Flooding is one of the most dangerous and costly natural hazards, and has a large impact on infrastructure, mobility, public health, and safety. Despite the disruptive impacts of flooding and predictions of increased flooding due to climate change, municipalities have little quantitative data available on the occurrence, frequency, or extent of urban floods. To address this, FloodNet has been designing, building, and deploying low-cost, ultrasonic sensors to systematically collect data on the presence, depth, and duration of street-level floods in New York City (NYC). FloodNet is a partnership between academic researchers and NYC municipal agencies, working in consultation with residents and community organizations. FloodNet sensors are designed to be compact, rugged, low-cost, and deployed in a manner that is independent of existing urban power and network infrastructure. These requirements were implemented to allow deployment of a hyperlocal, city-wide sensor network, given that urban floods often occur in a distributed manner due to local variations in land development, population density, sewer design, and topology. Thus far, 70 FloodNet sensors have been installed across the five boroughs of NYC. These sensors have recorded flood events caused by high tides, stormwater runoff, storm surge, and extreme precipitation events, illustrating the feasibility of collecting data that can be used by multiple stakeholders for flood resiliency planning and emergency response.

Charlie Mydlarz is a Research Associate Professor at NYU CUSP and the Music and Audio Research Laboratory. He is an acoustician/engineer who designs, develops, and deploys IoT devices to tackle different challenges, including: urban noise sensing, acoustic condition monitoring, urban flood detection, soundscape perception, building/classroom efficiency, and urban mobility. His PhD research at The University of Salford's Acoustic Research Centre enabled public engagement in a large-scale mass participation soundscape study using smart phones for global subjective and objective data collection and analysis.

This talk will present recent advancements in computational methods and large-scale, high-resolution urban data to address issues of social justice, public health, and climate action in cities. I will demonstrate how data-driven approaches can be applied to understand urban dynamics, support evidence-based policy and planning decisions, and empower residents through the democratization of data. Specific attention will be given to algorithmic bias and algorithm-in-the-loop decision-making. 

Constantine E. Kontokosta, PhD, is an Associate Professor of Urban Science and Planning and Director of the Civic Analytics Program at the Marron Institute of Urban Management at New York University (NYU). He also directs the Urban Intelligence Lab and holds faculty appointments at the NYU Center for Urban Science and Progress (CUSP) and the NYU Tandon School of Engineering. He previously served as the founding Deputy Director/Academic Director of CUSP. His work has been published in leading peer-reviewed journals, including PNAS, Nature Communications, and Nature Energy, and he is the recipient of research awards and grants from the National Science Foundation, IBM, Amazon, and the MacArthur Foundation, among others, and best paper awards from the Journal of the American Planning Association, ICLR, and the Bloomberg Data for Good conference. He holds degrees from the University of Pennsylvania, New York University, and Columbia University. 

The traffic forecasting problem is a challenging task that requires spatial-temporal modeling and gathers research interests from various domains. In recent years, spatial-temporal deep learning models have improved the accuracy and scale of traffic forecasting. While hundreds of models have been suggested, they share similar modules, or building blocks, which can be categorized into three temporal feature extraction methods of recurrent neural networks, convolution, and self-attention and two spatial feature extraction methods of convolutional graph neural networks (GNN) and attentional GNN. More importantly, the models have been mostly evaluated for their entire architectures with limited efforts to characterize and understand the performance of each category of building blocks. In this study, we design an extensive, multi-faceted experiment to relate the choice of building blocks on traffic forecasting accuracy considering environmental characteristics and distributions of datasets including outliers. Specifically, we implement six traffic forecasting models using three building blocks for temporal modeling and two for spatial modeling. When we evaluate the models on four datasets with diverse characteristics, the results show each building block demonstrates distinguishable characteristics depending on study sites, prediction horizons, and traffic categories. The results of this study can enhance the utility of existing models and suggest guidelines for researchers building traffic forecasting model architectures and for practitioners implementing these state-of-the-art techniques in real-world applications.

Yuyol Shin is currently a postdoctoral researcher in the Department of Civil and Environmental Engineering at Korea Advanced Institute of Science and Technology (KAIST). He received B.S. (2016) and Ph.D. (2022) in Civil and Environmental Engineering at KAIST, and worked as a visiting scholar in Department of Civil and Environmental Engineering at University of California, Berkeley from October 2022 to June 2023. His research interests are spatial-temporal data mining, graph neural networks, artificial intelligence in the field of transportation engineering, and transportation network analysis. His recent works have focuses on core technologies of intelligent transportation systems such as traffic forecasting in urban areas using Graph Neural Networks and time-series models such as causal convolution and self-attention. Recently, Yuyol Shin is investigating the field of AI-based maritime transportation including topics such as vessel trajectory prediction, weather routing, and smart port operation.

