  A `trajectory' refers to a trace generated by a moving object in geographical
spaces, usually represented by of a series of chronologically ordered points,
where each point consists of a geo-spatial coordinate set and a timestamp.
Rapid advancements in location sensing and wireless communication technology
enabled us to collect and store a massive amount of trajectory data. As a
result, many researchers use trajectory data to analyze mobility of various
moving objects. In this dissertation, we focus on the `urban vehicle
trajectory,' which refers to trajectories of vehicles in urban traffic
networks, and we focus on `urban vehicle trajectory analytics.' The urban
vehicle trajectory analytics offers unprecedented opportunities to understand
vehicle movement patterns in urban traffic networks including both user-centric
travel experiences and system-wide spatiotemporal patterns. The spatiotemporal
features of urban vehicle trajectory data are structurally correlated with each
other, and consequently, many previous researchers used various methods to
understand this structure. Especially, deep-learning models are getting
attentions of many researchers due to its powerful function approximation and
feature representation abilities. As a result, the objective of this
dissertation is to develop deep-learning based models for urban vehicle
trajectory analytics to better understand the mobility patterns of urban
traffic networks. Particularly, this dissertation focuses on two research
topics, which has high necessity, importance and applicability: Next Location
Prediction, and Synthetic Trajectory Generation. In this study, we propose
various novel models for urban vehicle trajectory analytics using deep
learning.
