Latent stage model for carsharing usage frequency estimation with Montréal case study
In order to predict the monthly usage frequency of members of a car-sharing scheme by analysing the gradual change of behaviour over time, a new model is proposed based on the Markov Chains model with latent stages. The model accounts for changing patterns of frequency from soon after signing up to later stages by including five latent user ‘life stages’. In applying the model to panel data from Montreal’s free-floating carsharing service the authors calculate each user’s ’lifetime’ applied to ‘system operation time’, the time period since the start of the scheme. Three-fold validation reveals effective performance of the model for both lifetime and system operation time dimensions. The model is further applied to illustrate how previous carsharing experience and the extension of the scheme to a larger area can affect usage frequency changes. We conclude that this approach is effective for usage prediction for novel transport schemes.
- Record URL:
- Record URL:
-
Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/00494488
-
Supplemental Notes:
- Copyright © 2022, Springer Nature. The contents of this paper reflect the views of the author[s] and do not necessarily reflect the official views or policies of the Transportation Research Board or the National Academy of Sciences.
-
Authors:
- Zhang, Cen
- Schmöcker, Jan-Dirk
-
0000-0003-2219-9447
- Trépanier, Martin
- Publication Date: 2021-2
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: 185–211
-
Serial:
- Transportation
- Volume: 49
- Issue Number: 1
- Publisher: Springer
- ISSN: 0049-4488
- EISSN: 1572-9435
- Serial URL: http://link.springer.com/journal/11116
Subject/Index Terms
- TRT Terms: Life cycle analysis; Markov chains; Stochastic processes; Travel behavior; Vehicle sharing
- Subject Areas: Highways; Safety and Human Factors;
Filing Info
- Accession Number: 01844374
- Record Type: Publication
- Files: TRIS
- Created Date: Apr 28 2022 3:41PM