Generative adversarial networks for spatio-temporal data: A survey
Generative Adversarial Networks (GANs) have shown remarkable success in producing
realistic-looking images in the computer vision area. Recently, GAN-based techniques are …
realistic-looking images in the computer vision area. Recently, GAN-based techniques are …
Generative adversarial networks: a survey on applications and challenges
MR Pavan Kumar, P Jayagopal - International Journal of Multimedia …, 2021 - Springer
Deep neural networks have attained great success in handling high dimensional data,
especially images. However, generating naturalistic images containing ginormous subjects …
especially images. However, generating naturalistic images containing ginormous subjects …
Understanding private car aggregation effect via spatio-temporal analysis of trajectory data
Understanding the private car aggregation effect is conducive to a broad range of
applications, from intelligent transportation management to urban planning. However, this …
applications, from intelligent transportation management to urban planning. However, this …
Spatiotemporal data mining: a survey on challenges and open problems
Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay
between space and time. Several available surveys capture STDM advances and report a …
between space and time. Several available surveys capture STDM advances and report a …
AdaBoost-Bagging deep inverse reinforcement learning for autonomous taxi cruising route and speed planning
Taxi cruising route planning has attracted considerable attention, and relevant studies can
be broadly categorized into three main streams: recommending one or multiple areas …
be broadly categorized into three main streams: recommending one or multiple areas …
Understand the impact of traffic states on crash risk in the vicinities of Type A weaving segments: A deep learning approach
The primary objective of this study was to evaluate the impacts of traffic states on crash risk
in the vicinities of Type A weaving segments. A deep convolutional embedded clustering …
in the vicinities of Type A weaving segments. A deep convolutional embedded clustering …
Prediction of Forest fire spread rate using UAV images and an LSTM model considering the interaction between fire and wind
X Li, H Gao, M Zhang, S Zhang, Z Gao, J Liu, S Sun… - Remote Sensing, 2021 - mdpi.com
Modeling forest fire spread is a very complex problem, and the existing models usually need
some input parameters which are hard to get. How to predict the time series of forest fire …
some input parameters which are hard to get. How to predict the time series of forest fire …
Inferring intersection traffic patterns with sparse video surveillance information: An st-gan method
Traffic patterns of urban road intersections are important in traffic monitoring and accident
prediction, thus play crucial roles in urban traffic management. Although real-time traffic …
prediction, thus play crucial roles in urban traffic management. Although real-time traffic …
Exploiting spatiotemporal correlations of arrive-stay-leave behaviors for private car flow prediction
Accurate prediction of private car flows in urban regions is strategically vital for constructing
smart cities. Private car flows are essentially reflected in the arrive-stay-leave (ASL) …
smart cities. Private car flows are essentially reflected in the arrive-stay-leave (ASL) …
A New Multitask Joint Learning Framework for Expensive Multi-Objective Optimization Problems
In this paper, we propose a multi-objective optimization algorithm based on multitask
conditional neural processes (MTCNPs) to deal with expensive multi-objective optimization …
conditional neural processes (MTCNPs) to deal with expensive multi-objective optimization …