Effective Learning from Mobile Data for Human Behavior and Urban Dynamics Sensing and Prediction

PhD Thesis Proposal Defence


Title: "Effective Learning from Mobile Data for Human Behavior and Urban 
Dynamics Sensing and Prediction"

by

Mr. Jiangchuan ZHENG


Abstract:

The phenomenal growth of sensor-equipped smartphones and GPS-equipped vehicles 
have produced an unprecedented wealth of digital information, such as humans' 
phone call records, mobility traces, Bluetooth proximity readings, as well as 
city taxi trajectories. Learning the hidden patterns from such mobile data can 
help understand the contexts and facts about individual, social group, 
community, and urban environment, and hence is an important task in both 
artificial intelligence and mobile computing. In this proposal, we study how to 
build effective analytical and predictive models of human behavior contexts and 
urban dynamics, which is crucial in building context-aware ubiquitous systems 
and enabling smart-city applications.

In the first part, we propose effective learning methods to capture useful 
behavior contexts from human mobile data, including personal mobility behaviors 
and social interaction behaviors, to enable exploratory, inference, and 
predictive purposes. The methods are designed to address several typical 
challenges in mobile data, namely the presence of noise and sparsity, and the 
absence of semantic labels. For personal mobility behaviors, we first propose a 
Bayesian network to discover the routine behavior pattern from a single user's 
time-stamped mobility traces. Based on such, we then propose non-trivial 
extensions to address the difficult problem of representing mobility habits of 
multiple individuals in a unified way. To address the challenge that multiple 
individuals rarely have spatial overlap or social connections in their 
mobility, we leverage the observation that temporal structures in habits can be 
highly shared across individuals. We design two methods based on novel 
extensions of matrix factorization and hierarchical Dirichlet processes to 
realize such population models, and demonstrate how they help solve several 
challenging pervasive tasks, including routine behavior pattern discovery from 
very sparse mobility data, individual mobility prediction under cold-start 
condition, and organizational rhythm discovery. To show the generality of the 
method, we also apply it to Bluetooth data to discover social circles semantics 
for human social behavior characterization and social event prediction.

In the second part, we propose effective methods to learn road latent cost from 
historical taxi trajectory data for urban sensing. Road latent cost quantifies 
how desirable each road is for traveling, and is a good representation of 
driving experience and urban dynamics. We first study how to robustly estimate 
the temporal dynamics of road travel time under temporal sparsity by exploiting 
temporal smoothness in a multi-task regression framework. In addition to travel 
time, a plenty of other hidden factors may influence the desirability of a 
road, but are impossible to obtain in practice. To address this, we propose to 
learn road latent cost from entire trajectories by modeling drivers' routing 
decisions based on inverse reinforcement learning, while at the same time 
properly considering the heterogeneity of destinations so that trajectories 
with different goals can be learned jointly. In addition, observing that real 
trajectory data often contains a few anomalous trajectories, we design a sparse 
noise-oriented robust inverse reinforcement learning framework which can 
automatically identify and remove anomalies in cost learning. Real data 
experiments show that compared with past edge-centric approaches, the road 
latent costs learned in our way are more useful and robust in facilitating 
typical smart-city applications, and require less data for learning.


Date:			Friday, 28 November 2014

Time:                   9:00am - 11:00am

Venue:                  Room 3501
                         lifts 25/26

Committee Members:	Prof. Lionel Ni (Supervisor)
 			Dr. Huamin Qu (Chairperson)
 			Dr. Qiong Luo
 			Dr. Raymond Wong


**** ALL are Welcome ****