The 7th NYUAD Transportation Symposium

Speaker Bios and Abstracts

• Scott Moura, UC Berkeley | Integrating Power & Transportation Networks: The Opportunities and Challenges

  Biography

  Dr. Scott Moura is the Clare and Hsieh Wen Shen Endowed Distingiushed Professor in Civil & Environmental Engineering, Director of the Energy, Controls, & Applications Lab (eCAL), PATH Faculty Director, and Chair of Engineering Science at the University of California, Berkeley. He received a BS degree from the University of California, Berkeley, CA, USA, and MS and PhD degrees from the University of Michigan, Ann Arbor, in 2006, 2008, and 2011, respectively, all in mechanical engineering. From 2011 to 2013, he was a Post-Doctoral Fellow at the Cymer Center for Control Systems and Dynamics, University of California, San Diego. In 2013, he was a Visiting Researcher at the Centre Automatique et Systèmes, MINES ParisTech, Paris, France. His research interests include control, optimization, and machine learning for batteries, electrified vehicles, and distributed energy resources.

  Dr. Moura is a recipient of the National Science Foundation (NSF) CAREER Award, ASME Dynamic Systems and Control Divisoin Young Investigator Award, Carol D. Soc Distinguished Graduate Student Mentor Award, the Hellman Fellowship, the O. Hugo Shuck Best Paper Award, the ACC Best Student Paper Award (as advisor), the ACC and ASME Dynamic Systems and Control Conference Best Student Paper Finalist (as student and advisor), the UC Presidential Postdoctoral Fellowship, the NSF Graduate Research Fellowship, the University of Michigan Distinguished ProQuest Dissertation Honorable Mention, the University of Michigan Rackham Merit Fellowship, and the College of Engineering Distinguished Leadership Award.

  Abstract

  This talk discusses the opportunities and challenges of integrating electrified transportation into electric power grids. We first review the overarching trends and challenges posed by transportation electrification and decarbonization, including grid capacity and charging infrastructure. Next, we discuss recent research results for analyzing these coupled networks, and operating charging infrastructure to enable integration. Highlighted projects include SlrpEV - Smart Learning Research Pilot for Electric Vehicle charging stations — and EEZ Mobility — a tool for enabling Equitable Electrification of Zero-Emissions Mobility.

• Oliver Gao, Cornell University | Shared Use of Electric Autonomous Vehicles: Air Quality and Health Impacts of Future Mobility in the United States

  Biography

  Dr. Gao is the Director of the Cornell Systems Engineering and Howard Simpson Professor of Engineering at Cornell University. Gao is an international leading expert in urban infrastructure, transportation, and health (environmental health, public health) systems analytics/modeling and innovation for healthy living in smart communities. His research focuses on urban technology, data science, integrated computational engineering models, and trans-disciplinary systems solutions for intelligent urban systems, low carbon and low emission transportation, and human-centered urban design for environment and public health. He is an elected member in the graduate fields of 1) Civil and Environmental Engineering, 2) Computer Science and Engineering; 3) Air Quality in Earth and Atmospheric Science, and 4) Systems Engineering at Cornell University.

  As the founding director, Gao spearheads cross-disciplinary systems research at the Center for Transportation, Environment, and Community Health (CTECH). Leveraging behavioral and economic sciences, epidemiology, information technology, and environmental and transportation sciences and technologies, Gao leads CTECH with a vision for systems innovations in research, teaching, and workforce development through diversity that supports sustainable mobility of people and goods while preserving the environment and improving community health.

  Abstract

  Vehicle electrification, automation, and shared mobility — also referred to as the transportation three revolutions (3Rs) — are the emerging trends in future mobility. This study performed a comprehensive integrated analysis to investigate the potential future development of passenger transportation in the United States. A technical-economic mobility model, a chemical transport model, and a health impact assessment tool were utilized. This study first adopted several assumptions for vehicle sales under the impact of the 3Rs, and made projections to 2050 for vehicle stocks, distance travel, energy use, and carbon dioxide (CO2) emissions.

