Spatial Quantum Computation in Graph Optimization Problems in Transportation Applications

Abstract

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.