With the support of Yu Zhang’s (USF) Ph.D. student, Hualong Tang, their research titled “Design of an automated advanced air mobility flight planning system (AAFPS)” was selected to receive the Amazon Research Award (ARA).
Dr. Yu Zhang and Hualong Tang were among the 101 recipients of the 2020 Amazon Research Awards, who represent 59 universities in 13 countries. Each award is intended to support the work of one to two graduate students or postdoctoral students for one year, under the supervision of a faculty member. Proposals were reviewed for the quality of their scientific content, their creativity, and their potential to impact both the research community, and society more generally.
Deployable Decentralized Routing Strategies using Envy-Free Incentive Mechanisms for Connected and Autonomous Vehicle Environments Webinar
April 30, 2021 at 4:40 p.m. ET
Frederick R. Dickerson Chair and Professor,
School of Civil and Environmental Engineering,
H. Milton Stewart School of Industrial and Systems Engineering
Georgia Institute of Technology
Register for Webinar: https://ucdavis.zoom.us/webinar/register/WN_dVfkMJXTSsyL0dFyKpp3SQ
Routing strategies using dynamic traffic assignment have been proposed in the literature to optimize system performance. However, challenges have persisted in their deployability and effectiveness due to inherent strong assumptions on traveler behavior and availability of network-level real-time traffic information, and the high computational burden associated with computing network-wide flows in real-time. To address these gaps, this study proposes an incentive-based decentralized routing strategy to nudge the network performance closer to the system optimum in a traffic system with connected and autonomous vehicles (CAVs). The strategy consists of three stages. The first stage incorporates a local route switching dynamical system to approximate the system optimal route flow in a local area based on vehicles’ knowledge of local traffic information. This system is decentralized in the sense that it only updates the local route choices of vehicles in this area to circumvent the high computational burden associated with computing the flows on the entire network. The second stage optimizes the route for each CAV by considering individual heterogeneity in traveler preferences (e.g., the value of time) to maximize the utilities of all travelers in the local area. Constraints are also incorporated to ensure that these routes can achieve the approximated local system optimal flow of the first stage. The third stage leverages an expected envy-free incentive mechanism to ensure that travelers in the local area can accept the optimal routes determined in the second stage. They prove that the incentive mechanism is expected individual-rational and budget-balanced. The study analytically shows that the proposed incentive-based decentralized routing strategy can enhance network performance and user satisfaction in a connected and autonomous traffic environment.
Bio: Srinivas Peeta is the Frederick R. Dickerson Chair and Professor in the School of Civil and Environmental Engineering and the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology. He is also Principal Research Faculty at the Georgia Tech Research Institute. Previously, he was the Jack and Kay Hockema Professor at Purdue University and the Director of the NEXTRANS Center, formerly the U.S. Department of Transportation’s (USDOT’s) Federal Region 5 University Transportation Center. He was also the Associate Director of USDOT’s Center for Connected and Automated Transportation (CCAT). He received his B.Tech., M.S. and Ph.D. degrees from the Indian Institute of Technology (Madras), Caltech and The University of Texas at Austin, respectively. Dr. Peeta’s research interests are multidisciplinary, and broadly span transportation and infrastructure systems.
Efficient Bayesian optimization techniques for high-dimensional urban mobility problems
April 16, 2021 at 4:40 p.m. ET
Associate Professor, Department of Decision Sciences
Abstract: In this talk, Prof. Osorio will discuss the opportunities and challenges of designing simulation based optimization (SO) algorithms to tackle high-dimensional urban mobility problems. An important component in high-dimensional problems is the exploration exploitation tradeoff. Their past work has focused mainly on improving the exploitation capabilities of SO algorithms. In this work, they focus on designing exploration techniques suitable for high-dimensional spaces. They consider a Bayesian optimization setting and propose the use of a simple analytical traffic model to specify the covariance function of a Gaussian process. They show how this enables the Bayesian optimization method to more efficiently sample in high-dimensional spaces. They present validation experiments on synthetic low-dimensional problems. They then apply the method to a high-dimensional traffic control problem for Midtown Manhattan, in NYC.
Bio: Carolina Osorio is an Associate Professor in the Department of Decision Sciences at HEC Montréal, where Osorio holds the SCALE AI Research Chair in Artificial Intelligence for Urban Mobility and Logistics. Osorio is also a Visiting Faculty at Google Research. Osorio’s work develops operations research techniques to inform the design and operations of urban mobility systems. It focuses on simulation-based optimization algorithms for, and analytical probabilistic modeling of, congested urban mobility networks. Osorio was recognized as one of the outstanding early-career engineers in the United States by the National Academy of Engineering’s EU-US Frontiers of Engineering Symposium, and is the recipient of a U.S. National Science Foundation CAREER Award, an MIT CEE Maseeh Excellence in Teaching Award, an MIT Technology Review EmTech Colombia TR35 Award, an IBM Faculty Award and a European Association of Operational Research Societies (EURO) Doctoral Dissertation Award.
Dynamic Driving and Routing Games for Autonomous Vehicles on Networks: A Mean Field Game Approach Webinar
Dynamic Driving and Routing Games for Autonomous Vehicles on Networks: A Mean Field Game Approach
April 9, 2021, 10:40 a.m. ET
Xuan (Sharon) Di
Department of Civil Engineering and Engineering Mechanics
Smart Cities Center, Data Science Institute
Columbia University in the City of New York
As this era’s biggest game-changer, autonomous vehicles (AV) are expected to exhibit new driving and travel behaviors, thanks to their sensing, communication, and computational capabilities. However, a majority of studies assume AVs are essentially human drivers but react faster, “see” farther, and “know” the road environment better. We believe AVs’ most disruptive characteristic lies in its intelligent goal-seeking and adapting behavior. Building on this understanding, we propose a dynamic game-based control leveraging the notion of mean-field games (MFG). Prof. Di will first introduce how MFG can be applied to the decision-making process of a large number of AVs. To illustrate the potential advantage that AVs may bring to stabilize traffic, she will then introduce a multi-class game where AVs are modeled as intelligent game-players and HVs are modeled using a classical non-equilibrium traffic flow model. Last but not the least, she will talk about how the MFG-based control is generalized to road networks, in which the optimal controls of both velocity and route choice need to be solved for AVs, by resorting to nonlinear complementarity problems.
Bio: Xuan (Sharon) Di is an Associate Professor in the Department of Civil Engineering and Engineering Mechanics at Columbia University in the City of New York since September 2016 and serves on a committee for the Smart Cities Center in the Data Science Institute. Prior to joining Columbia, she was a Postdoctoral Research Fellow at the University of Michigan Transportation Research Institute (UMTRI). She received her Ph.D. degree from the Department of Civil, Environmental, and Geo-Engineering at the University of Minnesota, Twin Cities in 2014.