Deep learning for spatio-temporal data mining: A survey
With the fast development of various positioning techniques such as Global Position System
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …
Applications of deep learning in intelligent transportation systems
AK Haghighat, V Ravichandra-Mouli… - Journal of Big Data …, 2020 - Springer
Abstract In recent years, Intelligent Transportation Systems (ITS) have seen efficient and
faster development by implementing deep learning techniques in problem domains which …
faster development by implementing deep learning techniques in problem domains which …
A survey on long short-term memory networks for time series prediction
Recurrent neural networks and exceedingly Long short-term memory (LSTM) have been
investigated intensively in recent years due to their ability to model and predict nonlinear …
investigated intensively in recent years due to their ability to model and predict nonlinear …
Real-time crash risk prediction on arterials based on LSTM-CNN
Real-time crash risk prediction is expected to play a crucial role in preventing traffic
accidents. However, most existing studies only focus on freeways rather than urban arterials …
accidents. However, most existing studies only focus on freeways rather than urban arterials …
Urban flow prediction from spatiotemporal data using machine learning: A survey
Urban spatiotemporal flow prediction is of great importance to traffic management, land use,
public safety. This prediction task is affected by several complex and dynamic factors, such …
public safety. This prediction task is affected by several complex and dynamic factors, such …
Deep learning-based forecasting approach in smart grids with microclustering and bidirectional LSTM network
Uncertainty modeling of renewable energy sources, load demand, electricity price, etc.
create a high volume of data in smart grids. Accordingly, in this article, a precise forecasting …
create a high volume of data in smart grids. Accordingly, in this article, a precise forecasting …
Fine-grained vessel traffic flow prediction with a spatio-temporal multigraph convolutional network
The accurate and robust prediction of vessel traffic flow is gaining importance in maritime
intelligent transportation system (ITS), such as vessel traffic services, maritime spatial …
intelligent transportation system (ITS), such as vessel traffic services, maritime spatial …
Graph neural networks for intelligent transportation systems: A survey
Graph neural networks (GNNs) have been extensively used in a wide variety of domains in
recent years. Owing to their power in analyzing graph-structured data, they have become …
recent years. Owing to their power in analyzing graph-structured data, they have become …
Anomaly detection for in-vehicle network using CNN-LSTM with attention mechanism
With the increasing connectivity between the Electronic Control Units (ECUs) and the
outside world, safety and security have become stringent problems. The Controller Area …
outside world, safety and security have become stringent problems. The Controller Area …
Sequence to sequence learning with attention mechanism for short-term passenger flow prediction in large-scale metro system
The accurate short-term passenger flow prediction is of great significance for real-time public
transit management, timely emergency response as well as systematical medium and long …
transit management, timely emergency response as well as systematical medium and long …