A Spatio-Temporal autocorrelation model for designing a carshare system using historical heterogeneous Data: Policy suggestion
As an emerging urban mobility service, carsharing has become increasingly popular worldwide. To understand customers’ needs and optimize the design of service networks, the usage of carsharing vehicles whose trips are recorded by the operators has been applied in research estimating carsharing demand. However, as a form of spatio-temporal correlated data, the underlying spatio-temporal information included in such carsharing records has not been investigated in existing models of carsharing demand. Meanwhile, due to the supply limitation of carsharing stations, some demand cannot be fulfilled and thus remains unrecorded in the operational data. Unrealized demand may lead to underestimation of carsharing demand and therefore an incorrect vehicle deployment strategy by the service providers. In view of these issues, this paper develops an innovative approach to estimating the actual demand at a carsharing station with operational data from GoGet, the largest carsharing company in Australia. The accuracy of the estimation is improved by adding spatio-temporal correlated variables as well as variables from emerging data sources such as social media. To explore the latent space-and-time correlated information, spatio-temporal autoregressive and moving-average models have been applied. Based on the results of the analysis, the paper also provides recommendations related to the operation policies of the service providers.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
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Supplemental Notes:
- © 2022 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
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Authors:
- Cheng, Zesheng
- Rashidi, Taha Hossein
- Jian, Sisi
- Maghrebi, Mojtaba
- Waller, Steven Travis
- Dixit, Vinayak
- Publication Date: 2022-8
Language
- English
Media Info
- Media Type: Web
- Features: Appendices; Figures; References; Tables;
- Pagination: 103758
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Serial:
- Transportation Research Part C: Emerging Technologies
- Volume: 141
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 0968-090X
- Serial URL: http://www.sciencedirect.com/science/journal/0968090X
Subject/Index Terms
- TRT Terms: Automobiles; Data mining; Machine learning; Policy; Social media; Vehicle sharing
- Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Policy; Vehicles and Equipment;
Filing Info
- Accession Number: 01851330
- Record Type: Publication
- Files: TRIS
- Created Date: Jul 14 2022 11:32AM