A Meta-Learning Algorithm for Rebalancing the Bike-Sharing System in IoT Smart City

Abstract

With the development of intelligent transport systems in the Internet of Things (IoT) smart cities, the bike-sharing system provides an environment-friendly choice for short-distance commuting, and it is employed extensively in major cities around the world. However, the issue of sharing bikes imbalance in various bike-sharing stations (BSS) constantly exists. Therefore, planning an effective route for rebalancing the bike-sharing system becomes a crucial task. In this article, based on a novel rebalancing problem of bike-sharing systems, which is to maximize the total allocated bikes at different stations under the constrained scheduling resources, we propose a meta-learning algorithm named ALRL to effectively allocate the sharing bikes under realistic constraints. Experimental results on real data sets and case studies demonstrate the effectiveness of our proposed approach which is better than the traditional methods.

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