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In recent years, car-sharing services have emerged as viable alternatives toprivate individual mobility, promising more sustainable and resource-efficient,but still comfortable transportation. Research on short-term prediction andoptimization methods has improved operations and fleet control of car-sharingservices; however, long-term projections and spatial analysis are sparse in theliterature. We propose to analyze the average monthly demand in a station-basedcar-sharing service with spatially-aware learning algorithms that offer highpredictive performance as well as interpretability. In particular, we comparethe spatially-implicit Random Forest model with spatially-aware methods forpredicting average monthly per-station demand. The study utilizes a rich set ofsocio-demographic, location-based (e.g., POIs), and car-sharing-specificfeatures as input, extracted from a large proprietary car-sharing dataset andpublicly available datasets. We show that the global Random Forest model withgeo-coordinates as an input feature achieves the highest predictive performancewith an R-squared score of 0.87, while local methods such as GeographicallyWeighted Regression perform almost on par and additionally yield excitinginsights into the heterogeneous spatial distributions of factors influencingcar-sharing behaviour. Additionally, our study offers effective as well ashighly interpretable methods for diagnosing and planning the placement ofcar-sharing stations.

Spatially-Aware Car-Sharing Demand Prediction