Mining Human Mobility Data

Mining Human Mobility Data

April 13, 2018

Zhenhui (Jessie) Li, of Pennsylvania State University, presented Mining Human Mobility Data on April 13 in 502 Davis at 4 p.m. 


Large-scale human mobility data can be collected from mobile phones, car navigation systems, road surveillance cameras, and location-based applications. Turning such raw data into knowledge can provide insights about our city and empower the city to be more intelligent. In this talk, I would like to share new data mining and machine learning technologies to understand human mobility data. First, I will discuss a new space distance defined by mobility flow. We propose region representation learning via mobility flow and demonstrate the use of such representations in predicting crimes and region properties. Next, I will introduce our novel spatial-temporal deep learning models that demonstrate superior performance in predicting taxi demands. The model is validated using real large-scale Didi Chuxing data. Lastly, I would like to share an opportunity of City Brain project, which I will soon start working on from summer 2018. This project will be in collaboration with traffic police department of Hangzhou, China. We will have access to real-time traffic data from thousands of road surveillance cameras in the city and we will conduct field experiments to actually control the traffic signals in the city. We will be exploring AI-empowered data-driven traffic signal control system. I am looking for potential collaborations on this exciting project.

Friday, April 13, 2018 - 4:00pm
502 Davis Hall


Dr. Zhenhui (Jessie) Li is Associate Professor of Information Sciences and Technology at the Pennsylvania State University. She is Haile family early career endowed professor. Prior to joining Penn State, she received her PhD degree in Computer Science from University of Illinois Urbana-Champaign in 2012, where she was a member of data mining research group. Her research has been focused on mining spatial-temporal data with applications in ecology, environment, social science, urban computing, and transportation. She is a passionate interdisciplinary researcher and has been actively collaborating with cross-domain researchers. She has received NSF CAREER award, junior faculty excellence in research, and George J. McMurtry junior faculty excellence in teaching and learning award. To learn more, please visit her homepage: