Pytorch implementation of structural recurrent neural network (SRNN) for traffic speed prediction.
Built with Python 3.5
- Scalable learning of the interaction between adjacent road segments and the temporal dynamics
- Able to predict traffic speed of road networks different from the network used to train
- Outperforms the image-based approaches using the CapsNet and CNN, requiring the smaller, constant number of parameters to train
Please cite the following paper if you find this code helpful:
Youngjoo Kim, Peng Wang, and Lyudmila Mihaylova, "Scalable Learning with a Structural Recurrent Neural Network for Short-Term Traffic Prediction", IEEE Sensors Journal, vol. 19, issue. 23, pp. 11359-11366, 2019. Available: https://www.researchgate.net/publication/335076235_Scalable_Learning_with_a_Structural_Recurrent_Neural_Network_for_Short-Term_Traffic_Prediction
Preliminary work:
Youngjoo Kim, Peng Wang, and Lyudmila Mihaylova, "Structural Recurrent Neural Network for Traffic Speed Prediction", IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2019. Available: https://www.researchgate.net/publication/331222757_Structural_Recurrent_Neural_Network_for_Traffic_Speed_Prediction