  In recent years, car-sharing services have emerged as viable alternatives to
private individual mobility, promising more sustainable and resource-efficient,
but still comfortable transportation. Research on short-term prediction and
optimization methods has improved operations and fleet control of car-sharing
services; however, long-term projections and spatial analysis are sparse in the
literature. We propose to analyze the average monthly demand in a station-based
car-sharing service with spatially-aware learning algorithms that offer high
predictive performance as well as interpretability. In particular, we compare
the spatially-implicit Random Forest model with spatially-aware methods for
predicting average monthly per-station demand. The study utilizes a rich set of
socio-demographic, location-based (e.g., POIs), and car-sharing-specific
features as input, extracted from a large proprietary car-sharing dataset and
publicly available datasets. We show that the global Random Forest model with
geo-coordinates as an input feature achieves the highest predictive performance
with an R-squared score of 0.87, while local methods such as Geographically
Weighted Regression perform almost on par and additionally yield exciting
insights into the heterogeneous spatial distributions of factors influencing
car-sharing behaviour. Additionally, our study offers effective as well as
highly interpretable methods for diagnosing and planning the placement of
car-sharing stations.
