@inproceedings{702abe50634041418c1d3a6aa611a92e,
title = "Back Propagation Neural Network with Feature Sensitivity Analysis in Railways: Short Term Prediction of Passenger Flow using Time, Environmental and Operational Factors",
abstract = "Railway stations serves as the main hub of carrier and transfer of passengers. The operations within the station is influenced majorly on the passenger flow. Having a reasonable and accurate prediction of passengers entering and leaving the station can be a basis for the station security, resources allocation and deployment of personnel. Neural network models can predict and process irregular data of the passenger flow and is susceptible to the other external factors. This paper introduces Black Propagation Neural Network in predicting the passenger flow.",
keywords = "Black Propagation Neural Network prediction, Passenger Flow, railway station, railway transportation",
author = "Limcauco, {John Argon Valenzuela} and Prasetyo, {Yogi Tri} and Reny Nadlifatin",
note = "Publisher Copyright: {\textcopyright} 2021 ACM.; 7th International Conference on Industrial and Business Engineering, ICIBE 2021 ; Conference date: 27-09-2021 Through 29-09-2021",
year = "2021",
month = sep,
day = "27",
doi = "10.1145/3494583.3494585",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "371--378",
booktitle = "ICIBE 2021 - 2021 7th International Conference on Industrial and Business Engineering",
}