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Realistic modeling of vehicular mobility has been particularly challengingdue to a lack of large libraries of measurements in the research community. Inthis paper we introduce a novel method for large-scale monitoring, analysis,and identification of spatio-temporal models for vehicular mobility using thefreely available online webcams in cities across the globe. We collectvehicular mobility traces from 2,700 traffic webcams in 10 different cities forseveral months and generate a mobility dataset of 7.5 Terabytes consisting of125 million of images. To the best of our knowl- edge, this is the largest dataset ever used in such study. To process and analyze this data, we propose anefficient and scalable algorithm to estimate traffic density based onbackground image subtraction. Initial results show that at least 82% ofindividual cameras with less than 5% deviation from four cities followLoglogistic distribution and also 94% cameras from Toronto follow gammadistribution. The aggregate results from each city also demonstrate that Log-Logistic and gamma distribution pass the KS-test with 95% confidence.Furthermore, many of the camera traces exhibit long range dependence, withself-similarity evident in the aggregates of traffic (per city). We believe ournovel data collection method and dataset provide a much needed contribution tothe research community for realistic modeling of vehicular networks andmobility.

Towards Realistic Vehicular Network Modeling Using Planet-scale Public Webcams