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.


Mots-clés:
Type de conférence:
full paper
Faculté:
Ingénierie et Architecture
Ecole:
HEIA-FR
Institut:
HumanTech - Technology for Human Wellbeing Institute
Classification:
Ingénierie
Adresse bibliogr.:
Volos, Greece, 5-7 November 2018
Date:
2018-12
Volos, Greece
5-7 November 2018
Pagination:
6 p.
Veröffentlicht 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
Le document apparaît dans:

Note: The status of this file is: restricted


 Datensatz erzeugt am 2019-01-22, letzte Änderung am 2019-01-29

Volltext:
Volltext herunterladen
PDF

Dieses Dokument bewerten:

Rate this document:
1
2
3
 
(Bisher nicht rezensiert)