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https://doi.org/10.21256/zhaw-19924| Publication type: | Article in scientific journal |
| Type of review: | Peer review (publication) |
| Title: | Fleet management in free-floating bike sharing systems using predictive modelling and explorative tools |
| Authors: | Templ, Matthias Heitz, Christoph |
| et. al: | No |
| DOI: | 10.17713/ajs.v49i2.1114 10.21256/zhaw-19924 |
| Published in: | Austrian Journal of Statistics |
| Volume(Issue): | 49 |
| Issue: | 2 |
| Page(s): | 53 |
| Pages to: | 69 |
| Issue Date: | Feb-2020 |
| Publisher / Ed. Institution: | Austrian Statistical Society |
| ISSN: | 1026-597X |
| Language: | English |
| Subjects: | Free-floating bike system; Spatio-temporal data; Generalized additive model; Markov chain; Managment tool |
| Subject (DDC): | 003: Systems |
| Abstract: | For redistribution and operating bikes in a free-floating systems, two measures are of highest priority. First, the information about the expected number of rentals on a day is an important measure for service providers for management and service of their fleet. The estimation of the expected number of bookings is carried out with a simple model and a more complex model based on meterological information, as the number of loans depends strongly on the current and forecasted weather. Secondly, the knowledge of a service level violation in future on a fine spatial resolution is important for redistribution of bikes. With this information, the service provider can set reward zones where service level violations will occur in the near future. To forecast a service level violation on a fine geographical resolution the current distribution of bikes as well as the time and space information of past rentals has to be taken into account. A Markov Chain Model is formulated to integrate this information. We develop a management tool that describes in an explorative way important information about past, present and predicted future counts on rentals in time and space. It integrates all estimation procedures. The management tool is running in the browser and continuously updates the information and predictions since the bike distribution over the observed area is in continous flow as well as new data are generated continuously. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/19924 |
| Fulltext version: | Published version |
| License (according to publishing contract): | CC BY 4.0: Attribution 4.0 International |
| Departement: | School of Engineering |
| Organisational Unit: | Institute of Data Analysis and Process Design (IDP) |
| Appears in collections: | Publikationen School of Engineering |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2020_Templ-Heitz_Fleet-Management_AJS.pdf | 12.4 MB | Adobe PDF |  View/Open |
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Templ, M., & Heitz, C. (2020). Fleet management in free-floating bike sharing systems using predictive modelling and explorative tools. Austrian Journal of Statistics, 49(2), 53–69. https://doi.org/10.17713/ajs.v49i2.1114
Templ, M. and Heitz, C. (2020) ‘Fleet management in free-floating bike sharing systems using predictive modelling and explorative tools’, Austrian Journal of Statistics, 49(2), pp. 53–69. Available at: https://doi.org/10.17713/ajs.v49i2.1114.
M. Templ and C. Heitz, “Fleet management in free-floating bike sharing systems using predictive modelling and explorative tools,” Austrian Journal of Statistics, vol. 49, no. 2, pp. 53–69, Feb. 2020, doi: 10.17713/ajs.v49i2.1114.
TEMPL, Matthias und Christoph HEITZ, 2020. Fleet management in free-floating bike sharing systems using predictive modelling and explorative tools. Austrian Journal of Statistics. Februar 2020. Bd. 49, Nr. 2, S. 53–69. DOI 10.17713/ajs.v49i2.1114
Templ, Matthias, and Christoph Heitz. 2020. “Fleet Management in Free-Floating Bike Sharing Systems Using Predictive Modelling and Explorative Tools.” Austrian Journal of Statistics 49 (2): 53–69. https://doi.org/10.17713/ajs.v49i2.1114.
Templ, Matthias, and Christoph Heitz. “Fleet Management in Free-Floating Bike Sharing Systems Using Predictive Modelling and Explorative Tools.” Austrian Journal of Statistics, vol. 49, no. 2, Feb. 2020, pp. 53–69, https://doi.org/10.17713/ajs.v49i2.1114.
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