Abstract
Context-aware applications of vehicular social networks (VSNs) play a significant role to achieve the goal of green transportation by sharing driving experience to reduce gasoline consumption. One of the main challenges is to evaluate the performance of these applications, which relies on the underlying VSN mobility model. In this paper, we investigate big urban traffic data to characterize essential features of urban mobility and construct large-scale green urban mobility models. We exploit the road and traffic information to enhance the trip generation algorithm and traffic assignment technique based on the weighted segments of roads. Besides, we perform extensive observations and corrections on the OpenStreetMap imported to simulation of urban mobility to make it analogous to real-world road topology. The experimental results and validation process show that the generated mobility models reveal realistic behavior required for analysis of context-aware applications of VSNs for green transportation systems.
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