SPATIAL-TEMPORAL DATA MINING WITH SMART CITY APPLICATIONS

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "SPATIAL-TEMPORAL DATA MINING WITH SMART CITY APPLICATIONS"

By

Mr. Guanyao LI


Abstract

Spatial-temporal data are data with both location and time dimensions, 
such as user trajectory, vehicular traffic, bike rental, etc. They not 
only reveal user mobility, preference and social activity, but also shed 
insights on the exchange of commodities or assets between regions. 
Spatial-temporal data mining hence plays a pivotal role in enabling many 
smart city applications, such as urban planning, traffic management, 
pandemic prevention and control, etc.

In this thesis, we first focus on the topic of co-location detection, 
which is to measure the spatial-temporal overlap between objects from 
their trajectory data. It has many important smart city applications, such 
as contact tracing, companion detection, personalized marketing, etc. 
Given trajectories with explicit locations (e.g., geo-location with 
latitude and longitude), we propose a novel and effective measure termed 
STS which considers location noise and heterogeneous and diverse data 
sampling rates in the data. Furthermore, for trajectories with strong 
location-privacy protection using WiFi Received Signal Strength Indicator 
(RSSI), we propose vContact, a novel, private, and effective IoT contact 
tracing solution given virus lifespan.

Then, we study the problem of traffic forecasting using transition data 
between regions and aggregated flow data of any region, respectively. 
Traffic forecasting is to predict the inflow (i.e., the number of arriving 
objects per unit time) and outflow (i.e., the number of departing objects 
per unit time) of any region in a city at the next time slot. It is 
crucial for many smart city applications, such as route planning, 
logistics and supply-chain management, public safety, etc. Leveraging 
transition data between regions, we propose a spatial-temporal graph 
neural network for docked bike prediction. Moreover, with only aggregated 
flow data of any region, we propose a spatial-temporal Transformer for 
traffic forecasting.


Date:			Thursday, 12 May 2022

Time:			2:00pm - 4:00pm

Zoom Meeting: 
https://hkust.zoom.us/j/95081761872?pwd=SnpKRVYweG5kY1JLV0gzK0c2TzgyZz09

Chairperson:		Prof. Yong HUANG (CHEM)

Committee Members:	Prof. Gary CHAN (Supervisor)
 			Prof. Qiong LUO
 			Prof. Xiaofang ZHOU
 			Prof. Mengqian LU (CIVL)
 			Prof. Ling CHEN (Zhejiang University)


**** ALL are Welcome ****