arXiv subjects

Chence Niu

Publications and source records attributed to Chence Niu.

6 recordsLinked to original sources

arXiv2026

Quantum percolation theory for dynamic propagation connectivity of transport networks

Connectivity degradation in transport networks under structural disturbance is a central problem in network resilience research. Existing methods rely mainly on percolation theory and topological connectivity measures. They focus on whether paths exist and whether connected components fragment. These approaches cannot capture functional degradation where network topology remains intact but propagation ability has already declined substantially. This paper introduces quantum percolation theory into transport network connectivity analysis and proposes Dynamic Propagation Connectivity (DPC) as a new measure that characterises network propagation ability under disturbance. By mapping a transport network under disturbance into a propagation operator system, this paper establishes a spectral analysis framework for DPC and defines the time-averaged participation index as its core quantification. This paper provides a series of rigorous theoretical results. DPC remains constant under homogeneous disturbance and degrades under heterogeneous disturbance. This paper establishes a quantitative relationship between the degradation rate, the minimum eigenvalue spacing of the propagation operator, and heterogeneous deviation strength. This paper proves a separation theorem between DPC and algebraic connectivity. It derives an analytical expression for DPC and a second-order perturbation approximation on the ring graph. Numerical experiments on three transport benchmark networks verify all theoretical conclusions and confirm degradation monotonicity, separation from algebraic connectivity, and degradation amplification by network size. This paper provides a theoretical framework for transport network resilience assessment that goes beyond topological connectivity.

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arXiv2026

Quantum percolation based dynamic propagation connectivity for critical-area identification in transport networks

Transport networks often lose functionality through gradual degradation in link operating conditions before topological disconnection occurs. Link-centred and binary percolation measures identify important facilities or connectivity failures, but they provide limited information on which spatial areas cause the largest loss of network-wide propagation capability. This paper develops a Dynamic Propagation Connectivity (DPC) metric based on quantum percolation for critical-area identification in transport networks. Time-varying link travel times are converted into continuous propagation strengths, which define a Hermitian propagation operator at each observation time. Candidate regions are then evaluated by a regional degradation experiment that measures the resulting loss of DPC. The method is applied to a benchmark Sioux Falls network and six Florida road networks during the post-Hurricane Irma disruption and recovery period, using 1,281 five-minute observation times. The benchmark confirms that the regional DPC score identifies a predefined structurally critical corridor. In the Florida networks, the identified critical areas differ from regions selected by link count, local degradation, edge betweenness, algebraic connectivity, and classical percolation. In Networks 1 to 4, DPC and classical percolation rankings have negative Spearman correlations, showing that continuous propagation degradation and binary fragmentation reveal different vulnerability patterns. Robustness tests under alternative travel time scaling, degradation strength, and grid size show stable results, with mean rank agreement between 0.84 and 0.96. The findings extend transport resilience analysis based on percolation from binary connectivity loss to continuous propagation degradation and provide a spatial diagnostic tool for regional monitoring, emergency planning, and recovery prioritisation.

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arXiv2026

Quantum Optimisation for Transport Vulnerability Identification

Transport network vulnerability analysis plays a crucial role in safeguarding urban resilience. Traditional vulnerability identification approaches have provided valuable insights, yet they face two major limitations. First, the number of disruption scenarios increases combinatorially with the number of disrupted links considered simultaneously, making classical approaches computationally prohibitive. Second, most studies approximate the impacts of multiple simultaneous link failures through linear aggregation, which fails to capture the nonlinear interaction effects observed in real networks. To address these gaps, we reformulate the bi-level Mixed-Integer Nonlinear Programming (MINLP) model into a quantum-compatible Quadratic Unconstrained Binary Optimisation (QUBO) structure, enabling parallel exploration of complex disruption scenarios while incorporating nonlinear interaction effects. We develop a hybrid optimisation framework that integrates the quantum optimisation algorithm with the Frank-Wolfe method to validate the model's effectiveness on the small-scale network. Then, we further verify the framework through the D-Wave hardware across benchmark networks of different scales, including Sioux Falls, Anaheim, Chicago Sketch, and Berlin Full, to examine scalability and feasibility. The results show that this framework achieves strong solvability and stability. In particular, optimisation for large and larger networks is completed within minutes (Approximately 2.8 minutes for the 914-link, 9.8 minutes for the 2950-link, and 31.2 minutes for the 6018-link on D-Wave), demonstrating a computational efficiency improvement by one to two orders of magnitude compared with classical metaheuristic algorithms. These findings highlight the feasibility and potential of applying quantum computing to network vulnerability identification and open a new avenue for resilience-oriented planning.

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arXiv2026

Quantum optimisation in cities: Limitations and prospects of urban transport systems

Recently, quantum computing has gained attention in urban studies as a tool for complex transport planning problems, but its role remains unclear. This paper reviews quantum computing research in urban transport planning and highlights major limits in scalability, robustness, constraint handling, and engineering feasibility.Stable and reproducible advantages of quantum optimisation in real urban systems have yet to be shown. By comparing quantum methods with established classical optimisation methods, it is found that decomposition methods, metaheuristics, and reinforcement learning already provide transparent, scalable, and policy-interpretable solutions for medium and large-sized urban transport networks. In contrast, the contribution of quantum methods largely lies in the exploratory analysis of limited, discrete combinatorial subproblems rather than full system-level optimisation. It is argued in this paper for a shift from technology-driven application narrative towards problem-driven method selection. From an urban transport planning perspective, we have identified the specific problem types where the exploratory use of quantum computing may be relevant, including critical link and node vulnerability identification, combinatorial screening of congestion and failure scenarios, disaster-related condition analysis, constrained path option selection, and small-scale facility location and investment option assessment. It is concluded that hybrid frameworks represent a more realistic pathway for integrating quantum computing into urban transport research, in which classical methods ensure systemlevel consistency and policy interpretability while quantum methods support local combinatorial exploration. Until stable engineering advantages are demonstrated, public agencies and researchers should prioritise method validation, scenario suitability, and cross-disciplinary collaboration.

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arXiv2026

Data-driven identification of critical links in transport networks using quantum annealing

In urban transport systems, time-varying demand and network conditions cause the importance of infrastructure elements to evolve, requiring the identification of period-specific critical links to support systemlevel risk and resilience analysis. However, static or time-averaged network analyses struggle to capture the temporal variation of infrastructure importance at the city scale. To address this gap, this study proposes a time-dependent critical link identification framework for large-scale urban transport networks. The problem is formulated as a Quadratic Unconstrained Binary Optimisation (QUBO) model and solved using quantum annealing on D-Wave hardware. Empirical analysis using real-world traffic data reveals a strong temporal concentration of critical links. Rather than persistently influencing system performance, critical links emerge mainly within a small number of key time windows, during which even limited disruptions can lead to substantial network delay amplification. These findings demonstrate the value of time-dependent analysis for risk screening, stress testing, and resilience-oriented transport management.

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arXiv2025

Quantum Computing in Transport Science: A Review

Quantum computing, leveraging the principles of quantum mechanics, has been found to significantly enhance computational capabilities in principle, in some cases beyond classical computing limits. This paper explores quantum computing's potential to address complex, large-scale problems in transportation systems. It focuses on three principal paradigms: Gate-based quantum computing, Quantum annealing, and Quantum machine learning, which, though based on gate-based quantum computing, is treated as distinct due to its unique methods and applications. Each paradigm's foundational concepts, practical applications, and potential impacts on the field are discussed to provide a comprehensive overview of quantum computing strategies and their future implications.

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