Mobility service route design requires potential demand information to well accommodate travel demand within the service region. Transit planners and operators can access various data sources including household travel survey data and mobile device location logs. However, when implementing a mobility system with emerging technologies, estimating demand level becomes harder because of more uncertainties with user behaviors. Therefore, this study proposes an artificial intelligence-driven algorithm that combines sequential transit network design with optimal learning. An operator gradually expands its route system to avoid risks from inconsistency between designed routes and actual travel demand. At the same time, observed information is archived to update the knowledge that the operator currently uses. Three learning policies are compared within the algorithm: multi-armed bandit, knowledge gradient, and knowledge gradient with correlated beliefs. For validation, a new route system is designed on an artificial network based on public use microdata areas in New York City. Prior knowledge is reproduced from the regional household travel survey data. The results suggest that exploration considering correlations can achieve better performance compared to greedy choices in general. In future work, the problem may incorporate more complexities such as demand elasticity to travel time, no limitations to the number of transfers, and costs for expansion.

Joseph Chow is an Institute Associate Professor at the NYU Tandon School of Engineering’s Civil and Urban Engineering Department with affiliations at CUSP, Rudin Center for Transportation Policy & Management, and Sustainable Engineering Initiative. Chow is an NSF CAREER award recipient, a former Canada Research Chair, and the co-founding Deputy Director of the C2SMART(ER) University Transportation Center at NYU. He is a co-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 almost 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.

Diversity of physical encounters in urban environments is a key feature of cities that foster economic productivity and social capital. How and where do we have the most and least diversity? How did the diversity of our social encounters change due to the pandemic? I will introduce our work that answers these questions using large-scale, privacy-enhanced mobility dataset of more than one million anonymized mobile phone users in Boston, Dallas, Los Angeles, and Seattle, across three years spanning before and during the pandemic.

Taka is currently a Postdoctoral Associate at the MIT Media Lab, working on the intersection of computational social science and urban science with Alex 'Sandy' Pentland and Esteban Moro. His research develops tools and models for analyzing large-scale human behavior data to better understand collective social dynamics during disruptions, and to improve the resilience of communities and cities to shocks (e.g., disasters, pandemics, and disruptive technology). He will join New York University Center for Urban Science and Progress (CUSP) as an Assistant Professor in January 2024.

North Korea has long been a black box with no official data for outsiders to assess its economic development. The lack of ground truth labels also makes it difficult to apply existing inference models with remote sensing data for the country’s economic measurement. To overcome these constraints, we develop a human-machine collaborative algorithm that leverages satellite imagery and lightweight human annotations in the machine-learning process. When applied to North Korean satellite images for the period from 2016 to 2019 to generate grid-level estimates of the country’s economic development, our human-machine collaborative algorithm outperforms machine-only learning approaches based on nightlight intensity or land cover classification. Using our measure as a proxy of economic development indicates that amid rising pressure from economic sanctions, the centrally planned economy has been directing more resources towards its capital and regions with highly publicized state-led development projects. Our model can be applied to other developing countries with insufficient data and provide reliable and inexpensive indicators on a granular level

Jihee Kim is an associate professor in the School of Business and Technology Management, College of Business at KAIST. She is an economist interested in how economic outcomes are distributed across individuals and regions. Her primary focus is on the study of income distribution, specifically at the top. She has explored how the creative destruction of entrepreneurs, tax policy, and CEO pays have contributed to increases in top income inequality. She also has expanded her research by applying machine learning algorithms to satellite images in collaboration with computer scientists, providing detailed economic insights into regions like North Korea. Jihee holds a Ph.D. in Management Science and Engineering, an M.A. in Economics, both from Stanford University, and a B.S. in Computer Science from KAIST.