  This study then quantified the impacts of changing emissions on concentrations of fine particulate matter and associated health benefits. Compared to a projected 2050 business-as-usual case, the wide use of electrification could lead to reductions of ~50% in petroleum consumption and ~75% in CO2 emissions, and obtain health benefits of 5500 prevented premature deaths, corresponding to $58 billion annually.

  The net energy impacts of automation are highly uncertain, and the improved efficiency from automation might be offset by an increase in travel. Sharing would bring additional benefits. The combination of the 3Rs could maximize the energy savings, carbon mitigations, and health benefits. The results of this study suggest that policies/incentives are needed to promote the transition from single-occupied conventional vehicles to shared electric vehicles.

• Mikhail Chester, Arizona State University | Rigidly Designed Transportation Systems for Climate Uncertainty and Doubt

  Biography

  Dr. Chester is a professor of Civil, Environmental, and Sustainable Engineering and the director of the Metis Center for Infrastructure and Sustainable Engineering at Arizona State University’s School of Sustainable Engineering and the Built Environment. He manages a research program focused on infrastructure disruption and innovation, describing the challenges and needs for transforming critical systems for the Anthropocene. A large portion of his work centers on adaptation to climate change and other extreme events considering infrastructure design and operation, people, and changing environmental hazards (including heat, wildfires, and flooding). In 2017 he was awarded ASCE’s Huber Early Career Research Prize for his contributions to infrastructure resilience and sustainability. He is a co-author of the U.S. Fifth National Climate Assessment Transportation chapter and contributing author of the U.N. Intergovernmental Panel on Climate Change Sixth Assessment Report (AR6) chapter on Cities, Settlements, and Key Infrastructure. He has recently published two books: The Rightful Place of Science: Infrastructure in the Anthropocene, and Urban Infrastructure: Reflections for 2100.

  Abstract

  Transportation systems are caught between legacy systems that emphasize priorities from a bygone era and a future marked by climate and other deep uncertainties that increasingly threaten how systems function and are governed. There is a decoupling between what our transportation systems are capable of doing and what we need them to do. Climate change represents just one of many Anthropocene disruptions, and so far our climate adaptation responses emphasize approaches that appear increasingly insufficient and focused solely on robustness-centric thinking. New approaches to managing transportation and other infrastructures in the Anthropocene are needed.

  In the face of climate change and Anthropocene disruption how infrastructure agencies make sense of change, reorganize to pivot between stability and chaos, manage failures, create agility and flexibility, and build knowledge will be critical to what services are and how they are delivered.

• Jinwoo Lee, KAIST | Optimal Battery Electric Bus Planning and Its Environmental and Economic Impacts

  Biography

  Dr. Jinwoo Lee is an Associate Professor at the Cho Chun Shik Graduate School of Mobility at the Korea Advanced Institute of Science and Technology (KAIST). His research focuses on sustainable transportation infrastructure systems, with a specific emphasis on shared, autonomous, and(or) electric mobility technologies. He received his PhD and MS in Civil and Environmental Engineering from the University of California, Berkeley, and his BS degree from KAIST.

  Before joining KAIST, he worked as a postdoctoral associate at New York University Abu Dhabi and as a research assistant professor in the Department of Electrical Engineering at the Hong Kong Polytechnic University.

  Abstract

  Urban Air Mobility (UAM) is an emerging transportation mode that offers fast and direct travel within urban networks. A shift in travel demand from existing ground modes to UAM can reduce travel time for UAM users and alleviate ground congestion for those continuing to use ground transport. However, it is necessary to determine whether UAM can generate enough demand to outweigh its substantial implementation costs. This evaluation encompasses generalized social cost reduction, mobility efficiency and equity for users, and agency profitability.

  In this study, we introduce a methodology to assess UAM demand within existing urban ground transportation systems using a stochastic user equilibrium model. We create a super-network that integrates ground and air networks and categorize individuals into four groups based on private vehicle ownership and UAM acceptance. Using a multinomial logit model with a hierarchical structure, we split the demand for each group among available modes. This is further distributed into paths in a sub-network, utilizing a logit-based stochastic network loading approach. The equilibrium in both mode and route choice allows us to estimate the shift in demand towards UAM.

