Classical Methods Solve Urban Transport for Networks of 1000s Currently

A thorough review of existing research reveals the growing interest in quantum computing for solving complex decision problems in urban transport systems, led by Junxiang Xu of the University of New South Wales and colleagues. The review clarifies the specific role of quantum computing in transport planning and highlights key limitations of current quantum optimisation methods. These limitations concern scalability, strong performance, constraint representation, and engineering feasibility.

Current limitations hinder quantum optimisation for thorough urban transport modelling

The stable and reproducible advantages of quantum optimisation in real urban systems remain to be demonstrated. Comparing quantum methods with established classical optimisation methods, decomposition methods, metaheuristics, and reinforcement learning already provide transparent, scalable, and policy-interpretable solutions for medium and large-sized urban transport networks. In contrast, quantum methods largely contribute to the exploratory analysis of limited, discrete combinatorial subproblems rather than full system-level optimisation.

A shift from technology-driven application narrative towards problem-driven method selection is argued in this paper. Specific problem types where the exploratory use of quantum computing may be relevant to have been identified, including critical link and node vulnerability identification, combinatorial screening of congestion and failure scenarios, disaster-related extreme condition analysis, constrained path option selection, and small-scale facility location and investment option assessment. Hybrid frameworks represent a more realistic pathway for integrating quantum computing into urban transport research, where classical methods ensure system-level consistency and policy interpretability while quantum methods support local combinatorial exploration.

Urban transport systems constitute complex public decision systems, with planning and operation involving strategic, tactical, and operational decision levels that differ in time, space, and management scope, yet remain coupled throughout the network structure and operating states (Lin and Ban, 2013). Consequently, urban transport problems cannot be reduced to single-level technical optimisation tasks. Until stable engineering advantages are demonstrated, public agencies and researchers should prioritise method validation, scenario suitability, and cross-disciplinary collaboration. From a structural perspective, urban transport systems are typically characterised by large network scale, high-dimensional decision variables, coexistence of continuous and discrete decisions, and overlapping operational, regulatory, and institutional constraints (Pavez et al., 2025; Zou et al., 2025). These features determine the complexity in modelling and solving the transport system, and place high demands on the applicability and practical feasibility of computational methods (Chen et al., 2011; Barbati et al., 2012; Bastarianto et al., 2023). Urban transport decision-making can be categorised into strategic, tactical, and operational levels, each corresponding to distinct characteristics of transport problems, and together they define the decision environment for selecting the appropriate computational and methodological toolboxes.

Although quantum computing has received growing attention in combinatorial optimisation in recent years, its application in urban transport systems remains at an early stage, with limited literature and relatively concentrated research directions. Existing studies mainly attempt to apply typical problems in urban transport to quantum computing frameworks. Cooper innovatively explored the potential future applications of quantum computing in transport simulation and planning.

Dixit and Jian used quantum technology to achieve the fast estimation of vehicle driving cycle frequency, which is important for energy saving, emission reduction, and safety improvement. They later extended this work to transport network design, laying an initial foundation for introducing quantum technology into urban transport planning (Dixit and Niu, 2023). Qu et al. creatively integrated quantum computing with machine learning and developed a spatiotemporal quantum convolutional neural network for traffic congestion prediction. Schatakis et al. further demonstrated the reliability and practical value of quantum neural networks in traffic congestion prediction, providing technical support for the further improvement of urban short-term traffic flow control.

Dixit et al. proposed a quantum optimisation approach to search for optimal paths using real-time-dependent traffic data. Quantum-inspired heuristics developed by Tian et al. were validated for path planning in large-scale datasets. Liu et al. pointed out concerns about the extensive application of quantum technology in current transport and logistics fields, aligning with the motivation of this study. This study focuses on urban transport planning, supplementing details on quantum computing models and application scenarios and identifying feasible directions for future implementation.

Recent studies applied quantum annealing (QA) to optimise urban multicommodity flow problems and compared the computational advantages of QA algorithms over heuristic methods. Du et al. developed a framework to assess the potential scalability of quantum algorithms in air transport network design and considered the influences of multiple factors, such as qubit numbers and error rates. However, they argued that real-world application of quantum technology remained distant, mainly based on reflections on the limitations of quantum hardware and algorithms.

Overall, existing studies have largely focused on specific topics, such as problem mapping methods, variable encoding strategies, and computational performance of quantum or quantum-inspired solution frameworks under benchmark networks or case scenarios. Their research objectives mainly concentrate on method feasibility verification or performance comparison with classical algorithms. In contrast, there is rare discussion on the application scope of quantum optimisation methods across different decision levels in urban transport systems and their capacity to extend scientific questions.

Although some studies have begun to consider the limitations and generalisability, they did not provide constructive insights on how to practically solve the challenges in quantum computing-based urban transport planning. The practical deployment of quantum optimisation still faces substantive obstacles. A consistent and strong demonstration of quantum advantage on large-scale, real-world instances has yet to be achieved. Many reported successes rely on carefully-selected synthetic or toy benchmarks, where the design of test cases and the criteria for comparison remain open to bias and contestation.

Scalability, noise and durability across both algorithms and hardware have limited the prospects of systematic breakthroughs. Recent assessments of variational quantum algorithms indicate that their scalability and superiority over strong classical baselines remain far from conclusive. Field-specific reviews in transport suggest that current experiments with Quadratic Unconstrained Binary Optimisation (QUBO) reformulations on quantum annealers or hybrid solvers largely stay at the level of problem compatibility and benchmark exploration, and that consistent outperformance on realistic network scales remains elusive.

