- Recent advances in deep learning for traffic probabilistic prediction🔍
- Deep Learning Algorithms for Traffic Forecasting🔍
- Enhancing road traffic flow prediction with improved deep learning ...🔍
- Recent Advances in Traffic Accident Analysis and Prediction🔍
- Probabilistic Deep Learning🔍
- Traffic Flow Prediction Research Based on an Interactive Dynamic ...🔍
- Applications of deep learning in congestion detection🔍
- Deep learning for intelligent traffic sensing and prediction🔍
Recent advances in deep learning for traffic probabilistic prediction
Recent advances in deep learning for traffic probabilistic prediction
This editorial provides a concise review focussing on the recent advancements in deep learning techniques for probabilistic traffic prediction.
Recent advances in deep learning for traffic probabilistic prediction
Recent advances in deep learning for traffic probabilistic prediction · Full Article · Figures & data · References · Citations · Metrics · Reprints & ...
Recent advances in deep learning for traffic probabilistic prediction
Long Cheng & Da Lei & Sui Tao, 2024. "Recent advances in deep learning for traffic probabilistic prediction," Transport Reviews, Taylor & Francis Journals, ...
Recent advances in deep learning for traffic probabilistic prediction
To fill this gap, we proposed SwarmDeepSurv by integrating swarm intelligence algorithms ... [Show full abstract] ...
Recent advances in deep learning for traffic probabilistic prediction
Recent advances in deep learning for traffic probabilistic prediction. 交通概率预测中深度学习的最新进展. Long Cheng, Da Lei, Sui Tao. DOI: 10.1080 ...
Deep Learning Algorithms for Traffic Forecasting: A Comprehensive ...
[14] have provided a comprehensive review of recent advances in the use of deep learning techniques for STDM. A systematic literature review ...
Enhancing road traffic flow prediction with improved deep learning ...
A diverse range of deep learning techniques has been employed in traffic flow forecasting [33], [34]. Among these methods, Recurrent Neural Networks (RNNs) have ...
Recent Advances in Traffic Accident Analysis and Prediction - arXiv
[23] compare SVM and Probabilistic Neural Network (PNN) models for real-time freeway accident detection using loop detector data, with PNN showing superior ...
Probabilistic Deep Learning | Papers With Code
Recent advances in probabilistic deep learning enable efficient amortized Bayesian inference in settings where the likelihood function is only implicitly ...
Traffic Flow Prediction Research Based on an Interactive Dynamic ...
By embedding the DGCN into the interactive learning structure, the dynamic spatial features of traffic flows are interactively acquired, while capturing their ...
Applications of deep learning in congestion detection, prediction ...
Detecting, predicting, and alleviating traffic congestion are targeted at improving the level of service of the transportation network.
Deep learning for intelligent traffic sensing and prediction
(2019) presented a review of short-term traffic state prediction with neural network-based models. Summary. The recent development in deep learning has produced ...
Enhancing Deep Learning-Based City-Wide Traffic Prediction ...
Deep learning models can effectively capture the non-linear spatiotemporal dynamics of city-wide traffic forecasting.
A Review of Traffic Congestion Prediction Using Artificial Intelligence
In recent years, traffic congestion prediction has led to a growing research area, especially of machine learning of artificial intelligence ...
A Survey on Deep Learning for Cellular Traffic Prediction
[16] investigated using a long short-term memory (LSTM) neural network [17] for cellular traffic prediction. Wang et al. [18] employed an LSTM network to learn ...
Road traffic can be predicted by machine learning equally effectively ...
A new paper, published in April 2023, proposes a hybrid STFSA-CNN-GRU model for short-term traffic speed prediction. This model eliminates the ...
Why Uncertainty in Deep Learning for Traffic Flow Prediction Is ...
Two recent examples demonstrate the importance of uncertainty in deep learning. Such misclassifications or misjudgments in traffic flow predictions can have ...
Uncertainty-Aware Probabilistic Graph Neural Networks for Road ...
Recent advances in deep learning techniques have shown promise in urban traffic crash prediction by incorporating spatio-temporal characteristics of the data.
A Probabilistic Neural Network-Based Road Side Unit Prediction ...
Abstract: Vehicular Networks will play a leading role in the next generation of Autonomous Driving (AD), as recent advances in vehicular networks are a ...
Recent advances in deep long-horizon forecasting - Google Research
Below on the left, we show that our model can be 10.6% better than the best transformer-based baseline (PatchTST) on a popular traffic ...