  In our numerical study conducted on a Sioux Falls network, we find that introducing UAM can enhance urban transportation systems, previously dominated by private vehicles, taxis, and ground transit. This improvement is particularly evident under conditions of high travel demand and limited ground road capacity. Additionally, the sensitivity analysis emphasizes the significance of increasing UAM acceptance rates and optimizing fare determinations to maximize its benefits.

• Joseph Chow, NYU Tandon School of Engineering | Fleet Routing Advances Toward Electric Mobility Ecosystems

  Biography

  Dr. Joseph Chow is an Institute Associate Professor at the NYU Tandon School of Engineering’s Civil and Urban Engineering Department with affiliations at CUSP and Rudin Center for Transportation Policy & Management. Chow is an NSF CAREER award recipient, a former Canada Research Chair, and the co-founding Deputy Director of the C2SMART University Transportation Center at NYU. He is the Chair of the Subcommittee on Route Choice & Spatiotemporal Behavior at TRB and former TSL Cluster Chair and elected Urban Transportation SIG Chair at INFORMS. He has published about 90 journal articles since 2010 and is an editor for three transportation journals including Transportation Research Part B. Dr. Chow received his PhD ('10) at UC Irvine and his MEng (’01) and BS (’00) at Cornell University.

  Abstract

  Electric mobility systems tend to be more operationally costly because of longer charging times and limited availability of charging infrastructure. More efficient routing is necessary to mitigate these issues. We developed an algorithm that considers dynamic passenger transfers for electric ridepooling service MOIA. We also developed a planning model for operating a fleet of on-demand charging-as-a-service mobile chargers as part of an NSF grant. These new methods allow mobility providers to more sustainably serve emerging electric Mobility-as-a-Service ecosystems. Future directions are discussed.

• Tierra Bills, UCLA | How Do Travelers Respond Under Conditions of Extreme Heat in the US?

  Biography

  Dr. Tierra Bills is an Assistant Professor in the Civil Engineering and Public Policy Departments at UCLA. She joined UCLA after spending two and a half years as an Assistant Professor at Wayne State University and three years as a Michigan Society Fellow and Assistant Professor at the University of Michigan. Prior to her fellowship at UMich, Dr. Bills worked as a Research Scientist at IBM Research Africa for three years, in Nairobi Kenya. Much of Dr. Bills’ research focuses on investigating the social impacts of transportation investment. She develops advanced travel-demand models to investigate individual and household-level transportation equity effects, for the purpose of designing transportation systems that will provide equitable allocations of transportation benefits and costs.

  Her latest project aims to understand the travel modeling benefits of high-quality, targeted surveys on travel behaviors of disadvantaged travelers for model estimate. Areas of interest include Transportation equity analysis, emerging data sources for travel demand modeling, transportation accessibility measurement, and transit design and reliability. Dr. Bills holds a BS in Civil Engineering Technology from Florida A&M University (‘08), and MS (’09) and PhD (’13) degrees in Transportation Engineering from the University of California, Berkeley.

  Abstract

  Extreme temperature events have been steadily increasing since the 1980s and this increase has been directly tied to global climate change. Approximately 1,300 people in the US die due to extreme heat exposure, making extreme heat the number one weather-related cause of death in the US. At the same time, travel activities using non-auto modes in particular, drive travel exposure to extreme heat events. While scholars are beginning to investigate more general traveler responses to extreme temperature events, there is limited understanding of how travel activity patterns may change under conditions of extreme heat.

  Using household travel surveys from four metropolitan areas in the US, and air temperature records from the National Oceanic and Atmospheric Administration (NOAA), we investigate the impacts of extreme heat on travel behavior, with a focus on trip chaining complexity, spatial variation within regions, and implications for disadvantaged travelers. We argue for targeted attention to impacts on disadvantaged traveler groups, as they are more likely to experience transportation-related exposure to extreme heat as well as greater risk of health-related harms due to such exposure.

