Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Bayesian Modeling of Dynamic Scenes for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
CRAWDAD: a community resource for archiving wireless data at Dartmouth
ACM SIGCOMM Computer Communication Review
A measurement study of vehicular internet access using in situ Wi-Fi networks
Proceedings of the 12th annual international conference on Mobile computing and networking
Study of a bus-based disruption-tolerant network: mobility modeling and impact on routing
Proceedings of the 13th annual ACM international conference on Mobile computing and networking
Spatio-temporal variations of vehicle traffic in VANETs: facts and implications
Proceedings of the sixth ACM international workshop on VehiculAr InterNETworking
Planet-scale human mobility measurement
Proceedings of the 2nd ACM International Workshop on Hot Topics in Planet-scale Measurement
Simulation of vehicular ad-hoc networks: Challenges, review of tools and recommendations
Computer Networks: The International Journal of Computer and Telecommunications Networking
Challenges of intervehicle ad hoc networks
IEEE Transactions on Intelligent Transportation Systems
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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 and use small and biased samples. To overcome these challenges, we introduce a novel framework for large-scale monitoring, analysis, modeling, and visualization of vehicular traffic using freely available online webcams. We follow a data-driven approach that examine six metropolitan regions' more than 800 locations and 25 million vehicular mobility records around the world. Initial analysis of traffic densities show 80% temporal correlation during various hours of a day. The modeling of empirical traffic densities against known theoretical models show less than 5% deviation for heavy-tailed distributions such as Weibull. We believe this framework and the dataset provide a much-needed contribution to the research community for realistic and data-driven design and evaluation of vehicular networks.