Fake it till you make it: Synthetic data for emerging carsharing programs
- Carsharing is an integral part of the transformation toward flexible and sustainable mobility. New carsharing programs are entering the market to challenge large operators by offering innovative services. This study investigates the use of generative machine learning models for creating synthetic data to support carsharing decision–making when data access is limited. To this end, it explores the evaluation, selection, and implementation of leading-edge methods, such as generative adversarial networks (GANs) and variational autoencoders (VAEs), to generate synthetic tabular transaction data of carsharing trips. The study analyzes usage data of an emerging carsharing program that is expanding its services to include free-floating electric vehicles (EVs). The results show that augmenting real training data with synthetic samples improves predictive modeling of upcoming trips by up to 4.63%. These results support carsharing researchers and practitioners in generating and leveraging synthetic mobility data to develop solutions toCarsharing is an integral part of the transformation toward flexible and sustainable mobility. New carsharing programs are entering the market to challenge large operators by offering innovative services. This study investigates the use of generative machine learning models for creating synthetic data to support carsharing decision–making when data access is limited. To this end, it explores the evaluation, selection, and implementation of leading-edge methods, such as generative adversarial networks (GANs) and variational autoencoders (VAEs), to generate synthetic tabular transaction data of carsharing trips. The study analyzes usage data of an emerging carsharing program that is expanding its services to include free-floating electric vehicles (EVs). The results show that augmenting real training data with synthetic samples improves predictive modeling of upcoming trips by up to 4.63%. These results support carsharing researchers and practitioners in generating and leveraging synthetic mobility data to develop solutions to real-world decision support problems in carsharing.…
Author: | Tobias Albrecht, Robert KellerORCiDGND, Dominik RebholzORCiD, Maximilian Röglinger |
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DOI: | https://doi.org/10.1016/j.trd.2024.104067 |
Identifier: | 1361-9209 OPAC HS OPAC extern |
Identifier: | 1879-2340 OPAC HS OPAC extern |
Parent Title (English): | Transportation Research Part D |
Publisher: | Elsevier |
Place of publication: | Amsterdam |
Document Type: | Article |
Language: | English |
Date of Publication (online): | 2024/01/17 |
Year of first Publication: | 2024 |
Tag: | Carsharing; Electric vehicle; Generative adversarial network; Machine learning; Shared mobility; Synthetic data |
Volume: | 28 |
Issue: | 127 / Februar |
Article Number: | 104067 |
Number of pages: | 20 Seiten |
First Page: | 1 |
Last Page: | 20 |
Institutes: | Fakultät Tourismus-Management |
INIT – Institut für Nachhaltige und Innovative Tourismusentwicklung | |
Open Access: | open_access |
Research focus: | FSP1: Energie |
FSP2: Mobilität | |
FSP4: Soziale Innovationen | |
Publication Lists: | Keller, Robert |
Rebholz, Dominik | |
Publication reviewed: | begutachtet |
Licence (German): | Creative Commons - CC BY - Namensnennung 4.0 International |
Release Date: | 2024/02/01 |