{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T11:34:49Z","timestamp":1775561689556,"version":"3.50.1"},"reference-count":65,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T00:00:00Z","timestamp":1744156800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Fund of Beijing, China","award":["4212026"],"award-info":[{"award-number":["4212026"]}]},{"name":"Natural Science Fund of Beijing, China","award":["2021-JCJQ-JJ-0059"],"award-info":[{"award-number":["2021-JCJQ-JJ-0059"]}]},{"name":"Fundamental Strengthening Program Technology Field Fund, China","award":["4212026"],"award-info":[{"award-number":["4212026"]}]},{"name":"Fundamental Strengthening Program Technology Field Fund, China","award":["2021-JCJQ-JJ-0059"],"award-info":[{"award-number":["2021-JCJQ-JJ-0059"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Efficient resource allocation in car-sharing systems relies on precise predictions of demand. Predicting vehicle demand is challenging due to the interconnections of temporal, spatial, and spatio-temporal features. This paper presents the Explainable Spatio-Temporal Inference Network (eX-STIN), a new approach that improves upon our prior Unified Spatio-Temporal Inference Prediction Network (USTIN) model. It offers a comprehensive framework for the integration of various data. The eX-STIN model enhances the previous one by utilizing Ensemble Empirical Mode Decomposition (EEMD), which results in refined feature extraction. It uses Minimum Redundancy Maximum Relevance (mRMR) to find features that are relevant and not redundant, and Shapley Additive Explanations (SHAP) to show how each feature affects the model\u2019s predictions. We conducted extensive experiments that use real car-sharing data to thoroughly evaluate the efficacy of the eX-STIN model. The studies revealed the model\u2019s ability to accurately represent the relationships among temporal, spatial, and spatio-temporal features, outperforming the state-of-the-art models. Moreover, the experiments revealed that eX-STIN exhibits enhanced predictive accuracy compared to the USTIN model. This proposed approach enhances both the accuracy of demand prediction and the transparency of resource allocation decisions in car-sharing services.<\/jats:p>","DOI":"10.3390\/ijgi14040163","type":"journal-article","created":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T12:05:56Z","timestamp":1744200356000},"page":"163","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Explainable Spatio-Temporal Inference Network for Car-Sharing Demand Prediction"],"prefix":"10.3390","volume":"14","author":[{"given":"Nihad","family":"Brahimi","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0137-4069","authenticated-orcid":false,"given":"Huaping","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7171-003X","authenticated-orcid":false,"given":"Zahid","family":"Razzaq","sequence":"additional","affiliation":[{"name":"Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genoa, 16126 Genova, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103923","DOI":"10.1016\/j.trc.2022.103923","article-title":"DROP: Deep relocating option policy for optimal ride-hailing vehicle repositioning","volume":"145","author":"Qian","year":"2022","journal-title":"Transp. 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