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.
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.
Quantum computing holds transformative potential for Intelligent Transportation Systems (ITS), offering solutions to complex and computationally intensive challenges in modern transportation networks. By leveraging quantum mechanics principles like superposition and entanglement, quantum algorithms can optimize traffic management, route planning, and logistics with unprecedented speed and accuracy. This can significantly enhance real-time traffic flow predictions, dynamic route optimization, and efficient resource allocation, particularly for electric vehicle networks, promoting sustainable and energy-efficient transportation solutions. However, integrating quantum computing into ITS faces challenges. Current quantum hardware is limited by issues like qubit instability, short coherence times, and high error rates, impeding large-scale applications. Additionally, developing hybrid systems that combine classical and quantum computing capabilities, and ensuring robust data privacy and security, are significant hurdles.
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
Modeling in transportation is essential for planning and operating current and future mobility and logistics systems, with significant implications for modern economies. However, many of these modeling tasks involve computationally complex problems that challenge traditional models and forecasting techniques. Quantum computing offers a promising way to address these difficulties. This systematic literature review examines the state-of-the-art in applying quantum computing to transport modeling, including and classifying 40 studies, selected through a systematic search of three databases using predefined inclusion and exclusion criteria. We identify three main types of quantum computing techniques used: quantum algorithmic primitives, quantum optimisation methods, and quantum machine learning approaches. With respect to the transport modeling problems addressed, the reviewed works cover forecasting demand, systems’ operation economic variables, predicting network states, estimating vehicle dynamics, and queueing systems. A notable trend is the application of quantum walks and quantum neural networks for the prediction of network state variables. Our review also underscores key challenges and research gaps, including the lack of standardized benchmarks and theoretical guarantees, particularly for quantum machine learning, the underexploited use of quantum walks for vehicle dynamics estimation, and quantum annealing for network state forecasting. We conclude with future research directions aimed at closing these gaps and building stronger theoretical foundations for sustained progress.
This analytical article rigorously evaluates the revolutionary capacity of quantumcomputing in enhancing network traffic optimisation and data routing, a field historicallycontrolled by conventional techniques. Due to the rapid expansion of data and the growingintricacy of network infrastructures, traditional methods are frequently constrained by theirinadequate capacity to effectively scale and oversee resources. Quantum computing, utilising theconcepts of superposition and entanglement, presents a hopeful solution to surpass theseconstraints by offering unparalleled processing capability and efficiency.The study starts by examining the fundamental concepts of quantum physics that serve as thefoundation of quantum computing. Superposition permits quantum bits (qubits) to concurrentlyembody and manipulate several states, whereas entanglement facilitates the interconnection ofqubits that are physically apart, hence enabling expedited and more intricate computations. Thesefeatures offer substantial benefits compared to classical bits, which are limited to binary states.We explore the precise quantum algorithms that have the ability to optimise networks, with aparticular focus on Grover's search algorithm and the Quantum Approximate OptimisationAlgorithm (QAOA). The Grover's technique provides a quadratic improvement in searchprocedures, enabling more efficient identification of optimal pathways in routing issues.Conversely, QAOA is well-suited for handling combinatorial optimisation issues, namely thosefound in network traffic management.A comprehensive analysis of current literature and case studies is performed to assess the practicaluses of quantum algorithms in network contexts. Metrics such as reducing latency, optimisingbandwidth utilisation, minimising packet loss, and effectively managing congestion are closely examined. Our study suggests that quantum computing has the potential to greatly improve theseperformance indicators. Quantum algorithms have the capability to handle large quantities of datasimultaneously, resulting in quicker identification of the best routes and more effective trafficcontrol. This leads to reduced latency and enhanced total network performance.The report also evaluates the existing technological and theoretical obstacles that impede themainstream implementation of quantum computing in network management. Important concernsencompass the reliability and consistency of qubits, the ability to repair errors, and the capacityfor quantum systems to be expanded. Furthermore, this study investigates the infrastructuralobstacles, such as the requirement for specialised hardware and the incorporation of quantumsystems into current network topologies. We emphasise the continuous progress in quantumtechnology, including the enhancement of qubits' stability and the refinement of quantum errorcorrection techniques, both of which are crucial for tackling these difficulties.Moreover, the research examines the possible long-term consequences of quantum computing onnetwork management paradigms. Quantum computing offers substantial enhancements inprocessing speed and efficiency, as well as presents novel approaches for solving complicatedoptimisation problems that are currently unsolvable using traditional methods. Our proposalinvolves investigating future research areas, specifically focusing on the advancement of hybridquantum-classical algorithms and the examination of quantum machine learning approaches foradaptive network management.
This paper explores the application of Quantum Neural Networks (QNNs) in the field of traffic prediction, an integral aspect of modern transportation management. Traditional machine learning methods like neural networks, decision trees, and support vector machines have been commonly used for traffic prediction. However, the advent of quantum computing offers promising new directions. QNNs, embodying principles of quantum mechanics, have the potential to handle large, complex traffic datasets with greater efficiency and possibly provide more accurate predictions. Despite being in the early stages of research and development, the potential advantages of QNNs in terms of handling complexity, leveraging quantum superposition, entanglement, and interference make them a compelling area of study. Nevertheless, the practical implementation of QNNs for traffic prediction entails overcoming significant challenges related to quantum computing hardware and the development of new quantum machine learning algorithms and frameworks. Ultimately, QNNs could revolutionize traffic prediction and numerous other fields.