Publication Type : Journal Article
Source : Neural Network World, 31(3), 173. (Impact Factor : 1.48 )
Url : http://www.nnw.cz/doi/2021/NNW.2021.31.009.pdf
Campus : Coimbatore
School : School of Artificial Intelligence - Coimbatore
Year : 2021
Abstract : Metro rail systems are increasingly becoming relevant and inevitable
in the context of rising demand for sustainable transportation methods. Metros
are therefore going to have a consistently expanding user-base and hence user satisfaction will require meticulous planning. Usage forecast is clearly an integral
component of metro planning as it enables forward looking and efficient allocation
of resources leading to greater commuter satisfaction. An observation from studying the usage of Kochi Metro Rail Ltd. is that there is a consistently occurring
temporal pattern in usage for every station. But the patterns differ from station
to station. This hinders the search for a global model representing all stations.
We propose a way to overcome this by using station memorizing Long Short-Term
Memory (LSTM) which takes in stations in encoded form as input along with usage
sequence of stations. This is observed to significantly improve the performance of
the model. The proposed architecture with station parameter is compared with algorithms like SVR (support vector regression) and neural network implementation
with the best architecture to testify the claim. The proposed model can predict
the future flow with an error rate of 0.00127 MSE (mean squared error), which is
better than the other models tested
Cite this Research Publication : Sajanraj, T. D., Mulerikkal, J., Raghavendra, S., Vinith, R., Fabera, V. (2021). ”Passenger flow prediction from AFC data using station memorizing LSTM for metro rail systems”, Neural Network World, 31(3), 173. (Impact Factor : 1.48 )