Multi-Modal Vaas Selection in Smart Mobility Networks via Spectral Hyper-Graph Clustering and Quantum-Driven Optimization

Abstract

In recent years, smart mobility networks have experienced significant growth due to the integration of key technologies such as cloud computing, edge intelligence, and the Internet of Things (IoT) into transportation infrastructure. When combined with the principles of service-oriented computing (SOC), various transportation modes now feature intelligent capabilities, including eco-driving assistance, emergency service integration, V2X communication, environmental sensors, in-vehicle infotainment, Over-the-Air (OTA) updates, driver behavior monitoring, and AI-powered assistance. This has led to the emergence of Connected Vehicle as a Service (CVaaS) as a new paradigm for smart vehicles and transportation services. However, with the increasing complexity of AI-driven features and integration with smart city infrastructure, traditional recommender systems can no longer meet user requirements such as personalized connectivity preferences and eco-friendly route optimization. CVaaS recommendations also inherit challenges from traditional transportation systems, including multi-modal integration (e.g., coordinating smart buses and autonomous vehicles), environmental considerations (e.g., smart parking and dedicated lanes for autonomous cars), uncertain demand, user trust, regulatory compliance, and data privacy concerns. In this article, we address the challenges of multi-modal transportation and environmental uncertainty, such as traffic congestion and VaaS demand fluctuations. By modeling Smart Urban Network (SUN) traffic and VaaS demand, we predict congestion patterns and VaaS availability using a Long Short-Term Memory (LSTM) model. Additionally, we apply Spectral hyper-graph Theory to cluster the SUN into closely connected regions, identifying traversed areas for trip requests. These preprocessing steps help eliminate high-congestion zones and low-demand VaaS services, improving trip efficiency. Finally, inspired by the combinatorial nature of VaaS selection, we propose a Quantum-Inspired variant of the Gravitational Search Algorithm (Q-GSA) to explore and evaluate possible VaaS combinations, ultimately selecting an optimal set of smart transportation services. Experimental comparisons with four benchmark methods confirm the superiority of our approach in terms of efficiency and solution quality.