, traditional computational methods often struggle with the complex, dynamic nature of traffic systems. Recent advances in quantum computing, particularly quantum annealing, offer promising new techniques that could revolutionize traffic flow optimization. This work systematically reviews the literature, starting with search term formulation and ending with the final set of articles. These articles are categorized into three groups: (1) traffic signal control, (2) traffic flow optimization, and (3) routing problems optimization (including vehicle routing problem and traveling salesman problem). The review critically examines current studies on quantum annealing-based traffic optimization, focusing on contributions, methods, solvers, problem suitability, key findings, benchmark fairness, and limitations. It identifies key challenges and provides recommendations for future research. Insights from this work offer researchers and practitioners a concise overview of current challenges and future directions in traffic optimization.
We explore how quantum computing (QC) can advance transportation optimization, with a focus on two high-impact areas: traffic signal control and vehicle electrification with grid integration. As transportation systems grow in complexity, classical optimization methods increasingly struggle to deliver scalable and efficient solutions, particularly for real-time environments. This work identifies key challenges within these two domains where QC may offer advantages, particularly in handling combinatorial decision spaces and dynamic constraints. We begin by outlining the limitations of classical approaches for traffic signal control optimization and electric vehicle charging coordination, highlighting where computational limitations arise. Previous quantum formulations are discussed to demonstrate how emerging quantum algorithms, including quantum annealing and hybrid algorithms, could be leveraged to reformulate and address these problems. We also evaluate the suitability of current quantum hardware and discuss the advantages that QC could bring to these domains. While acknowledging the present limitations of QC technologies, this poster emphasizes the importance of preparing quantum-compatible models today. By reviewing and establishing formulations that align with the strengths of quantum algorithms, researchers and practitioners can better position themselves to take advantage of QC advancements as they occur. This work aims to provide a practical, forward-looking perspective on the near-term potential of quantum computing in transportation optimization. Quantum Computing Transportation Optimization Traffic Signal Control Electric Vehicle Charging
Quantum computing introduces new computational methods that address complex optimization and machine learning challenges in transportation engineering. This literature survey reviews fundamental quantum principles, including qubits, superposition, entanglement, and quantum gates, and examines their applications in transportation. Fundamental quantum algorithms, including Shor's algorithm, the Quantum Approximate Optimization Algorithm (QAOA), and Grover's search, are described for their applications in transportation, such as network analysis, traffic management, and vehicle routing. Quantum optimization techniques, including hybrid quantum-classical approaches and quantum annealing, have been explored for combinatorial challenges such as the Vehicle Routing Problem (VRP) and dynamic traffic signal control. Additionally, quantum machine learning models, such as Quantum Convolutional Neural Networks (QCNNs) and quantum clustering, present applications in traffic prediction and anomaly detection. Despite its potential, quantum computing in transportation faces challenges related to hardware limitations, error rates, and a lack of established quantum programming frameworks. Addressing these challenges necessitates progress in hardware scalability, error mitigation techniques, and algorithm refinement. As quantum computing continues to evolve, its integration into transportation engineering could improve computational efficiency and enable new capabilities in optimization, traffic management, and intelligent transportation systems. This survey outlines key developments, challenges, and future research directions in this emerging field.
