Bike usage forecasting for optimal rebalancing operations in bike-sharing systems

Ruffieux, Simon (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland) ; Mugellini, Elena (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland) ; Abou Khaled, Omar (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland)

This article presents the first step of a project focusing on enhancing the management of bike-sharing systems. The objective of the project is to optimize the daily rebalancing operations that need to be performed by operators of bike-sharing systems using machine-learning algorithms and constraint programming. This study presents an evaluation of machine learning algorithms developed for forecasting the availability of bikes on three Swiss bike-sharing networks. The results demonstrate the superiority of the Multi-Layer Perceptron algorithm for forecasting available bikes at station-level for different prediction horizons and its applicability for real-time prediction generation.


Keywords:
Conference Type:
full paper
Faculty:
Ingénierie et Architecture
School:
HEIA-FR
Institute:
HumanTech - Technology for Human Wellbeing Institute
Subject(s):
Ingénierie
Publisher:
Volos, Greece, 5-7 November 2018
Date:
2018-12
Volos, Greece
5-7 November 2018
Pagination:
6 p.
Published in:
Proceedings of 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI), 5-7 November 2018, Volos, Greece
DOI:
ISBN:
978-1-5386-7449-9
Appears in Collection:

Note: The status of this file is: restricted


 Record created 2019-01-22, last modified 2019-01-29

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