Towards Efficient Urban Network Mobility: A Cloud-Based Ride-Sharing System

Abstract

Cloud vehicle network (CVN) is a promising smart city service offering social and economic benefits. Within this framework, we propose a cloud service specifically designed for smart cities, utilizing a hybrid approach combining predictive queuing models and matching game theory. This hybrid solution effectively allocates ride-sharing vehicles and balances their traffic across different city zones. Additionally, we introduce a recommendation method for vehicle sharing. Our system’s primary objective is to control and manage urban traffic, with a specific focus on reducing empty vehicle journeys and minimizing passenger wait times. This objective is achieved through efficient vehicle allocation and passenger matching, facilitated by the hybrid model. Moreover, our system aims to provide an affordable vehicle-sharing service for passengers seeking cost-effective travel options, regardless of trip duration. Moreover, leveraging the power of cloud computing and fog computing, our approach formulates the vehicle allocation problem within specific zones using matching game theory and queueing theory. These powerful tools enable efficient vehicle and passenger matching, leading to reduced wait times and lower travel costs for passengers.