A Dynamic Shared Bikes Rebalancing Method Based on Demand Prediction

Xiaojian Zhang1, Hongtai Yang1, Rong Zheng1, Zhicheng Jin1, Bowen Zhou1

  • 1Southwest Jiaotong University

Details

Category

Regular Session

Sessions

11:00 - 12:00 | Mon 28 Oct | Gallery Room 3 | MoC-T10

Regular Session on Public Transportation Management (I)

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Abstract

The free-floating bike sharing systems (BSSs) are booming all over the world. How to rebalance the bikes is a problem faced by all operators. To tackle this problem, firstly, we compare five models to predict shared bikes demand and choose time series and decision tree model. Then based on the prediction results, we propose a zone-based two-stage rebalancing model and an algorithm to solve this model. The proposed model divides the research area into two kinds of zones: zones with deficient bikes (ZDB) and zones with sufficient bikes (ZSB). The objective of the model is to optimize the matching degree of the demand and actual number of shared bikes in each zone. Finally, we employ real world data to validate the flexibility and practicality of our model and algorithm. Experimental results demonstrate that this method can effectively balance all zones.

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