News & Events

We leverage unique features from the two latest releases of the National Household Travel Survey (NHTS) data and a machine learning approach to predict household-level online shopping purchases in the US. The modeling and investigation are performed at the national level and for three of the largest cities (New York,… read more 

This presentation will provide a perspective, building on a recent review, of the Smart City considerations from the broader urban climate, urban air quality including heat and health perspective. Specific cases/projects- both locally in Austin as well as internationally -will be highlighted. Speaker: Dev Niyogi Dev Niyogi is a professor… read more 

Over 50% of the world’s population currently lives in cities, and this number is expected to rise to 70% by 2050. The urban built environment is expected to triple in the next three decades. This built environment has significant impacts on sustainability and human well-being. In the United States, the… read more 

Given the on-going digital revolution and our present-day sustainability challenges, we have to reinvent the way cities and societies are operated. I propose that the requirement of organizing societies in a more resilient way implies the need for distributed solutions, based on digitally assisted self-organization, and that this concept is… read more 

The real-time city is real! As layers of networks and digital information blanket urban space, new approaches to studying the built environment are emerging. The way we describe and understand cities is being radically transformed — as are the tools we use to design them. The Senseable City Laboratory’s mission—a… read more 

This project develops a generalized model to operate an integrated public transit, transportation network company (TNC), and FMLM service considering uncertain rider demand and network dynamics. It optimally matches riders to shuttle vehicles, and route vehicles in real time. In addition, it is a general platform to evaluate user costs… read more 

Pecan Street works with advanced energy systems including smart inverters, energy storage, controlled electric vehicle charging, HVAC demand response and V2G system testing, as well as residential electrical system issues that are not being addressed by these technologies. Speaker: Scott Hinson Scott Hinson is a Chief Technology Officer at Pecan… read more 

Reinforcement learning (RL) has gained popularity in the research community as a model-free and adaptive control paradigm for the built environment, especially for building energy control. RL has the potential to enable inexpensive plug-and-play building controllers that can be implemented without necessitating potentially expensive control models (unlike model predictive control),… read more 

We use natural language processing technique to construct sentiment index from social media; we link the sentiment index with extreme weather conditions and COVID-19 shocks to quantify their negative impacts on people’s expressed sentiment; we explore the heterogeneity across countries and different population groups, and the underlying mechanisms. Speaker: Siqi… read more