Commuters’ Ridership Forecasting
Many parts of the transportation sector require advanced technology, like Machine Learning, to make important decisions. The transit agencies’ foremost responsibility is to ensure safe and in-time transportation of the passengers.
Machine Learning in transport can help departments to mine the data and find solutions to its various problems. Applications supporting public transport, travel, and parking have widespread use. They offer the possibility to develop smarter and more user-friendly services, which will promote more sustainable transport use.
This Machine Learning use case provides an in-depth analysis of a Transit system in San Francisco Bay Area. These insights will help the organization to smoothly plan, manage, and evaluate its services.
With a huge passenger count on its side, it becomes difficult for the transit organization to gain some insights on parameters like the busiest time of the day, the busiest day of the week, the number of passengers who travel during night time, etc. These insights will help the organization to draw relevant marketing and operational plans to meet the demand.
To meet the growing demands, an in-depth analysis of the weekly average ridership along with the time periods and seasons in San Francisco will provide the organization the required insights to draft their future plans and optimally allocate their resources accordingly.
We use Data Science and Machine Learning models to predict whether the machine fails or works on a respective day if it faces an error.
We have successfully built a forecasting model and forecasted average weekly ridership with 96% accuracy.
To read the complete use-case with detailed information on the data sets, technologies, approach, models and procedures followed download usecase.