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Quantum computing introduces powerful computational paradigms capable of addressing optimization and machine learning challenges that are increasingly critical in transportation engineering. This paper presents a comprehensive survey of how quantum algorithms and models are being applied across transportation domains, including intelligent transportation systems, traffic management, vehicle routing, logistics, and autonomous systems. Unlike prior reviews that primarily introduce quantum theory, we systematically connect fundamental quantum principles-qubits, superposition, entanglement, and quantum gates-with specific transportation use cases. We synthesize recent findings showing that the Quantum Approximate Optimization Algorithm, Grover’s search, and hybrid quantum–classical solvers can improve performance in combinatorial problems such as vehicle routing and dynamic traffic signal control, while quantum machine learning models (e.g., Quantum Convolutional Neural Networks, Quantum Generative Adversarial Networks, and clustering algorithms) demonstrate promise in traffic prediction, anomaly detection, and autonomous vehicle perception. Our review highlights that although quantum hardware limitations (noise, scalability, connectivity) remain significant, empirical studies already suggest measurable improvements in solution quality and computational efficiency when compared to classical heuristics. The key contribution of this survey is to map the landscape of quantum computing in transportation engineering, identify application-specific advantages and current bottlenecks, and outline future research directions toward scalable, domain-specific quantum algorithms. By doing so, we provide transportation researchers and practitioners with a structured foundation to integrate quantum approaches into emerging mobility systems, with implications for efficiency, safety, sustainability, and equity.
Transportation systems combine physical infrastructure, human behavior, and algorithmic decision-making at enormous scale. Planning and operations routinely rely on combinatorial optimization (routing, timetabling, resource allocation), control (signals, congestion management), and machine learning (forecasting, detection, decision support). Classical methods excel in many settings, but problem size, real-time constraints, and multi-objective structure continue to push the community toward new computational tools.
Quantum computing introduces a different set of primitives—superposition, interference, and entangled state manipulation—that can change the cost of specific subroutines, and quantum annealing offers a direct hook from discrete optimization to energy minimization. This survey organizes the literature for transportation engineering and intelligent transportation systems (ITS) audiences: what quantum models exist, how they map to mobility problems, and what barriers matter for credible evaluation.
Figure 1: Evolution of quantum computing from foundational concepts through algorithmic milestones, the optimization turn, the NISQ era, and quantum ML, with capabilities versus constraints and a mapping to transportation tasks (routing, traffic control, ITS analytics, autonomy support).
The survey connects standard quantum concepts to the kinds of models transportation researchers already use:
Framing matters: the contribution of a survey is not to claim universal speedups, but to clarify which problem encodings appear in the literature and what assumptions each approach requires.
Across the transportation quantum literature, recurring application threads include:
Managing congestion, reallocating capacity, or coordinating subnetworks often leads to large discrete decision spaces. Quantum and hybrid methods are explored as alternatives or supplements to classical decomposition, metaheuristics, and commercial solvers—typically at proof-of-concept scale first.
Traveling salesman, vehicle routing, and rich variants with time windows and capacity constraints are canonical NP-hard structures. Surveys emphasize QUBO encodings, annealing demonstrations, and variational approaches, alongside the importance of constraint handling and feasibility when moving from toy instances to operational settings.
Adaptive signals and corridor coordination can be cast as sequential decision or combinatorial timing problems. Quantum proposals must be judged against mature classical adaptive control and reinforcement-learning baselines, including latency and safety requirements.
Quantum machine learning proposals touch traffic prediction, pattern analysis, and anomaly detection. A survey-level takeaway is methodological: claims of advantage should be read alongside issues such as data loading, trainability, noise, and comparisons to strong classical models already deployed in ITS.
Most practical roadmaps assume hybrid quantum–classical computation: classical outer loops handle decomposition, preprocessing, constraint repair, and learning, while quantum subroutines tackle selected subproblems or surrogate objectives. This division matches current hardware limits and encourages problem-first benchmarking rather than hardware demonstrations alone.
Credible progress in this space depends on addressing:
Published in the IEEE Transactions on Intelligent Transportation Systems, this survey offers a structured entry point for transportation researchers, ITS practitioners, and quantum computing specialists who need a shared vocabulary and a map of methods, use cases, and open gaps.