Predictive Routing Query Processing and Movement Analysis in Road Networks

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


PhD Thesis Defence


Title: "Predictive Routing Query Processing and Movement Analysis in Road 
Networks"

By

Miss Jing ZHAO


Abstract:

Routing query processing in road networks is fundamental for modern 
transportation applications. It aims to find optimal paths between origins 
and destinations by considering traffic conditions, network topology, and 
user preferences. To achieve good query results, we need 1) accurate and 
efficient traffic condition prediction, 2) corresponding route planning and 
navigation, and 3) targeted user movement analysis. While recent years have 
seen advances in route planning, traffic prediction, and trajectory 
prediction, these components are typically treated separately and face 
individual challenges. Current route planning methods either assume static 
costs (based on road segment length or historical travel times) or static 
cost functions, failing to adapt to real-time traffic evolution and 
resulting in suboptimal navigation. Existing traffic prediction approaches 
inefficiently process entire road networks for fixed periods of time, 
leading to high prediction workload, data throughput, and excessive GPU 
utilization. Moreover, existing trajectory prediction methods focus on 
general travel preferences without addressing cold-start users or 
trajectories. Therefore, we propose an integrated Predictive Routing Query 
Processing and Movement Analysis Framework to address the following critical 
challenges: 1) the efficiency and scalability of traffic prediction, 2) the 
maintenance of route optimality under dynamic traffic evolution, and 3) the 
accuracy of personalized trajectory prediction. Specifically, we propose a 
just-in-time continuous routing method adaptive to real-time traffic 
changes, a routing-oriented traffic prediction approach that maintains 
up-to-date traffic data for running queries and reduces computational 
overhead both spatially and temporally, and a discriminative learning method 
for personalized trajectory prediction. Extensive experiments on real- world 
road networks demonstrate that our methods achieve enhanced route 
optimization, improved traffic prediction efficiency, and more accurate 
personalized travel preference learning compared to existing 
state-of-the-art methods.


Date:                   Wednesday, 4 June 2025

Time:                   2:00pm - 4:00pm

Venue:                  Room 3494
                        Lifts 25/26

Chairman:               Prof. Xueqing ZHANG (CIVL)

Committee Members:      Prof. Xiaofang ZHOU (Supervisor)
                        Prof. Qiong LUO
                        Prof. Ke YI
                        Prof. Hai YANG (CIVL)
                        Prof. Haibo HU (PolyU)