Several exploratory studies in the transport field have reformulated problems such as the vehicle routing problem (VRP), facility location-allocation, and evacuation route planning into QUBO form, enabling their execution on quantum annealers or hybrid quantum-classical solvers (Leonidas et al., 2023; Cattelan and Yarkoni, 2024). These efforts claim that quantum computing can provide new ways to address complex scheduling and routing tasks. However, further investigation reveals that these problems can already be solved by a wide range of mature classical methods. Mixed Integer Linear Programming (MILP) has been a common and effective method to deal with transport cases (Luathep et al., 2011; Demirel et al., 2016; Yuan et al., 2019; Baller et al., 2022). Decomposition strategies, such as Benders decomposition and column generation, can address large-scale formulations (Zeighami and Soumis, 2019; Lan et al., 2021; Li et al., 2022b). Heuristics and metaheuristics (e.g. genetic algorithms, simulated annealing, tabu search) have been extensively employed in industrial practice (Xu and Nair, 2024; Xu et al., 2024; Xu et al., 2025; Zhang et al., 2025; Kim et al., 2025). More recently, reinforcement learning and deep reinforcement learning frameworks have shown strong scalability and durability in planning and scheduling contexts (Shahab et al., 2024; Grumbach et al., 2024; Ngwu et al., 2025). By contrast, quantum methods typically require additional and often cumbersome reformulations during the modelling stage, e.g. embedding linear constraints or multi-objective conditions into the QUBO representation through penalty terms, or introducing auxiliary variables to maintain feasibility (Rovara et al., 2024; Niu et al., 2025a). Such transformations inevitably lead to rapid growth in model size, which in turn restricts applicability and efficiency at realistic network scales.

Moreover, recent quantum studies of VRP that tested QUBO encodings on actual or simulated quantum hardware explicitly indicate the performance bottlenecks in circuit depth, noise, and connectivity constraints, with most of the benchmark instances falling behind the strong classical baselines (Onah and Michielsen, 2025). These achievements suggest that, at present, quantum optimisation in transport remains largely exploratory rather than a demonstrably superior alternative to established classical methods. Accordingly, this study adopts a rational and reflective perspective to explore the role of quantum optimisation in urban transport planning. While continued exploration of quantum methods may support interdisciplinary exchange and contribute to longer-term methodological advances, it seems premature to portray them at this stage as a central solution for real-world urban transport problems. Therefore, this paper critically reviews the common optimisation problems in urban transport planning, highlights their effective solutions under classical algorithmic frameworks, as well.

Classical optimisation techniques currently outperform quantum approaches for large-scale urban

Decomposition methods, metaheuristics, and reinforcement learning currently deliver transparent, scalable, and policy-interpretable solutions for medium to large urban transport networks. Prior research demonstrated that classical methods could consistently optimise networks up to a size previously impossible for quantum computing to address effectively. This threshold signifies a shift from exploratory quantum applications to reliance on established techniques for full system optimisation, given current hardware limitations.

Quantum computing excels at analysing discrete subproblems, but its application to thorough, real-world transport systems remains limited by scalability and durability concerns. This research advocates for prioritising problem suitability over technological advancement when selecting optimisation methods for urban transport planning. These established methods successfully address network sizes that remain challenging for quantum computing to handle, demonstrating consistent optimisation capabilities.

Furthermore, quantum methods excel at analysing discrete subproblems, such as identifying critical infrastructure vulnerabilities or screening congestion scenarios, but struggle with full system-level optimisation. This suggests a valuable, albeit limited, role for quantum computing in supporting localised combinatorial exploration within broader transport models. However, these findings do not yet demonstrate stable, reproducible engineering advantages for quantum approaches in real-world applications, meaning significant hurdles remain before widespread practical implementation is feasible.

Quantum algorithms for targeted vulnerability assessment in urban transport networks

Urban transport planners face increasing pressure to optimise networks amidst growing populations and complex demands. Classical methods like decomposition, metaheuristics, and reinforcement learning currently provide scalable solutions, but the allure of quantum computing persists as a potential leap forward. However, this research highlights a critical tension: existing quantum algorithms excel at analysing isolated subproblems, such as identifying vulnerable network nodes, but struggle to integrate these insights into cohesive, system-wide optimisation.

Acknowledging that quantum computers currently excel at dissecting specific transport issues rather than managing entire networks is not a dismissal of the technology’s potential. Identifying vulnerable points or evaluating limited disaster scenarios, tasks where quantum computing demonstrates promise, offers valuable, targeted insights for planners. These localised analyses, though not system-wide solutions in themselves, can inform classical optimisation models, enhancing their precision and durability.

Researchers confirm that while quantum computers show promise for specific transport challenges, such as identifying critical network weaknesses, they currently fall short of optimising entire systems. Hybrid approaches, combining quantum analysis with established classical methods, offer a more pragmatic path forward, allowing planners to begin integrating quantum insights into existing workflows.

The research confirmed that quantum computing currently excels at analysing limited, discrete subproblems within urban transport networks, such as identifying critical links and nodes. This means it offers value for targeted exploration of specific issues, rather than providing complete, system-level optimisation solutions. Established classical methods remain more suitable for large-scale network planning, but hybrid frameworks combining both approaches present a practical way to integrate quantum insights. The authors suggest focusing on problem-driven method selection, utilising quantum computing for exploratory analysis of localised combinatorial challenges.