• Jason Cao, University of Minnesota | Can an Identified Built Environment Correlate of Car Ownership Serve as a Practical Tool for Planning Interventions?

  Biography

  Dr. Jason Cao is a professor at the Humphrey School of Public Affairs, University of Minnesota, Twin Cities. He specializes in land use and transportation interaction, the effects of ICT on travel behavior, and planning for quality of life. He has published about 140 peer-reviewed papers and edited four books. Dr. Cao is internationally well-known for his research on residential self-selection in the relationships between the built environment and travel behavior. His recent work focuses on the applications of machine learning approaches in addressing nonlinear relationships between variables. Dr. Cao is the Co-Editor-in-Chief of Transportation Research Part D. He received his degrees from University of California, Davis and Tsinghua University.

  Abstract

  Many studies have shown that built environment attributes (such as population density and transit access) have negative relationships with car ownership, supporting planning efforts of using built environment interventions to mitigate car ownership. However, most studies have overlooked the possible plateau association — within a certain range of a built environment variable, there is little variation in car ownership as the built environment variable increases. Applying gradient-boosting decision trees to data in the Minneapolis-St. Paul metropolitan area, this study reveals the complexity of the nonlinear relationships between built environment attributes and car ownership. The results show that although population density and intersection density are strongly and negatively related to car ownership, their effects occur on both ends of the two variables. This finding suggests that reducing car ownership through population and intersection densification is unrealistic in planning practice. On the other hand, directing population growth around centers and densifying transit stops are promising interventions for car ownership mitigation.

• Yafeng Yin, University of Michigan | End-to-end Learning of Transportation Network Equilibrium via Implicit Layers

  Biography
  
  Dr. Yafeng Yin is Donald Cleveland Collegiate Professor of Engineering and Donald Malloure Department Chair of Civil and Environmental Engineering at the University of Michigan, Ann Arbor. He works on transportation systems analysis and modeling and has published nearly 150 refereed papers in leading academic journals. He currently serves as Area Editor of Transportation Science and Associate Editor of Transportation Research Part B: Methodological and was the Editor-in-Chief of Transportation Research Part C: Emerging Technologies between 2014 and 2020. Dr. Yin received his PhD from the University of Tokyo, Japan in 2002, his master’s and bachelor’s degrees from Tsinghua University, Beijing, China in 1996 and 1994 respectively.

  Abstract

  The current paradigm of transportation network equilibrium modeling is instrumental in designing efficient and environmentally sustainable transportation networks. In this presentation, I will present results from our latest research aimed at reshaping this paradigm using an end-to-end learning approach. We note that previous models have been constructed via a "bottom-up" assembly approach, in which the selection of behavior model for travel choices or the calibration of the supply function is divorced from the end goal of the model building, i.e., prescribing an equilibrium flow distribution that matches observations as closely as possible. Capitalizing on recent advances in game theory and machine learning, we aim to forge an end-to-end framework that seamlessly learns both demand and supply-side components, as well as the equilibrium state, directly from empirical data.

• Yoonjin Yoon, KAIST | Learning Urban Dynamics from Urban Mobility Patterns - Two Deep Learning Studies

  Biography
  
  Yoonjin Yoon is an associate professor of Civil and Environmental Engineering at Korea Advanced Institute of Science and Technology with a joint appointment in the Graduate School of Artificial Intelligence and the Graduate School of Data Science. Her main research area is computational transportation science focusing on urban mobility. Her most recent efforts involve modeling short-term traffic prediction problems with graph neural networks, and deep urban region representation learning with topological data analysis. Her earlier work in the U.S. has dealt with geometric air traffic flow optimization and optimization of infrastructure maintenance. Yoonjin is the recipient of the Mid-Career Award from Korea National Research Foundation (NRF), and the PI of NRF Basic Research Lab Grant Deep Traffic. She received BS in Mathematics from Seoul National University, dual MS degrees in Computer Science, and Management Science and Engineering from Stanford University. She received her PhD in Civil and Environmental Engineering from the University of California, Berkeley in 2010.

