A cloud computing framework for real-time carpooling services

摘要

Traffic congestion is a serious problem in many urban areas around the world. It causes a multitude of negative impacts from producing air pollution and carbon dioxide emissions, to wasting fuel and increasing travel time, among many others. Carpooling is one of the most effective solutions to traffic congestion, and involves decreasing the number of cars on a roadway by increasing vehicle occupancy. In this paper, we propose an intelligent carpool system called BlueNet. The proposed system provides a real-time carpool service by involving two significant proposed modules: the Mobile Client module and the Cloud Global Carpool Services module. In our proposed Mobile Client module, users can use their smartphones or other devices to find carpool matches by utilizing the proposed Cloud Global Carpool Services module via the mobile communication network. The following Cloud Global Carpool Services module is used to deal with carpool requests and generate suitable carpool match results through the proposed real-time carpool-matching algorithm. To develop a carpool service that can be used around the world, we integrated the open GIS system with abundant global geographical information into the proposed Cloud Global Carpool Services module. The experimental results show that the proposed real-time carpool matching algorithm dramatically reduces the processing time required to generate the carpool matching results. This is accomplished by dynamically adjusting the random ratio value.