Abstract: Intelligent Transportation System (ITS) is the fundamental requirement to an intelligent transport system. The proposed hybrid model Stacked Bidirectional LSTM and Attention-based GRU (SBAG) is used for predicting the large scale traffic speed. To capture bidirectional temporal dependencies and spatial features, BDLSTM and attention-based GRU are exploited. It is the first time in traffic speed prediction that bidirectional LSTM and attention-based GRU are exploited as a building block of network architecture to measure the backward dependencies of a network. We have also examined the behaviour of the attention layer in our proposed model. We compared the proposed model with state-of-the-art models e.g. Fully Convolutional Network, Gated Recurrent Unit, Long -short term Memory, Bidirectional Long-short term Memory and achieved superior performance in large scale traffic speed prediction.

Adnan Riaz, Muhammad Nabeel, Mehak Khan and Huma Jamil, “SBAG: A Hybrid Deep Learning Model for Large Scale Traffic Speed Prediction” International Journal of Advanced Computer Science and Applications(IJACSA), 11(1), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110135

@article{Riaz2020,
title = {SBAG: A Hybrid Deep Learning Model for Large Scale Traffic Speed Prediction},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110135},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110135},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {1},
author = {Adnan Riaz and Muhammad Nabeel and Mehak Khan and Huma Jamil}
}