Vehicle-as-a-Service (VaaS) refers to on-demand rides and the sharing/offering of various kinds of intelligent transportation facilities (e.g., smart buses, electric vehicles, autonomous cars) to move from a source to a destination across one or several regions. Coupled with smart transportation systems–which are critical for addressing road network issues such as traffic congestion, parking shortages, and safety concerns–VaaS is increasingly being adopted in smart cities. For example, a user may specify their needs in terms of source and destination stations, time and cost constraints, as well as preferred transportation modes and services (e.g., only connected buses and cars). Such a user profile is evaluated against available VaaS options under current and anticipated urban network conditions. However, current solutions do not support the customization of VaaS compositions and often treat user requests as traditional vehicle routing problems. In a smart mobility context, however, processing VaaS requests involves not only finding the optimal transportation path that meets user constraints (e.g., time, cost, transfer stations) but also selecting the top-rated combination of available VaaSs (e.g., a sequence of smart buses) with respect to the user profile (e.g., connectivity needs, specific facilities) and the quality of smart urban services. To address these challenges, the goal of this paper is to develop a multi-modal recommender system that enables the personalized selection, composition, and scheduling of VaaS services while optimizing trip constraints (e.g., minimizing trip time and cost, and maximizing VaaS availability and reputation). As a multi-population technique, Composite Particle Swarm Optimization (CPSO) is applied to aggregate the optimal set of high-quality and high-coverage VaaSs with respect to the requested trip. The regions composing the trip are explored using a modified A* search algorithm to find the optimal local (partial) path in each traversed region of the smart urban network. Comparative experiments involving two metaheuristics, a greedy algorithm, and a fuzzy clustering technique demonstrate the efficiency and superiority of our CPSO-based approach, achieving approximately a 28% improvement over its closest competitors.
