Cornell University researchers have developed algorithms and data analysis tools to help rebalance the New York City Citi Bike system as efficiently as possible.
The researchers first analyzed massive quantities of data to learn usage patterns and determine how many bikes would be found at each station at key times during the day. "The next step is to figure out how many bikes should be at each station at key times, so riders would find bikes available as well as open docks to put them in at the end of a ride," says Cornell professor David Shmoys.
The researchers used the algorithms to create a system that generates a map showing dispatchers where bikes are needed the most, given the current state and expected usage.
The researchers note the program must calculate many possible solutions across the entire city and choose the one with the best overall result. First, the algorithm represents each possible route as a separate point in a high-dimension space, then repeatedly recalculates to eliminate a large fraction of those points in each step.
In the future, the researchers want to develop a system to choose the best locations for new bike-sharing sites, based on data from taxi usage and neighborhood boundaries.
From Cornell Chronicle
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