Short-term traffic prediction using deep learning long short-term memory: Taxonomy, applications, challenges, and future trends
This paper surveys the short-term road traffic forecast algorithms based on the long-short
term memory (LSTM) model of deep learning. The algorithms developed in the last three …
term memory (LSTM) model of deep learning. The algorithms developed in the last three …
Floating Car Data–Based Short-Term Travel Time Forecasting with Deep Recurrent Neural Networks Incorporating Weather Data
M Walch, M Neubauer, W Schildorfer - Journal of Transportation …, 2023 - ascelibrary.org
The prediction of future traffic conditions represents a main building block for traffic
management. With the advent of multiple traffic and environmental sensors, diverse data for …
management. With the advent of multiple traffic and environmental sensors, diverse data for …
A Multi-Scale Residual Graph Convolution Network with hierarchical attention for predicting traffic flow in urban mobility
J Ling, Y Lan, X Huang, X Yang - Complex & Intelligent Systems, 2024 - Springer
Accurate prediction of traffic flow is essential for optimizing transportation resource allocation
and enhancing urban mobility efficiency. However, traffic data generated daily are vast and …
and enhancing urban mobility efficiency. However, traffic data generated daily are vast and …
Meteorological Data-Driven Traffic Flow Forecasting Using Intelligent Algorithms
TM Newbolt, P Mandal - 2023 IEEE PES Innovative Smart Grid …, 2023 - ieeexplore.ieee.org
This paper proposes the incorporation of meteorological and typical seasonal Historical
Traffic Flow (HTF) data into Deep Learning (DL) algorithms using Long Short-Term Memory …
Traffic Flow (HTF) data into Deep Learning (DL) algorithms using Long Short-Term Memory …
Forecasting short-term indoor radon: a machine learning approach using LSTM networks
V Mpinga, AMR Da Cruz… - 2023 18th Iberian …, 2023 - ieeexplore.ieee.org
Indoor radon is a radioactive gas that can accumulate in homes and pose a health risk for
humans. Forecasting indoor radon levels may be used as a tool for mitigating human …
humans. Forecasting indoor radon levels may be used as a tool for mitigating human …
DCGCN: Dynamic community graph convolutional network for traffic forecasting
Traffic forecasting is one of the core issues in transportation systems. Graph convolution
based spatiotemporal model can process the complex and highly nonlinear traffic data, but …
based spatiotemporal model can process the complex and highly nonlinear traffic data, but …
Implementing Long Short-Term Memory Network for Enhancing Hyper Spectral Time Series Modeling
M Eti, J Shekhawat, M Nagpal… - … on Paradigm Shift in …, 2023 - ieeexplore.ieee.org
This take a look at proposes using lengthy quick-time period reminiscence (LSTM) networks
as a tool for enhancing the accuracy of Hyper Spectral Time series (HSTS) fashions. via the …
as a tool for enhancing the accuracy of Hyper Spectral Time series (HSTS) fashions. via the …