GeoAI and Deep Learning Symposium: GeoAI and Urban Mobility Analytics
Date: 3/28/2025
Time: 4:10 PM - 5:30 PM
Room: 251A, Level 2, Huntington Place
Type: Paper
Recorded:
Theme:
Curated Track:
Sponsor Group(s):
No Sponsor Group Associated with this Session
Organizer(s):
Dan Qiang McGill University
Grant McKenzie McGill University
Xiao Huang Emory University
Yihong Yuan
Chair(s):
Dan Qiang, McGill University
Grant McKenzie, McGill University
Description:
The integration of Geospatial Artificial Intelligence (GeoAI) with urban mobility analytics is paving the way for groundbreaking insights into the complex dynamics of cities and their transportation networks. This rapidly developing field harnesses the power of AI, including machine learning, deep learning, and big data mining, combined with geospatial science to address urban mobility challenges and optimize transportation systems for more efficient and sustainable urban living.
Urban mobility, a cornerstone of modern urbanization involves how people, goods, and services move around a city. Leveraging GeoAI allows researchers and practitioners to analyze vast amounts of spatiotemporal data, uncover hidden patterns, forecast demand, and propose data-driven strategies to understand daily mobility patterns, improve traffic flow, enhance public transit systems, and promote active transportation modes. Deep learning, with its ability to model non-linear, intricate relationships, is instrumental in understanding the interactions between urban infrastructure, human behavior, and external factors such as socio-economic conditions and environmental changes.
Presentations (if applicable) and Session Agenda:
Yubin Lee |
Exploring the Spatiotemporal Patterns of Shared Bicycle Usage: A Case Study of MetroBike in Austin, Texas |
Zhiyuan Yao |
Riding Through Change: Gender-Specific Trends in Bike Share Pre- and Post-COVID-19 |
Jiaomin Wei, Chinese University of Hong Kong |
Uncovering travel communities among older and younger adults using smart card data |
Tiansheng Tan, University of Minnesota - Twin Cities |
A data-driven spatial-temporal approach to measure the influence of Mobility of care in daily travel behaviors |
Pingping Wang, Texas State University, San Marcos |
Improve GWR with Explainable Machine Learning: A Case Study on Factors Influencing Human Mobility |
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GeoAI and Deep Learning Symposium: GeoAI and Urban Mobility Analytics
Description
Type: Paper
Date: 3/28/2025
Time: 4:10 PM - 5:30 PM
Room: 251A, Level 2, Huntington Place
Contact the Primary Organizer
Dan Qiang McGill University
dan.qiang@mail.mcgill.ca
Session sponsored by: