Efficient transportation management is an important aspect of urban sustainability, impacting economic growth, environmental sustainability, and quality of life. This research explores the potential of Quantum Computing (QC) to address spatial optimization problems in transportation systems. By leveraging the principles of quantum mechanics, this research aims to enhance the efficiency and effectiveness of transportation networks through QC-based solutions to challenges such as reducing the spread of viruses on road networks, dynamic rebalancing of \nomenclature{BSS}{Bike Sharing Systems}Bike Sharing Systems (BSS), clustering of BSS stations, and Feature Selection (FS) for predictive models. The study begins by examining the potential of QC in solving combinatorial optimization problems, specifically focusing on minimizing exposure to COVID-19 during city journeys. A novel QC-based approach is developed for the dynamic rebalancing of BSS, which is a critical component of BSS management. The research further explores the clustering of BSS stations using \nomenclature{QML}{Quantum Machine Learning}Quantum Machine Learning (QML) techniques to enhance system management and improve user satisfaction. Additionally, this research introduces a QC-based FS method to improve the accuracy of predictive models, utilizing spatial data to optimize station placement and service availability. The proposed methodologies are validated through different experiments and real-world data, demonstrating significant improvements in computational efficiency and solution quality compared to traditional methods. Overall, this research advances the application of QC in transportation systems, providing a QC-based framework for future studies and practical implementations in urban transportation management. It highlights the transformative potential of QC in addressing pressing urban mobility challenges, paving the way for more sustainable and efficient transportation networks.
Efficient transportation management is an important aspect of urban sustainability, impacting economic growth, environmental sustainability, and quality of life. This research explores the potential of Quantum Computing (QC) to address spatial optimization problems in transportation systems. By leveraging the principles of quantum mechanics, this research aims to enhance the efficiency and effectiveness of transportation networks through QC-based solutions to challenges such as reducing the spread of viruses on road networks, dynamic rebalancing of \nomenclature{BSS}{Bike Sharing Systems}Bike Sharing Systems (BSS), clustering of BSS stations, and Feature Selection (FS) for predictive models. The study begins by examining the potential of QC in solving combinatorial optimization problems, specifically focusing on minimizing exposure to COVID-19 during city journeys. A novel QC-based approach is developed for the dynamic rebalancing of BSS, which is a critical component of BSS management. The research further explores the clustering of BSS stations using \nomenclature{QML}{Quantum Machine Learning}Quantum Machine Learning (QML) techniques to enhance system management and improve user satisfaction. Additionally, this research introduces a QC-based FS method to improve the accuracy of predictive models, utilizing spatial data to optimize station placement and service availability. The proposed methodologies are validated through different experiments and real-world data, demonstrating significant improvements in computational efficiency and solution quality compared to traditional methods. Overall, this research advances the application of QC in transportation systems, providing a QC-based framework for future studies and practical implementations in urban transportation management. It highlights the transformative potential of QC in addressing pressing urban mobility challenges, paving the way for more sustainable and efficient transportation networks.
Quantum Machine Learning (QML) is a field that combines the principles of Quantum Computing (QC) and Machine Learning (ML). QC works by taking advantage of the properties of quantum physics , such as superposition and entanglement . To fully realize the potential of this technology, more research is necessary, as the field of QML is still in its early stages. Since QC technologies and devices continue to develop quickly, it is important to identify the use cases and applications that benefit the most. This paper investigates the potentials of QC, and more specifically, Quantum Annealing (QA), for clustering real-world data in transportation systems. The Bike Sharing System (BSS) is used as a case study applying a clustering model on QA computers. The main contribution of this research is to introduce a hybrid model to cluster stations in a BSS by solving it as a Constraint Satisfaction Problem (CSP) problem with different methods on a QA computer using a real-time dataset. In addition to the practical contribution, this research also offers theoretical advancements in the field of computational optimization by defining a new topology for the input data that is compatible with QC topology (e.g., Chimera topology). The goal of real-time clustering BSS stations based on dynamic and static datasets is, in fact, to assist decision-makers in better managing and minimizing the risk of bike unavailability at each station and rebalancing bikes shared. Three different methods have been used to determine the number of clusters, and Euclidean, Manhattan, Pearson , and Spearman dissimilarity functions have been applied to cluster the stations. The evaluation is done using the magnitude vs. cardinality approach. The distribution of the stations, magnitude, and cardinality of the results indicate the potential to use QC for clustering for a real-world application, e.g., BSS.
Smart Mobility is the key component of Smart City initiative that are being explored throughout the world. The bike-sharing system (BSS) aims to provide an alternative mode of Smart Mobility transportation system, and it is being widely adopted in urban areas. The use of bikes for short-distance travel helps to reduce traffic congestion, reduce carbon emissions, and decrease the risk of overcrowding. Effective bike sharing system operations requires rebalancing analysis, which corresponds to transferal of bikes across various bike stations to ensure the supply meets expected demand. In this work, we present Quantum Approximate Optimization Algorithm(QAOA), a variational hybrid quantum-classical algorithm that has shown significant computational advantages in solving combinatorial optimization problems such as bike sharing system rebalancing problem (BSS-RBP). Here, we minimize the overall distance travelled by the transport vehicle across various bike station. In this preliminary work, we demonstrate the application of QAOA using the IBM-Qiskit quantum computing simulator for rebalancing analysis across three bike locations.
The bike-sharing system (BSS) aims to provide an alternative mode of public transportation that are being adopted in urban cities. The use of the bike for short-distance travel aids in mitigating traffic congestion problems, reduce carbon emissions, and decrease the risk of overcrowding during a pandemic like COVID-19, thereby satisfying the urban-mobility needs of the residents. The key success of incorporating urban-mobility through BSS lies behind the prediction of bikes by identifying the pick-up and drop-off operations in each station. The main challenge includes the demand prediction for the number of bikes available for pick-up and drop-off during a specific point in time. Quantum Computing has been increasingly gaining popularity for its superior computational performance over similar classical algorithms. In this paper, we will illustrate potential applications of Quantum Bayesian networks, which are quantum-equivalent to classical Bayesian networks for probabilistic bike demand prediction.
Smart Mobility is the key component of Smart City initiative that are being explored throughout the world. The bike-sharing system (BSS) aims to provide an alternative mode of Smart Mobility transportation system, and it is being widely adopted in urban areas. The use of bikes for short-distance travel helps to reduce traffic congestion, reduce carbon emissions, and decrease the risk of overcrowding. Effective bike sharing system operations requires rebalancing analysis, which corresponds to transferral of bikes across various bike stations to ensure the supply meets expected demand. A critical part for a bike sharing system operations is the effective management of rebalancing vehicle carrier operations that ensures bikes are restored in each station to its target value during every pick-up and drop-off operations. In this work, we present potential applications of Quantum Bayesian networks, which are quantum-equivalent to classical Bayesian networks for probabilistic rebalancing cost prediction under uncertainty. In this preliminary work, we demonstrate the proposed approach using IBM-Qiskit and compared the results classically using Netica for a case study involving rebalancing across three bike stations.
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.
The escalating impacts of climate change and the increasing demand for sustainable development and natural resource management necessitate innovative technological solutions. Quantum computing (QC) has emerged as a promising tool with the potential to revolutionize these critical areas. This review explores the application of quantum machine learning and optimization techniques for climate change prediction and enhancing sustainable development. Traditional computational methods often fall short in handling the scale and complexity of climate models and natural resource management. Quantum advancements, however, offer significant improvements in computational efficiency and problem-solving capabilities. By synthesizing the latest research and developments, this paper highlights how QC and quantum machine learning can optimize multi-infrastructure systems towards climate neutrality. The paper also evaluates the performance of current quantum algorithms and hardware in practical applications and presents realistic cases, i.e., waste-to-energy in anaerobic digestion, disaster prevention in flooding prediction, and new material development for carbon capture. The integration of these quantum technologies promises to drive significant advancements in achieving climate resilience and sustainable development.