DeepThink IoT: the strength of deep learning in internet of things
Abstract The integration of Deep Learning (DL) and the Internet of Things (IoT) has
revolutionized technology in the twenty-first century, enabling humans and machines to …
revolutionized technology in the twenty-first century, enabling humans and machines to …
Spatial–temporal multi-feature fusion network for long short-term traffic prediction
Y Wang, Q Ren, J Li - Expert Systems with Applications, 2023 - Elsevier
Exploiting deep spatial–temporal features for traffic prediction has become growing
widespread. Accurate traffic prediction is still challenging due to the complex spatial …
widespread. Accurate traffic prediction is still challenging due to the complex spatial …
A graph convolutional incorporating GRU network for landslide displacement forecasting based on spatiotemporal analysis of GNSS observations
Landslide displacement prediction is crucial for the early warning of slope failure but
remains a challenging task due to its spatiotemporal complexity. Although temporal …
remains a challenging task due to its spatiotemporal complexity. Although temporal …
IG-Net: An interaction graph network model for metro passenger flow forecasting
The urban metro system accommodates significant travel demand and alleviates traffic
congestion. Improving metro operational efficiency can increase the metro operator revenue …
congestion. Improving metro operational efficiency can increase the metro operator revenue …
Spatio-temporal dynamic graph relation learning for urban metro flow prediction
Urban metro flow prediction is of great value for metro operation scheduling, passenger flow
management and personal travel planning. However, the problem is challenging. First …
management and personal travel planning. However, the problem is challenging. First …
Passenger flow anomaly detection in urban rail transit networks with graph convolution network–informer and Gaussian Bayes models
Passenger flow anomaly detection in urban rail transit networks (URTNs) is critical in
managing surging demand and informing effective operations planning and controls in the …
managing surging demand and informing effective operations planning and controls in the …
Adaboosting graph attention recurrent network: A deep learning framework for traffic speed forecasting in dynamic transportation networks with spatial-temporal …
Y Zhang, X Wang, J Yu, T Zeng, J Wang - Engineering Applications of …, 2024 - Elsevier
In construction engineering, transportation is a key factor affecting the construction schedule,
and Transportation Speed Prediction (TSP) provides essential information for the precise …
and Transportation Speed Prediction (TSP) provides essential information for the precise …
A generative adversarial network-based framework for network-wide travel time reliability prediction
F Shao, H Shao, D Wang, WHK Lam, ML Tam - Knowledge-Based Systems, 2024 - Elsevier
This paper introduces a generative model named the travel time reliability-generative
adversarial network (TTR-GAN) model for predicting network-wide TTR using automatic …
adversarial network (TTR-GAN) model for predicting network-wide TTR using automatic …
Learning spatial-temporal dynamics and interactivity for short-term passenger flow prediction in urban rail transit
J Wu, X Li, D He, Q Li, W Xiang - Applied Intelligence, 2023 - Springer
Accurate short-term passenger flow prediction in urban rail transit is critical in ensuring the
stable operation of urban rail systems. However, accurate passenger flow prediction still …
stable operation of urban rail systems. However, accurate passenger flow prediction still …
Learning spatial–temporal pairwise and high-order relationships for short-term passenger flow prediction in urban rail transit
Short-term passenger flow prediction (STPFP) helps ease traffic congestion and optimize
urban rail transit (URT) system resource allocation. Although graph-based models have …
urban rail transit (URT) system resource allocation. Although graph-based models have …