Efficient transportation networks are vital for modern urban environments, where minimizing travel time and reducing congestion are key objectives. The goal of this thesis is to review the applications and benefits of quantum computing in transportation network optimization and to determine whether quantum computing can produce more efficient or accurate solutions than classical computational methods. This thesis is a literature study based on 18 sources, most of which were published between 2017 and 2025 due to the topicality of the subject. Transportation network optimization can be approached from many perspectives, such as adding new roads to existing networks, increasing road capacities, optimizing traffic signals, or improving the computational efficiency of modeling network flows. Transportation networks are modeled using a mathematical framework that divides the optimization problem into an upper level and a lower level. Upper-level optimization focuses on network design, while the lower level focuses on testing the designed upper level. The study examined four applications of quantum computing, focusing on the optimization of lane capacities, traffic signals, and computational efficiency in network flows. The study concluded that quantum computing can achieve significant improvements in network optimization problems. The road capacity optimization method compared a combined classical-quantum hybrid quantum annealing algorithm to a purely classical "Tabu Search" algorithm. The hybrid method achieved a runtime up to 11 times faster than the classical algorithm. The traffic signal optimization method also compared a hybrid quantum computing method to classical methods, producing significantly better results. Network flows were optimized using the qbsolv hybrid quantum annealing algorithm to calculate vehicle flows and redistribute routes, with the goal of reducing congestion and shortening average travel times. The obtained result successfully reduced congestion in targeted areas and generally provided a better way to model network flows compared to the user equilibrium model. Finally, the purely quantum MNDAS algorithm constituted an improved version of the metaheuristic ACO method, producing a quadratic speedup compared to the ACO algorithm. Quantum computing methods were thus computationally faster and produced, on average, shorter travel times by reducing traffic congestion. However, several sources indicated that the full potential of quantum computing is not yet attainable, as scaling quantum computers remains challenging due to qubit instability. If these scalability challenges can be resolved, the benefits offered by the technology will be extensive and significant.
Quantum computing offers transformative potential to solve complex optimisation problems in transportation and logistics, particularly those that involve large combinatorial decision spaces such as vehicle routing, traffic control, and supply chain design. Despite theoretical promise and growing empirical interest, its adoption remains limited. This systematic literature review synthesises fifteen peer-reviewed studies published between 2015 and 2025, examining the application of quantum and quantum-inspired methods to transport optimisation. The review identifies five key problem domains (vehicle routing, factory scheduling, network design, traffic operations, and energy management) and categorises the quantum techniques used, including quantum annealing, variational circuits, and digital annealers. Although several studies demonstrate performance gains over classical heuristics, most rely on synthetic datasets, lack statistical robustness, and omit critical operational metrics such as energy consumption and queue latency. Four cross-cutting barriers are identified: hardware limitations, data availability, energy inefficiency, and organisational readiness. The review identifies limited real-world deployment, a lack of standardised benchmarks, and scarce cost–benefit evaluations, highlighting key areas where further empirical work is needed. It concludes with a structured research agenda aimed at bridging the gap between laboratory demonstrations and practical implementation, emphasising the need for pilot trials, open datasets, robust experimental protocols, and interdisciplinary collaboration.
Traffic congestion remains a significant challenge in urban environments, necessitating innovative solutions for efficient traffic management. This paper explores the use of quantum annealing for traffic optimization, building on the pioneering work of Volkswagen and D-Wave. In 2017, a collaborative team demonstrated the potential of quantum annealing to optimize traffic flow by addressing a complex 418-car Quadratic Unconstrained Binary Optimization problem. Using a hybrid classical and quantum computing approach, they achieved notable improvements in traffic distribution. In this paper, we introduce a novel approach: Quantum Congestion-Focused Traffic Optimization (Q-CFTO). Our approach is designed to enhance traffic management optimization by breaking down the problem of optimizing traffic flow into smaller and more manageable subproblems rather than dealing with a single large optimization challenge. This decomposition facilitates the handling of the problem's complexity as embedding a large problem into a Quantum Processing Unit can be difficult. We successfully embedded the problem directly into the D-Wave Advantage Quantum Processing Unit with the "Pegasus" topology, significantly accelerating the solution process. The results demonstrate that our approach is both faster and more effective in real-world scenarios.
The growth of cities and the resulting increase in vehicular traffic poses significant challenges to the environment and citizens' Quality of Life. To address these challenges, a new algorithm has been proposed that leverages the Quantum Annealing paradigm and D-Wave's machines to optimize the control of traffic lights in cities. The algorithm considers traffic information collected from a wide urban road network to define activation patterns that holistically reduce congestion. An in-depth analysis of the model's behaviour has been conducted by varying its main parameters. Robustness tests have been performed on different traffic scenarios, and a thorough discussion on how to configure D-Wave's quantum annealers for optimal performance is presented. Comparative tests show that the proposed model outperforms traditional control techniques in several traffic conditions, effectively containing critical congestion situations, reducing their presence, and preventing their formation. The results obtained put in evidence the state-of-the-art of these quantum machines, their actual capabilities in addressing the problem, and opportunities for future applications.