Trajectory data mining: an overview
Y Zheng - ACM Transactions on Intelligent Systems and …, 2015 - dl.acm.org
The advances in location-acquisition and mobile computing techniques have generated
massive spatial trajectory data, which represent the mobility of a diversity of moving objects …
massive spatial trajectory data, which represent the mobility of a diversity of moving objects …
A survey of traffic prediction: from spatio-temporal data to intelligent transportation
H Yuan, G Li - Data Science and Engineering, 2021 - Springer
Intelligent transportation (eg, intelligent traffic light) makes our travel more convenient and
efficient. With the development of mobile Internet and position technologies, it is reasonable …
efficient. With the development of mobile Internet and position technologies, it is reasonable …
Deep representation learning for trajectory similarity computation
Trajectory similarity computation is fundamental functionality with many applications such as
animal migration pattern studies and vehicle trajectory mining to identify popular routes and …
animal migration pattern studies and vehicle trajectory mining to identify popular routes and …
A survey on trajectory data mining: Techniques and applications
Rapid advance of location acquisition technologies boosts the generation of trajectory data,
which track the traces of moving objects. A trajectory is typically represented by a sequence …
which track the traces of moving objects. A trajectory is typically represented by a sequence …
Constructing popular routes from uncertain trajectories
The advances in location-acquisition technologies have led to a myriad of spatial
trajectories. These trajectories are usually generated at a low or an irregular frequency due …
trajectories. These trajectories are usually generated at a low or an irregular frequency due …
A graph-based approach for trajectory similarity computation in spatial networks
Trajectory similarity computation is an essential operation in many applications of spatial
data analysis. In this paper, we study the problem of trajectory similarity computation over …
data analysis. In this paper, we study the problem of trajectory similarity computation over …
Learning effective road network representation with hierarchical graph neural networks
Road network is the core component of urban transportation, and it is widely useful in
various traffic-related systems and applications. Due to its important role, it is essential to …
various traffic-related systems and applications. Due to its important role, it is essential to …
Jointly contrastive representation learning on road network and trajectory
Road network and trajectory representation learning are essential for traffic systems since
the learned representation can be directly used in various downstream tasks (eg, traffic …
the learned representation can be directly used in various downstream tasks (eg, traffic …
A survey of localization methods for autonomous vehicles in highway scenarios
In the context of autonomous vehicles on highways, one of the first and most important tasks
is to localize the vehicle on the road. For this purpose, the vehicle needs to be able to take …
is to localize the vehicle on the road. For this purpose, the vehicle needs to be able to take …
Estimating human trajectories and hotspots through mobile phone data
Nowadays, the huge worldwide mobile-phone penetration is increasingly turning the mobile
network into a gigantic ubiquitous sensing platform, enabling large-scale analysis and …
network into a gigantic ubiquitous sensing platform, enabling large-scale analysis and …