Spatially-aware station based car-sharing demand prediction

Bibliographic Data

ID12295276
AuthorsDominik J Mühlematter (ETH Zurich, corresponding author), Nina Wiedemann (0000-0002-8160-7634, ETH Zurich), Yanan Xin (0000-0003-3866-821X, ETH Zurich), Martin Raubal (0000-0001-5951-6835, ETH Zurich)
Year2023
Volume114
Pages103765-103765
Publication date2023-12-18
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueJournal of Transport Geography (JOURNAL)
Journal identifiersISSN: 0966-6923 • E-ISSN: 1873-1236
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.jtrangeo.2023.103765
OpenAlexW4389915514
LanguageEN
Citations received1
References cited39

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. Our 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 first compare the performance of different modeling approaches and find that a global Random Forest with geo-coordinates as part of input features achieves the highest predictive performance with an R-squared score of 0.87 on test data. While a local linear model, Geographically Weighted Regression, performs almost on par in terms of out-of-sample prediction accuracy. We further leverage the models to identify spatial and socio-demographic drivers of car-sharing demand. An analysis of the Random Forest via SHAP values, as well as the coefficients of GWR and MGWR models, reveals that besides population density and the car-sharing supply, other spatial features such as surrounding POIs play a major role. In addition, MGWR yields exciting insights into the multiscale heterogeneous spatial distributions of factors influencing car-sharing behaviour. Together, our study offers insights for selecting effective and interpretable methods for diagnosing and planning the placement of car-sharing stations

Bike sharing · Car sharing · Transport engineering · Computer Science · Engineering · Human Mobility and Location-Based Analysis · Sharing Economy and Platforms · Transportation and Mobility Innovations

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