Data analytics and data-driven processes have been used to make urban planning decisions and to improve related city service operations. With the proliferation of digital data, new opportunities are being availed to measure, understand and propose changes to the communities in which we live, work, and play. This has led to a host of new terms and disciplines – urban science, big data, smarter cities, urban informatics, civic analytics – that seeks to understand the intersection of digital technologies and the human environment. The most benefit of those disciplines is not only an in-depth understanding of urban phenomena but also predicting and preparing for future scenarios in cities composed of complex systems. There are immense opportunities with big data and analytic capacities to support responsive and effective urban systems, ultimately aiming at sustainable and livable cities through a problem-driven analytic approach. This presentation focuses on the introduction to the next phase of urban planning based on analytics and introduce a research project sample using different scale of urban data.

Boyeong Hong is a Researcher in the Civic Analytics Program at the NYU Marron Institute of Urban Management. Her research interests focus on how to apply urban informatics to real world problems in urban planning and operations. Boyeong’s current work deals with predictive city analytics using Big Data and Machine Learning techniques to deliver better city services allocation. Additionally, she is working on the human mobility project associated with the disaster management and the urban resilience planning. Boyeong is currently an affiliated adjunct faculty at Columbia University, the Graduate School of Architecture, Planning, and Preservation. Boyeong earned a Ph.D. in Civil and Urban Engineering, majoring in Urban Informatics from New York University, and she holds a M.S. in Applied Urban Science and Informatics from NYU Center for Urban Science and Progress (CUSP). She holds a B.Arch from Yonsei University and a Master of City Planning degree from Seoul National University.

Carefully designed urban and architectural spaces can reduce stress, promote physical activities, reduce crime rates, prevent infection, and even save lives. I will introduce studies that highlight how the design of cities and buildings could improve the health and well-being of individuals. More specifically, our studies regarding the relationships between the design of cities and the health of older adults will be shared. Using GPS data of older adults in South Korea, we will illustrate the walking behaviors of older adults in relation to the design of cities and streets.

Lisa Lim, Ph.D. is a researcher, designer, and educator with her primary focus on improving the health and wellness of users through design. She has an academic and practical background in architectural design and majored in Evidence-based design for her PhD. She joined KAIST in 2021 and prior to joining KAIST, she was an assistant professor at Texas Tech University. She is interested in how spatial layouts can support individual experience and organizational outcomes and how designers can provide such environments to users.

In this talk, we investigate two fundamental questions related to safety and insurance in the age of automation. First, we touch upon the question of safety and liability under infrastructure-assisted automated driving. In such an environment, automakers provide vehicle automation technology while infrastructure service providers (ISSPs) provide smart infrastructure services. Additionally, customers can receive coverage for accidents from either of these actors but also from legacy auto insurers. We investigate the effect of market structure on safety and accident coverage and show that an integrated monopoly provides full coverage and fully accounts for accident costs when choosing safety levels. However, in the Nash setting, even though full coverage obtains, lack of coordination leads to partial internalization of accident costs by the automaker. Moreover, multiple equilibria might exist, some of them undesirable. We show that, both in the presence and absence of legacy insurance, an appropriate liability rule can induce optimal safety levels under the Nash setting. Our second question concerns itself with the role of legacy auto insurance in the age of infrastructure-assisted automated driving. Our analysis shows that the industry is not necessary for optimal coverage when the cost of accidents is known in advance and all possible accident scenarios are contractible. In fact, their presence can even harm safety, even though it ensures full coverage for accidents. However, when only insurance contracts with capped liability for automakers and ISSPs are available, a window of opportunity opens up for legacy insurers to enter the market while improving coverage.

Daniel Vignon’s research seeks to inform the design, regulation and operation of emerging mobility services and of smart infrastructure systems. Drawing from his background in both engineering and economics, he models and analyzes the interactions of these systems with different markets, studies their performance and their impact on social welfare, and designs policies to optimally and parsimoniously regulate them. He holds a BSc in Mechanical Engineering from MIT, as well as an MA in Economics and a PhD in Civil Engineering from the University of Michigan.

Traditional methods to understand diverse changes of urban state such as traffic estimation, population change prediction, etc are usually done by analyzing spatial temporal changes of their corresponding physical attribute. However, they lack in terms of causal effect analysis and prediction accuracy. We propose a new noble approach where we analyze how urban dwellers exploit a target urban space - human flow analysis. This allows us to understand various spatial temporal changes in urban space in more accurate and explanable manner.

Dongman Lee is a provost and executive vice president at Korea Advanced Institute of Science and Technology, Daejeon, South Korea, and also with school of computing at KAIST. His research interests include smart space middleware, edge IoT virtualization, social media analysis, and trust management. He is a member of KISS and IEEE, and a Senior Member of ACM.