Improvements to Worker Assignment in Bike Sharing Systems

Abstract

Bike-sharing systems (BSSs) are widely used in cities worldwide as they offer an affordable, eco-friendly method of transport. However, the rate of renting and returning bikes from stations is not always equal. The stations with imbalanced demand can become out of service by having all docks filled or emptied. These out-of-service stations can lead to a worse user experience and fewer people using BSSs. Researchers are trying to solve the rebalancing problem by developing algorithms that incentivize workers to pick up and drop off bicycles from different stations to balance the rent and return rates. Many of these algorithms focus on creating incentive and pricing models to encourage workers to go to imbalanced stations. Since they do not consider all of the placements of workers, this strategy may lead to inefficiencies where workers travel farther than they need. We can treat rebalancing as a Worker Assignment Problem by assigning worker stations to minimize the total distance traveled. We propose an algorithm that can approximate the optimal assignment significantly faster than other techniques with very high performance. The rapid speed allows for real-time use in augmenting pricing models and as a stand-alone method for worker assignment. Furthermore, we compare our approach against four existing algorithms on real-world data to evaluate computational speed and effectiveness.

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