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. 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 compare the performance of two types of quantum optimization algorithms (Quantum Approximate Optimization Algorithm and Quantum Annealing) to evaluate performance of bike sharing rebalancing optimization. In this preliminary work, we compared the performance of QAOA on IBM-Qiskit and Quantum Annealing on two D-Wave solvers (QPU and hybrid solvers) for a case study involving rebalancing across three bike stations.
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. 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 compare the performance of two types of quantum optimization algorithms (Quantum Approximate Optimization Algorithm and Quantum Annealing) to evaluate performance of bike sharing rebalancing optimization. In this preliminary work, we compared the performance of QAOA on IBM-Qiskit and Quantum Annealing on two D-Wave solvers (QPU and hybrid solvers) for a case study involving rebalancing across three bike stations.
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.
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.
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.
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.
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.
Managing contemporary smart cities demands computational capabilities that far exceed what conventional processors can deliver. Traffic signal coordination across hundreds of intersections, real-time balancing of renewable energy grids, and cryptographically securing millions of IoT endpoints each constitute combinatorially hard problems that classical algorithms cannot resolve at urban scale within practical time budgets. This paper addresses these limitations by developing and evaluating a hybrid quantum-classical architecture designed for practical deployment in mid-sized smart cities. Three core technical modules are proposed and validated: a Quantum Approximate Optimization Algorithm (QAOA) module for adaptive traffic signal control, a quantum machine learning (QML) module for short-term renewable energy forecasting, and a multi-node Quantum Key Distribution (QKD) architecture for quantum-resilient IoT security. Simulation experiments conducted on IBM Qiskit using realistic urban datasets from Bangalore, India, demonstrate that QAOA-based signal optimization reduces average intersection wait times by 47–53% compared to fixed-timing baselines and executes 4–5× faster than genetic algorithm solvers on equivalent instances. The QML forecasting model achieves 8–12% lower RMSE than classical LSTM networks on solar generation prediction tasks. The QKD-ECC hybrid security framework sustains a 99.9% device authentication success rate while remaining quantum-safe under post-quantum threat models. A phased deployment roadmap tailored for Indian smart cities is provided, alongside a frank assessment of current hardware constraints and practical mitigation strategies. The findings contribute a unified integration blueprint, quantified performance benchmarks on urban data, and evidence-based guidance for cities beginning the transition toward quantum-enhanced infrastructure.