Background

Delay in train arrivals is a common phenomenon and it needs to be efficiently managed to minimize any time delay for passengers waiting to commute in a train. The primary objective of this project was to analyze the existing delay in train schedules and later predict the time delay for any given time and train routes, considering various factors that influence the arrival of a train to the station. Our client provides public train commute services and manages scheduling and operation of trains on various train routes. The delay in schedules for trains on routes they are operated has to be managed with the least possible and predictable delay time.

Maintaining a predictable train delay impacts the commute pattern of passengers and impacts the frequency of train services on all routes.

TekBank used the following technologies in developing our Predictive Analytics solution:

• Data Engineering using Time series and meta data
• Machine Learning Operations (MLOPS)
• Deep AR on Amazon Web service
• Professional Visualization using Tableau
• Test and train ML models using DeepDR+ Algorithms
• Amazon S3 captures training dataset

Results

The result of this project aims at developing a MLOPS workflow using AWS web services cloud computing platform to store and retrieve data sets in real time. The following is the nature of workflow that was intended to be adhered to achieve a better delay time prediction capability using MLOPS,

• Creating a target dataset
• Ingest data set into Data Integration platform
• Import data as training and test data set in the pre-processing stage
• Create a predictor model using Deep AR+ algorithm and train the model
• Create a forecast on how delay time is expected to be in the future (for the next 14 days)
• Update data sources and other model parameters in real-time
• Reduce error rate in the forecasted schedule over the actual schedule

Visualization of the Data – Tableau
The interpretation of the comparison of delay minutes grouped was by delay code between the actual delay time and the predicted delay time. This dashboard is updated every two weeks in production, based on forecast.