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Metro Operations Optimization refers to the systematic process of enhancing the efficiency, reliability, and effectiveness of Metro services through various data-driven techniques and operational adjustments.

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Metro-Operations-Optimization-using-Python

Metro Operations Optimization refers to the systematic process of enhancing the efficiency, reliability, and effectiveness of Metro services through various data-driven techniques and operational adjustments.

The problem I am working on in this article is my take on a problem statement I found at statso. The dataset contains several files, as follows:

Agency: Information about the Delhi Metro Rail Corporation, including name, URL, and contact details. Calendar: Service schedules delineating the operation days (weekdays, weekends) and valid dates for these schedules. Routes: Details of metro routes, including short and long names, type of route, and descriptions. Shapes: Geographical coordinates of routes, providing the precise paths taken by the metro lines. Stop Times: Timetables for each trip indicating arrival and departure times at specific stops. Stops: Locations of metro stops, including latitude and longitude coordinates. Trips: Information linking trips to routes, including details like trip identifiers and associated route IDs.

For the task of Metro Operations Optimization, we will aim to identify the key areas where adjustments in train frequencies could significantly improve the service levels, reduce wait times, and alleviate overcrowding.

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Metro Operations Optimization refers to the systematic process of enhancing the efficiency, reliability, and effectiveness of Metro services through various data-driven techniques and operational adjustments.

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