Rebalancing Bike-Sharing System With Deep Sequential Learning

Abstract

Bike-Sharing Systems (BSS), as a green and convenient transportation means, has attracted significant attention and developed rapidly around the world. However, rooted to the temporal-spatial variance of users’ demand, bike stations of BSS can easily run into the empty or full state, which undermines the performance of BSS and users’ experience. To solve this problem and rebalance the bikes efficiently, researchers have proposed lots of methods, especially from the operation research community. However, this problem is intrinsically an NP-hard problem, and most proposed methods cannot be applied to large-scale BSS. In this paper, inspired by recent advance in the AI area, we notice that for a specific bike-sharing system, similar rebalancing problem is solved every day. Thus, it is promising to learn useful knowledge from the past problem instances and use it in the future ones. In this paper, we adopt sequential to sequential learning technique for knowledge learning and use it for new problems. Evaluation on real-world dataset shows that our approach substantially outperforms existing rebalancing schemes.