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Successful Strategy for the introduction of new bike sharing company B-Cycle in the city of Austin
Our objective was to determine optimal food truck routes and schedules within the Austin area given B-cycle data.
We explore high-level usage patterns and develop a model to predict traffic at kiosks throughout the day. This model can be used to keep kiosks properly stocked and improve the customer experience.
bcycle vs other transportation methods
Our algorithm identifies modes of transportation based on time-series GPS coordinates. Over a large amount of time, it is able to predict steady-state and shifts in modes of transportation.
Leveraging machine learning to predict membership types based on trip log(Austin B-Cycle)
Product analytics and future growth implications of BCycle ridership data
We use methods in persistent homology and () to measure periodicity in GPS tracked movement.
A plan for supplying higher-demand stations with more bikes before they run empty.
This project provides a creative visualization of route trajectories that citizens in Beijing take daily through graphing routes in 3D and providing live animations of the routes.
We used K-means clustering to determine the highest congested areas for B-Cycle, and suggested locations to place new kiosks based on this knowledge.
Introducing BCycle into Beijing City! Where should we put the BCycle kiosk? How BCycle will save commuters' time consumption? What are expected Memberships? Check out in this project.
analyse bicycle usage during the time south by southwest
Here's some interesting data about Bcycle usage from 2014 to 2018 by UT students
We will use the Microsoft Research Asia GPS Trajectory Data to predict the time it will take a user to reach their destination.
Using ML to predict the next time a person will be at a location based on previous location history
In this project, we visualize different aspects of BCycle data, analyzing according to season, day of week, and length of bicycle rental.
Predicts how popular a new Bcycle kiosk would be
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