Dartmouth Events

Enabling Urban Intelligence by Harnessing Human-Generated Spatial-Temporal Data

Yanhua will first introduce the spatial-temporal imitation learning framework that inversely learns and “imitates” the decision-making strategies of human agents...

Wednesday, April 21, 2021
11:45am – 12:45pm
Zoom - Contact Susan P Cable for link
Intended Audience(s): Public
Categories: Lectures & Seminars

Abstract: The rapid development of mobile sensing and information technology has led to an explosive growth in both the amount and the scale of human-generated spatial-temporal data (HSTD). Examples of HSTD include taxi GPS trajectories, passenger trip data from automated fare collection (AFC) devices on buses and trains, and working traces from the emerging gig-economy services, such as food delivery (DoorDash, Postmates), and everyday tasks (TaskRabbit). Such HSTD capture unique decision-making strategies of the human agents (e.g., the passenger-seeking strategies of taxi drivers and transit mode choice strategies of travelers). Harnessing HSTD to characterize the unique decision-making strategies of human agents has transformative potential in many applications, including promoting individual well-being of gig-workers and improving the service quality and revenue of transportation service providers. In this talk, I will first introduce our spatial-temporal imitation learning framework that inversely learns and “imitates” the decision-making strategies of human agents from their HSTD. Moreover, I will present how to use the learned human decision strategies to enable human-centric urban intelligence, that enhances the well-being and fairness for urban dwellers and society in terms of income level, travel and living convenience.

Bio: Yanhua Li is an Assistant Professor in the Computer Science Department and Data Science Program at Worcester Polytechnic Institute (WPI). His research interests focus on artificial intelligence (AI) and data science, with applications in smart cities in many contexts, including spatial-temporal data analytics, urban planning and optimization. Recently, his research has an emphasis on advancing imitation learning and meta learning in AI for learning and influencing the decision-making strategies of urban human agents, such as passenger-seeking strategies of taxi drivers and transit mode/route choices of urban travelers. Dr. Li received two Ph.D. degrees in computer science from University of Minnesota at Twin Cities in 2013, and in electrical engineering from Beijing University of Posts and Telecommunications, Beijing in China in 2009, respectively. His work has been honored with the Best Applied Data Science Paper Award at SDM 2019. His research has been funded by NSF CAREER and CRII Awards, and two projects with NSF Smart and Connected Communities (S&CC) Program. Please find more details of his work at http://www.wpi.edu/~yli15/.

For more information, contact:
Susan Cable

Events are free and open to the public unless otherwise noted.