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ITE Seminar


Friday, May 19, 2017 4:00 PM - 5:00 PM

Dr. Pitu Mirchandani  School of Computing, Informatics and Decision Systems Engineering  ATLAS Research Center, Arizona State University

Abstract

While driving on your favorite route to your destination, have you ever wondered why the technology you are seeing as far as traffic management is concerned is so antiquated? My answer to that organizations that manage the traffic are not “real-time optimizers” and do not use available data effectively to set signal and manage traffic. On the other hand, managers in modern production systems, logistics, and air traffic are gradually using more and more real-time data analytics, and optimization. Mirchandani argues that while computation and communication technologies have seen an unprecedented growth in innovations and capabilities, traffic control systems have lagged in using these technologies partially due to the difficulty and costs to change/upgrade control and communication technologies in publicly operated traffic management systems.  Mirchandani says that many current systems use “no data” to set optimum signal timings for the actual traffic being experienced. Recently, some systems such as adaptive traffic control and adaptive ramp metering systems use “point” detectors such as loop detectors or video emulators of loops,” some” real-time to change signal timings based on currently observed traffic. Mirchandani argues that with our current state-of-practice and-art of related technologies, traffic management can be more effective using a real-time trajectory data and proactive strategies. Mirchandani’s group is developing MIDAS traffic management system. MIDAS hopes to demonstrate the synergistic use of a cyber-physical infrastructure consisting of smart-phone type devices; cloud computing, wireless communication, to manage vehicles in the complex urban network – through the use of traffic controls, route advisories and road pricing/rewards – to jointly optimize drivers’ mobility as well as achieve the sustainability goals of reducing energy usage and improving air quality. A key element of MIDAS is the real-time streaming data collection and data analysis and the subsequent traffic management through proactive traffic controls and advisories. MIDAS research is supported through a multidisciplinary NSF project that is the nexus of several areas: real-time image processing, real-time traffic prediction and supply/demand management, and big data processing/management through cloud computing.

Speaker Biography

Dr. Pitu B. Mirchandani [UCLA, BS/MS in Engineering; MIT, SM (Aero and Astro) ScD in Operations Research] is a Professor of Computing, Informatics, and Decision Systems Engineering at Arizona State University where he holds the AVNET Chair for Supply Chain Networks. He is also a Senior Sustainability Scientist within the Global Institute of Sustainability and the Director of Advanced Transportation and Logistics Algorithms and Systems (ATLAS) Research Center. For close to 40 years, Pitu Mirchandani has been studying relevant problems on Dynamic Stochastic Networks, with interests in models and systems for making strategic/tactical/operational decisions in dynamic and stochastic networked environments. Problems related to traffic flows on transportation networks can be typically addressed as such. Mirchandani’s contributions are in: (1) Location Decision Modeling, (2) Traveler and Vehicle Routing Models, (3) Real-time Data-Driven Decision Systems, and (4) general theoretical contributions to OR modeling, methods and algorithms. He has authored/co-authored four books and approximately 230 articles. Dr. Mirchandani is a member of IEEE, INFORMS, IIE, TRB, and a charter member of ITS-Arizona, where he was awarded the “Member of the Year” in 2007. He became a Fellow of INFORMS in 2015 and recently, in 2017, Fellow of IEEE.


Kayla Fleskes
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