  Abstract

  People movement are both the causes and results of urban dynamics such as economic outcomes and social interactions. In this talk, two deep representation learning studies are presented to learn the key urban characteristics from human mobility data at a finer scale. Heterogeneous Urban Graph Attention Network (HUGAT) utilizes the Urban-HIN (Heterogeneous Information Network), constructed from Heterogeneous Urban Graph, meta-paths, and meta-path neighbors to model the composite relations among regions combining geo-spatial and human activity data. Experiments in New York City show that HUGAT outperforms state-of-the-art models across tasks like predicting personal income, poverty rates, region popularity, and spatial clustering. These results underscore HUGAT's efficacy and its capacity to extract insights from readily available urban data sources, facilitating informed urban policy decisions without extensive surveys. In MobiCLR, we focus on the social vulnerability based on near real-time taxi and ride-hailing time series data. We propose the mobility time series contrastive learning, which extracts the semantic information about inbound and outbound-specific feature through instant-wise contrastive loss. In the New York case study, MobiCLR outperformed the comparable state-of-art models. Moreover, transferability test demonstrates the potential to apply the model to unassessed regions.

• Samer Madanat, NYU Abu Dhabi | Multimodal Transportation Systems Protection against Sea Level Rise

  Biography
  
  Samer M. Madanat is the Dean of Engineering at NYUAD. He is also the Xenel Distinguished Professor of Engineering Emeritus and former Chair of the Department of Civil & Environmental Engineering (from 2012 to 2015) at the University of California at Berkeley.

  From 2005 to 2014, he served as the Director of the UC Berkeley Institute of Transportation Studies. He received a B.Sc. in Civil Engineering from the University of Jordan in 1986, and a MS and PhD in Transportation Systems from MIT in 1988 and 1991 respectively.

  His research and teaching interests are in the area of Transportation Infrastructure Management, with an emphasis on modeling facility performance and the development of optimal management policies under uncertainty. More recently, his research has focused on the field of the environmental sustainability of transportation systems, and on the protection of transportation facilities from the adverse effects of climate change. From 2001 to 2011, he served as the Editor- in-Chief of the ASCE Journal of Infrastructure Systems. He is Associate Editor of the European Journal of Transportation and Logistics.

  Madanat has served as member or chair of external review committees for the Department of Civil and Environmental Engineering MIT, the ENAC School of EPFL in Switzerland, the Department of Civil Engineering at National University of Singapore, and the School of Transportation Engineering at Tongji University in China. He has served on advisory boards for the National Academy of Engineering, the World Bank, the American University of Beirut, and as proposal review panels for NSF, USDOT, the Swiss NSF, and other international foundations. He has delivered several named lectureships including at MIT, Cornell University, the University of Texas at Austin, and the University of Minnesota. He is the recipient of the Science and Technology Award of the University of California, a prize given annually to one professor in the UC System.
  
  Abstract

  Transportation infrastructure resilience is an important component of a region’s ability to recover itself from natural disasters. While sea level rise (SLR) is becoming inevitable with climate change, we do not yet understand how relevant protection strategies will affect transportation systems, as policy decisions will have significant implications on multiple infrastructure systems. In the present study, we develop a framework where a range of coastal protection strategies are undertaken in the case of one meter of SLR (expected by the year 2100). The methodology incorporates high-resolution hydrodynamic simulations using the Coastal Storm Modeling System (CoSMoS) and traffic simulations using the Multi-Agent Transport Simulation (MATSim) to quantify the potential impact of SLR and protection strategies on the transportation system in the San Francisco Bay Area.

  The results of the traffic simulations are analyzed at the regional and Transportation Analysis Zone (TAZ) levels. Modeling results show that coastal protection of one area will affect the transportation system (sometimes negatively) in areas beyond its vicinity. The improved spatial resolution reveals transportation phenomena that were not identified in previous studies. By quantifying the impacts on commuters’ mobility in different TAZs, the methodology can be used to propose effective and inclusive strategies against SLR for the whole region.