Spatially-Aware Car-Sharing Demand Prediction

• URL: http://arxiv.org/abs/2303.14421v1
• Date: Sat, 25 Mar 2023 10:10:11 GMT
• Title: Spatially-Aware Car-Sharing Demand Prediction
• Authors: Dominik J. M\"uhlematter, Nina Wiedemann, Yanan Xin and Martin Raubal
• Abstract summary: We analyze the average monthly demand in a station-based car-sharing service with spatially-aware learning algorithms. 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. Our study offers effective as well as highly interpretable methods for diagnosing and planning the placement of car-sharing stations.
• Score: 3.085449079520639
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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