Joshua Auld is a Computational Transportation Engineer in Argonne’s Transportation Research Systems Modeling and Control Group, in the Transportation and Power Systems division. He completed his Masters degree in May 2007 and his Doctorate in August 2011, in the Civil and Materials Engineering Department at the University of Illinois at Chicago with a concentration in transportation. He also completed a Post-Doctoral Appointment with the University of Illinois at Chicago and Argonne’s Transportation Research and Analysis Computing Center in December 2014.
Auld has experience in a variety of areas in transportation, with a primary focus on dynamic activity-based travel demand microsimulation models and the interactions between travel demand and intelligent transportation systems operations. He was an NSF IGERT fellow in the UIC Computational Transportation Science Program, a multidisciplinary research group focusing on the information technology aspects of transportation. As a researcher at Argonne, Auld has continued this work on computational issues in transportation in a project addressing issues of integrated modeling of travel demand and network operations.
Auld’s research has led to many journal publications, numerous peer-reviewed conference presentations and four book chapters, as well as a number of other presentations, technical reports and two guest editorials. Additionally, he has experience in teaching and developing course materials as a teaching assistant and guest lecturer in courses on highway design, travel demand modeling and transportation planning. He has supervised a number of graduate students in the completion of research projects. Outside of academic pursuits Auld has served the profession as a committee member on two Transportation Research Board Committees, including the Travel Forecasting Methods Committee (ADB40) and the Special Committee on Travel Forecasting Resources (ADB45), as well as acting as reviewer for several journals and organizing a number of workshops on agent-based modeling issues in travel demand modeling.