GG Brown 2355
Title: Estimating traffic incidents and quantifying resilience to events
This talk will describe new approaches to monitor traffic in the presence of incidents and events. In the first part of the talk, traffic state estimation and incident detection is posed as a hybrid state estimation problem. A continuous variable denotes the traffic state and a discrete model variable identifies the location and severity of an incident. The hybrid state is estimated using a multiple model particle smoother to accommodate the nonlinearity and switching dynamics of the traffic incident model. The proposed method is evaluated through numerical experiments using CORSIM as the model of the true state.
The second part of the talk proposes a method to quantify the resilience of transportation systems to events using GPS data from taxis. The method works by computing the historical distribution of pace between various regions of a city and measuring the pace deviations during an unusual event. This method is applied to a dataset of nearly 700 million taxi trips in New York City, which is available for download at http://publish.illinois.edu/dbwork/open-data/. The analysis indicates that Hurricane Sandy impacted traffic conditions for more than five days, and caused a peak delay of two minutes per mile. Practically, it identifies that the evacuation caused only minor disruptions, but significant delays were encountered during the post-disaster reentry process.
Bio: Dan Work is an assistant professor in Civil & Environmental Engineering and a research assistant professor at the Coordinated Science Laboratory at the University of Illinois at Urbana-Champaign. He earned a Bachelor of Science degree (2006) from the Ohio State University, and a Master of Science (2007) and Ph.D. (2010) from the University of California, Berkeley, each in civil engineering. Work was a guest researcher at Microsoft Research, Redmond in 2010 and a visiting researcher at Nokia Research Center, Palo Alto from 2008-2010. His research interests are control, estimation, and optimization of transportation systems, mobile sensing, and inverse modeling and data assimilation. Prof. Work has won a number of awards including the CAREER Award from the National Science Foundation in 2014 and the IEEE ITSS Best Dissertation Award in 2011.