Abstract
A data-driven realistic design and evalua-
tion of vehicular mobility has been particularly chal-
lenging due to a lack of large-scale real-world mea-
surements in the research community. Current research
methodologies rely on arti cial scenarios, random con-
nectivity, and use small and biased samples. In this pa-
per, we perform a combined study to learn the struc-
ture and connectivity of urban streets and modeling and
characterization of vehicular trac 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 di erent re-
gions 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 represen-
tation exhibits small world properties with short path
lengths and large clustering coecient; iii. trac densi-
ties show 80% temporal correlation during several hours
of a day; iv. modeling trac 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.
