2025

Journal

Cluster Computing

Vehicle-as-a-Service (VaaS) refers to on-demand rides and the sharing/offering of various kinds of transportation facilities (e.g., smart buses, electric vehicles, business jets, autonomous cars), to move from a source to a destination across one or several regions. Coupled with smart transportation services, which are critical for addressing road network issues (e.g., traffic congestion, parking, safety), VaaS is increasingly adopted in smart cities. For example, a user may express his needs in terms of source/destination regions, time and cost constraints, in addition to preferred transportation modes and services (e.g., only connected buses and electric cars). This user profile is evaluated against each available VaaS, as well as the smart urban network (SUN) conditions. However, current solutions do not consider the customization of VaaS combinations and treat user requests as a traditional vehicle routing problem; while in a SUN context, processing VaaS requests not only consists of finding the optimal transportation path that meets user constraints (e.g., time, cost), but also selecting the top-rated combination of available VaaSs (e.g., sequence of smart buses) that meet the user profile (e.g., connectivity, specific facilities, transit restrictions) and trip constraints. To cope with these challenges, the goal of this paper is to develop an orchestration engine that allows the personalized selection and composition of VaaS services. We applied Fuzzy Relational Concept Analysis as an effective data analysis and clustering technique to solve the following issues: (i) representing VaaS-related knowledge and excluding low-quality VaaSs, (ii) clustering of VaaS services based on their common features and covered regions, (iii) clustering of smart urban regions based on their neighborhood and their frontier stations. At a second stage, a search method based on A* algorithm is proposed to find the optimal local paths in each traversed region of the smart urban network. Finally, a bottom-up lattice parsing method is defined to determine the VaaSs with high quality and high coverage capacity. Comparative experiments with state-of-the-art solutions proved the efficiency of our approach.

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