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Found 4 papers, 0 papers with code
Evaluating the reliability of automatically generated pedestrian and bicycle crash surrogates
Signalized intersections are a major safety concern for VRUs due to their complex and dynamic nature, highlighting the need to understand how these road users interact with motor vehicles and deploy evidence-based countermeasures to improve safety performance.
Newell's theory based feature transformations for spatio-temporal traffic prediction
Hence, the transferability of these models to different locations becomes challenging, particularly when data is unavailable at the new location for model training.
A Bayesian approach to quantifying uncertainties and improving generalizability in traffic prediction models
In our paper, we have shown that normalization alters the training process of deep neural networks by controlling the model's complexity and reducing the risk of overfitting to the training data.
Hybrid hidden Markov LSTM for short-term traffic flow prediction
Deep learning (DL) methods have outperformed parametric models such as historical average, ARIMA and variants in predicting traffic variables into short and near-short future, that are critical for traffic management.
