The Structure and Traffic Flow Anatomy of the Planet-Scale Urban Vehicular Mobility

A data-driven 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 research methodologies 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 more than 222 thousand routes and 25 million vehicular mobility images from 1091 online web cameras located in six different regions of the world. Our results centered around four major observations: i. Study shows that driving routes and visiting locations of regions demonstrate power-law distribution, indicating a planned or recently designed road infrastructure. ii. We represent regions 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. iii. Traffic densities show 80% temporal correlation during several hours of a day. iv. Modeling traffic densities against known theoretical distributions show less than 5% deviation for heavy-trailed models such as log-logistic and log-gamma distributions. We believe this work will provide a much-needed contribution to the research community for design and evaluation of future vehicular networks and smart cities.