[HTML][HTML] In-depth insights into the application of recurrent neural networks (rnns) in traffic prediction: A comprehensive review
Traffic prediction is crucial for transportation management and user convenience. With the
rapid development of deep learning techniques, numerous models have emerged for traffic …
rapid development of deep learning techniques, numerous models have emerged for traffic …
Edge-based graph neural network for ranking critical road segments in a network
Transportation networks play a crucial role in society by enabling the smooth movement of
people and goods during regular times and acting as arteries for evacuations during …
people and goods during regular times and acting as arteries for evacuations during …
Long-Term Demand Prediction for Public Bicycle Sharing System: A Spatio-Temporal Attentional Graph Convolution Networks Approach
Z Liu, H Gokon, Y Sekimoto - IEEE Transactions on Intelligent …, 2024 - ieeexplore.ieee.org
Accurately predicting the long-term demand for public bicycle systems (PBS) is crucial for
policy implementations such as operator rebalancing. With the continuous advancement of …
policy implementations such as operator rebalancing. With the continuous advancement of …
Trajectory Data Mining and Trip Travel Time Prediction on Specific Roads
Predicting a trip's travel time is essential for route planning and navigation applications. The
majority of research is based on international data that does not apply to Pakistan's road …
majority of research is based on international data that does not apply to Pakistan's road …
Hourly Long-Term Traffic Volume Prediction with Meteorological Information Using Graph Convolutional Networks
S Park, M Kim, J Kim - Applied Sciences, 2024 - mdpi.com
Hourly traffic volume prediction is now emerging to mitigate and respond to hourly-level
traffic congestion augmented by deep learning techniques. Incorporating meteorological …
traffic congestion augmented by deep learning techniques. Incorporating meteorological …