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A Bayesian approach to quantifying uncertainties and improving generalizability in traffic prediction models
Agnimitra Sengupta, Sudeepta Mondal, Adway Das +1
Deep-learning models for traffic data prediction can have superior performance in modeling complex functions using a multi-layer architecture. However, a major drawback of these ap…
Evaluating the reliability of automatically generated pedestrian and bicycle crash surrogates
Agnimitra Sengupta, S. Ilgin Guler, Vikash V. Gayah +1
Vulnerable road users (VRUs), such as pedestrians and bicyclists, are at a higher risk of being involved in crashes with motor vehicles, and crashes involving VRUs also are more li…
Newell's theory based feature transformations for spatio-temporal traffic prediction
Agnimitra Sengupta, S. Ilgin Guler
Deep learning (DL) models for spatio-temporal traffic flow forecasting employ convolutional or graph-convolutional filters along with recurrent neural networks to capture spatial a…
Hybrid hidden Markov LSTM for short-term traffic flow prediction
Agnimitra Sengupta, Adway Das, S. Ilgin Guler
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…