March 28, 2014
Dr. Srinivas Peeta: Professor of Civil Engineering, and Director of the NEXTRANS Center, Purdue University
Dr. Peeta is a Professor of Civil Engineering and the Director of the NEXTRANS Center, the USDOT’s Region 5 Regional University Transportation Center. He was the immediate past Chair of the Transportation Network Modeling Committee (ADB30) of the Transportation Research Board (TRB) of the National Academies, and he serves as a board member at the International Federation of Automatic Control (IFAC) Technical Committee on Transportation Systems. He received his B. Tech., M.S. and Ph.D. in Civil Engineering from the Indian Institute of Technology (Madras), California Institute of Technology (Caltech), and The University of Texas at Austin, respectively. He is an Editorial Board member of the journals Transportation Research, Part B, Intelligent Transportation Systems J., the KSCE J. of Civil Engineering, and Transportmetrica B: Transport Dynamics. He is the Area Editor for Transport Telematics for the journal Networks and Spatial Economics.
Dr. Peeta has authored over 215 technical publications, and has presented papers/talks/lectures at more than 340 invited and/or international conferences/symposiums in several countries. He is a co-editor of three books, and has won several awards, such as the NSF CAREER Award (1997), Wansik Excellence in Research Award (2004), the Exceptional Paper Award from TRB’s Traffic Signal Systems Committee (2007), the ASCE Walter Huber Research Award (2009), Visiting Distinguished Scholar, Taiwan (2009), and UniSA Distinguished Researcher Award, Australia (2010), to name a few. He has received more than US $30 million in sponsored research funding from a diverse set of funding sources such as the USDOT, NSF, FHWA, Indiana DOT, US DOE, NASA, US Department of Education, Canadian Department of Foreign Affairs, and Indo-US Science & Technology Forum.
Graph-based Modeling of Information Flow Propagation under Vehicle-to-Vehicle Communications
A vehicle equipped with a vehicle-to-vehicle (V2V) communications capability can continuously update its knowledge using its own experience and anonymously obtained travel experience data from other such equipped vehicles.
In a V2V communications based traffic system, the dynamics of traffic flow and inter-vehicle communication lead to the dynamics of information flow propagation.
In this context, this study proposes a graph-based multi-layer network framework to represent the V2V communications based traffic system as a complex system which is comprised of three coupled network layers: a physical traffic flow network, and virtual inter-vehicle communication and information flow networks.
The potential benefits of the proposed graph-based modeling of the information flow propagation are illustrated by the ability to identify vehicle knowledge in time and space using a simple graph-based reverse search algorithm and the storage of the information flow network as a single graph database.