Modeling and characterization of urban vehicular mobility using web cameras

Realistic design and evaluation of vehicular mobility has been particularly challenging due to a lack of large-scale real-world measurements in the research community. Current mobility models and simulators rely on artificial scenarios, random connectivity, and use small and biased samples. In this paper, we perform a combined study to learn the structure and connectivity of urban streets and modeling and characterization of vehicular traffic densities on them. Our dataset is a collection of 154 thousand routes and 12 million vehicular mobility images from 730 online web cameras located in four different cities. First, our study shows that driving routes and visiting locations of cities demonstrate power law distribution, indicating a planned or recently designed road infrastructure. Second, we represent cities by network graphs in which nodes are camera locations and edges are urban streets that connect the nodes. Such representation exhibits small world properties with short path lengths and large clustering coefficient. Third, traffic densities show 80% temporal correlation during several hours of a day. Finally, modeling these densities against known theoretical distributions show less than 5% deviation for Log-logistic and Gamma distribution. We believe this work will provide a much-needed contribution to the research community for realistic and data-driven design and evaluation of vehicular networks.