An integrated framework for modeling and predicting spatiotemporal phenomena in urban environments

• Main Author: LE, Tuc Viet
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2017
• Subjects: framework; machine learning; spatiotemporal; geospatial; reinforcement learning; craussian process; Artificial Intelligence and Robotics; Databases and Information Systems; Urban Studies and Planning
• Online Access: https://ink.library.smu.edu.sg/etd_coll/141; https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1140&context=etd_coll
• Institution: Singapore Management University

This thesis proposes a general solution framework that integrates methods in machine learning in creative ways to solve a diverse set of problems arising in urban environments. It particularly focuses on modeling spatiotemporal data for the purpose of predicting urban phenomena. Concretely, the fram...