The demand for rail transport is expected to increase due to growing mobility needs and shifts in transport policies. This growth will lead to a higher capacity utilization of existing networks, making disturbances more frequent. To minimize their impact, dispatchers engage in real-time traffic management by adjusting the timetable to resolve the arising conflicts. Given the limited time to identify high-quality solutions, developing computer-based optimization methods to support dispatchers can significantly enhance rail transport efficiency. However, this is a challenging task with high computational complexity particularly in large and dense railway networks. Quantum computing offers a possible research direction, as it may offer advantages over classical computing due to its unique properties. This paper introduces the fundamentals of quantum computing and explores its challenges and opportunities for real-time railway traffic management. For this purpose, the paper reviews existing approaches that use quantum computing in railway operations and related fields, and highlights open challenges. Finally, several further research directions are identified, ranging from detailed benchmarking to new algorithm development.
The demand for rail transport is expected to increase due to growing mobility needs and shifts in transport policies. This growth will lead to a higher capacity utilization of existing networks, making disturbances more frequent. To minimize their impact, dispatchers engage in real-time traffic management by adjusting the timetable to resolve the arising conflicts. Given the limited time to identify high-quality solutions, developing computer-based optimization methods to support dispatchers can significantly enhance rail transport efficiency. However, this is a challenging task with high computational complexity particularly in large and dense railway networks. Quantum computing offers a possible research direction, as it may offer advantages over classical computing due to its unique properties. This paper introduces the fundamentals of quantum computing and explores its challenges and opportunities for real-time railway traffic management. For this purpose, the paper reviews existing approaches that use quantum computing in railway operations and related fields, and highlights open challenges. Finally, several further research directions are identified, ranging from detailed benchmarking to new algorithm development.
In this work, we consider a practical railway dispatching problem: delay and conflict management on a single-track railway line. We examine the issue of train dispatching consequences caused by the arrival of an already delayed train to the segment being considered. This problem is computationally hard and often needs to be solved timely. Here, we introduce a quadratic unconstrained binary optimization (QUBO) model of the problem in question, compatible with the emerging quantum annealing technology. The model's instances can be executed on present-day quantum annealers. As a proof-of-concept, we solve selected real-life problems from the Polish railway network using D-Wave quantum annealers. As a reference, we also provide solutions calculated with classical methods, including those relevant to the community (linear integer programming) and a sophisticated algorithm based on tensor networks for solving QUBO problems.
The consequences of disruptions in railway traffic are the primary cause of passengers' dissatisfaction. Hence, appropriate dispatching decisions are necessary (e.g., by assigning the order of trains), given the numerous restrictions of traffic nature. The latter is perceived as an NP-hard problem. This paper outlines QUBO (quadratic unconstrained binary optimization) and HOBO (higher-order binary optimization) representations for dispatching problems of railway traffic management. Specifically, we consider minimal span between trains, minimal stay on stations, station/track occupation, and rolling stock circulation. The main result is the hybrid algorithm to deal with disturbances in rail traffic on single-, double- and multi-track lines; the demonstrative model illustrates the issue briefly. This algorithm can solve railway dispatching problems using the quantum annealer or any other QUBO-based optimization device.
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.
The rapid evolution of quantum technologies presents transformative opportunities for various industries, including the transport sector. As global supply chains grow increasingly complex and data-driven logistics become central to operational efficiency, quantum computing and quantum communication are emerging as potential enablers of next-generation transport systems. This research explores the intersection of quantum innovation and transportation, with a focus on enhancing efficiency, security, and decision-making processes in logistics and mobility. The study adopts a qualitative methodology grounded in an extensive literature review of scholarly publications, industry reports, and policy papers from the past decade. Sources were selected based on their relevance to quantum applications in transportation, including routing optimization, traffic flow prediction, secure communications, and fleet management. The review framework categorizes insights according to technological readiness, practical implementation, and strategic value in the transport sector. Findings from the literature suggest that quantum computing holds significant promise for solving large-scale optimization problems, such as dynamic route planning and freight allocation, which are currently limited by classical computational methods. Additionally, quantum key distribution (QKD) is highlighted as a secure communication solution for protecting sensitive data in autonomous and connected transport systems. Although real-world applications remain limited, pilot studies and simulations indicate measurable improvements in processing speed and security protocols. This research underscores the potential of quantum technologies to redefine competitive advantage in the transport sector. By integrating quantum solutions into existing digital infrastructure, companies may achieve faster, more secure, and more sustainable transport operations. The findings also call for interdisciplinary collaboration and proactive policy support to bridge the gap between theoretical potential and practical deployment.
We are in the noisy intermediate-scale quantum (NISQ) devices' era, in which quantum hardware has become available for application in real-world problems. However, demonstrations of the usefulness of such NISQ devices are still rare. In this work, we consider a practical railway dispatching problem: delay and conflict management on single-track railway lines. We examine the train dispatching consequences of the arrival of an already delayed train to a given network segment. This problem is computationally hard and needs to be solved almost in real time. We introduce a quadratic unconstrained binary optimization (QUBO) model of this problem, which is compatible with the emerging quantum annealing technology. The model's instances can be executed on present-day quantum annealers. As a proof-of-concept, we solve selected real-life problems from the Polish railway network using D-Wave quantum annealers. As a reference, we also provide solutions calculated with classical methods, including the conventional solution of a linear integer version of the model as well as the solution of the QUBO model using a tensor network-based algorithm. Our preliminary results illustrate the degree of difficulty of real-life railway instances for the current quantum annealing technology. Moreover, our analysis shows that the new generation of quantum annealers (the advantage system) does not perform well on those instances, either.
In the current era of noisy intermediate-scale quantum (NISQ) technology, quantum devices present new avenues for addressing complex, real-world challenges including potentially NP-hard optimization problems. Acknowledging the fact that quantum methods underperform classical solvers, the primary goal of our research is to demonstrate how to leverage quantum noise as a computational resource for optimization. This work aims to showcase how the inherent noise in NISQ devices can be leveraged to solve such real-world problems effectively. Utilizing a D-Wave quantum annealer and IonQ's gate-based NISQ computers, we generate and analyze solutions for managing train traffic under stochastic disturbances. Our case study focuses on the Baltimore Light RailLink, which embodies the characteristics of both tramway and railway networks. We explore the feasibility of using NISQ technology to model the stochastic nature of disruptions in these transportation systems. Our research marks the inaugural application of both quantum computing paradigms to tramway and railway rescheduling, highlighting the potential of quantum noise as a beneficial resource in complex optimization scenarios.