Data-driven vehicular mobility modeling and prediction

Abstract

Vehicular networks have been recently attracting an increasing attention from both the industry and research communities. One of the challenges in this area is the understanding of vehicular mobility and further propose accurate and realistic mobility models to aid the vehicular communication and networks design and evaluation. In this chapter, different from the current works focusing on designing microscopic level models that are describing the individual mobility behaviors, we are exploring the use of open Jackson queuing network frameworks to model the macroscopic level vehicular mobility. The proposed intuitive model can accurately describe the vehicular mobility, and further predict various measures of network-level performance. These measures include the vehicular distribution and vehicular-level performance, such as average sojourn time in each area and the number of sojourned areas in the vehicular networks. Model validation based on two large-scale urban vehicular motion traces reveals that such a simple model can accurately predict a number of system measure concerned with the vehicular network performance. Moreover, we develop two applications to illustrate the proposed model's effectiveness in the analysis of system-level performance and dimensioning of vehicular networks.