Multivariate Demand Forecasting for Rental Bike Systems Based on an Unobserved Component Model
| Autor: | Christian Wirtgen, Matthias Kowald, Johannes Luderschmidt, Holger Hünemohr |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
demand forecasting
300 Sozialwissenschaften::380 Handel Kommunikation Verkehr::388 Verkehr Computer Networks and Communications Hardware and Architecture Control and Systems Engineering time series analysis dashboard Signal Processing smart mobility Electrical and Electronic Engineering unobserved component model visualization |
| Zdroj: | Electronics; Volume 11; Issue 24; Pages: 4146 |
| ISSN: | 2079-9292 |
| Popis: | Many German cities, municipalities and transport associations are expanding their bike-sharing systems (BSS) to offer citizens a cost-effective and climate-friendly means of transport and an alternative to private motorized transport (PMT). However, operators face the challenge of generating high-quality predictive analyses and time series forecasts. In particular, the prediction of demand is a key component to foster data-driven decisions. To address this problem, an Unobserved Component Model (UCM) has been developed to predict the monthly rentals of a BSS, whereby the station-based BSS VRNnextbike, including over 2000 bikes, 297 stations and 21 municipalities, is employed as an example. The model decomposes the time series into trend, seasonal, cyclical, auto-regressive and irregular components for statistical modeling. Additionally, the model includes exogenous factors such as weather, user behavior (e.g., traveled distance), school holidays and COVID-19 relevant covariates as independent effects to calculate scenario based forecasts. It can be shown that the UCM calculates reasonably accurate forecasts and outperforms classical time series models such as ARIMA(X) or SARIMA(X). Improvements were observed in model quality in terms of AIC/BIC (2.5% to 22%) and a reduction in error metrics from 15% to 45% depending on the considered model. |
| Databáze: | OpenAIRE |
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