This workflow implements an alarm system for bicycle restocking at Washington bike stations. Capital Bikeshare offers a download of their bike usage data dating as far back as 2010. They also offer a live REST-API. In this workflow, we use data from 2018 and 2019 as training data and evaluate the learned model on the data of the first three months of 2020.
The task is to predict one of three classes, that is whether a bike station needs: to remove bikes, to add bikes, or no action. Predicting 3 classes is easier than predicting a precise number and classification methods can be used. We only use the data that was provided by capital bikeshare (with some assumptions on the initial status of the stations at the end of 2017). The primary purpose of this workflow is to show how KNIME and Cumulocity can be used in concert. We therefore do not put much effort in the optimization of the machine learning model and also to not use any external data. A natural extension of this workflow would be for example to join weather data to the device data in order to improve the prediction quality.
To use this workflow in KNIME, download it from the below URL and open it in KNIME:Download Workflow
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