The last decade has seen a sharp increase in the amount of data available to estimate vehicular traffic, pedestrians and crowds, and human mobility patterns. Furthermore the type of data available has also diversified dramatically. This evolution has been particularly quick in the last few years, due to the prolific growth of mobile phones and the datasets they generate.
The workshop will investigate techniques that are commonly used for traffic estimation with partial differential equations, ranging from centralized and decentralized nonlinear extensions of Kalman filtering to particle filters (subtopic 1). It will also focus on statistical methods, in particular for the arterial networks where data is often sparse (subtopic 2). Subtopic 3 will cover optimization methods and games applied to networks of PDEs, with specific emphasis on traffic models. Subtopic 4 examines how to estimate mobility patterns on these networks, using massive datasets generated from call detail records and other positioning data. Subtopic 5 will explore estimation problems such as tracking and localization, generated at the scale of small groups of pedestrians and crowds. Finally, the fundamental challenges and new approaches to maintain privacy of users who contribute the data to be used for estimation will be the focus of subtopic 6.
This workshop will include a poster session; a request for posters will be sent to registered participants in advance of the workshop.
Hani Mahmassani, Co-chair
(Northwestern University)
Lyudmila Mihaylova
(University of Sheffield)
Hans van Lint
(Technische Universiteit te Delft)
Daniel Work, Co-chair
(University of Illinois at Urbana-Champaign)
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