A Markov Jump Process Model for Urban Vehicular Mobility: Modeling and Applications

Vehicular networks have been attracting increasing attention recently from both the industry and research communities. One of the challenges in this area is understanding vehicular mobility, which is vital for developing accurate and realistic mobility models to aid the vehicular communication and network design and evaluation. Most of the existing works mainly focus on designing microscopic level models that describe the individual mobility behaviors. In this paper, we explore the use of Markov jump process to model the macroscopic level vehicular mobility. Our proposed simple model can accurately describe the vehicular mobility and, moreover, it can predict various measures of network-level performance, such as the vehicular distribution, and vehicular-level performance, such as average sojourn time in each area and the number of sojourned areas in the networks. Model validation based on two large scale urban city vehicular motion traces confirms that this simple model can accurately predict a number of system metrics crucial for vehicular network performance evaluation. Furthermore, we propose two applications to illustrate that the proposed model is effective in analysis of system-level performance and dimensioning for vehicular networks.