1. bookVolume 30 (2022): Edition 3 (September 2022)
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Magazine
eISSN
2450-5781
Première parution
30 Mar 2017
Périodicité
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Anglais
access type Accès libre

Accuracy of Hourly Demand Forecasting of Micro Mobility for Effective Rebalancing Strategies

Publié en ligne: 13 Jul 2022
Volume & Edition: Volume 30 (2022) - Edition 3 (September 2022)
Pages: 246 - 252
Reçu: 01 Dec 2021
Accepté: 01 Jul 2022
Détails du magazine
License
Format
Magazine
eISSN
2450-5781
Première parution
30 Mar 2017
Périodicité
4 fois par an
Langues
Anglais
Abstract

The imbalance in bike-sharing systems between supply and demand is significant. Therefore, these systems need to relocate bikes to meet customer needs. The objective of this research is to increase the efficiency of bike-sharing systems regarding rebalancing problems. The prediction of the demand for bike sharing can enhance the efficiency of a bike-sharing system for the operation process of rebalancing in terms of the information used in planning by proposing an evaluation of algorithms for forecasting the demand for bikes in a bike-sharing network. The historical, weather and holiday data from three distinct databases are used in the dataset and three fundamental prediction models are adopted and compared. In addition, statistical approaches are included for selecting variables that improve the accuracy of the model. This work proposes the accuracy of different models of artificial intelligence techniques to predict the demand for bike sharing. The results of this research will assist the operators of bike-sharing companies in determining data concerning the demand for bike sharing to plan for the future. Thus, these data can contribute to creating appropriate plans for managing the rebalancing process.

Keywords

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