Abstract: Stochastic models of traffic flow are used in a variety of applications, e.g., traffic state estimation, travel time reliability, and traffic control. This talk will present techniques used to develop stochastic models. A main source of uncertainty in traffic dynamics is heterogeneity among drivers. This is captured using parametric uncertainty, resulting in stochastic microscopic models. These, in turn, are used to develop probabilistic traffic relations and stochastic Lagrangian models of traffic dynamics. Applications of the stochastic models, namely data assimilation, are presented.
Bio: Saif Jabari is an Assistant Professor of Civil and Urban Engineering at New York University in Abu Dhabi. His research interests lie at the interface between data analysis and theoretical traffic flow modeling. One of the main themes of his research is the development of methods for understanding and quantifying uncertainty in transportation systems. His recent work has focused on the development of real-time traffic analytics, including traffic state estimation, network-wide real-time dynamic control, and incident detection, localization, and sensor placement problems for urban traffic networks. Prior to joining NYUAD, Jabari was a Post-Doctoral Researcher in the Mathematical Sciences and Analytics Department at the IBM Watson Research Center in Yorktown Heights, NY. Jabari received his M.S. and Ph.D. in Civil Engineering from the University of Minnesota, Twin Cities and his B.Sc. degree in Civil Engineering from in the University of Jordan.