Predictive Modeling for Metro Performance Using MetroPT3 Dataset

The study titled "Predictive Modeling for Metro Performance Using the MetroPT3 Dataset" aims to create a predictive maintenance system for the metro systems in order to reduce unanticipated breakdowns. The dataset known as MetroPT3 is primarily used to provide data useful in monitoring the operation of certain features of the APU and includes several types of time-series data like air pressure, the current drawn by a motor and oil temperatures. Some basic data quality enhancement procedures, such as cleaning, interpolation of missing entries and normalization were performed. The analysis aims to develop a Long Short-Term Memory (LSTM) Autoencoder based on an encoder-decoder architecture to perform sequence modeling and identify anomalies. The model learns normal operational patterns and detects deviations using reconstruction error as an anomaly threshold, enabling timely intervention. The results obtained are encouraging since the model performed excellently in reconstructing clean operating values using the Autoencoder structure.

@article{C2025,
  title     = {Predictive Modeling for Metro Performance Using MetroPT3 Dataset},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Akshitha Mary A C and R Rakshinee and Stefani Jeyaseelan and Sakthivel V and Prakash P},
  doi       = {10.14569/IJACSA.2025.0160854},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160854}
}