A Bike-sharing Optimization Framework Combining Dynamic Rebalancing and User Incentives

Abstract

Bike-sharing systems have become an established reality in cities all across the world and are a key component of the Smart City paradigm. However, the unbalanced traffic patterns during rush hours can completely empty some stations, while filling others, and the service becomes unavailable for further users. The traditional approach to solve this problem is to use rebalancing trucks, which take bikes from full stations and deposit them at empty ones, reducing the likelihood of system outages. Another paradigm that is gaining steam is gamification, i.e., incentivizing users to fix the system by influencing their behavior with rewards and prizes. In this work, we combine the two efforts and show that a joint optimization considering both rebalancing and incentives results in a higher service quality for a lower cost than using simple rebalancing. We use simulations based on the New York CitiBike usage data to validate our model and analyze several schemes to optimize the bike-sharing system.