[2310.18215] CROSS-Net: Region-Agnostic Taxi-Demand Prediction Using Feature Disentanglement Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2310.18215 (cs) [Submitted on 27 Oct 2023 ( v1 ), last revised 7 Apr 2026 (this version, v2)] Title: CROSS-Net: Region-Agnostic Taxi-Demand Prediction Using Feature Disentanglement Authors: Ren Ozeki , Haruki Yonekura , Aidana Baimbetova , Hamada Rizk , Hirozumi Yamaguchi View a PDF of the paper titled CROSS-Net: Region-Agnostic Taxi-Demand Prediction Using Feature Disentanglement, by Ren Ozeki and 4 other authors View PDF HTML (experimental) Abstract: The growing demand for ride-hailing services has led to an increasing need for accurate taxi demand prediction. Existing systems are limited to specific regions, lacking generality to unseen areas. This paper presents a novel taxi demand prediction system, harnessing the strengths of multiview graph neural networks to capture spatial-temporal dependencies and patterns in urban environments. Additionally, the proposed system CROSS-Net employs a spatially transferable approach, enabling it to train a model that can be deployed to previously unseen regions. To achieve this, the framework incorporates the power of a Variational Autoencoder to disentangle the input features into region-specific and region-agnostic components. The region-agnostic features facilitate cross-region taxi demand predictions, allowing the model to generalize well across different urban areas. Experimental results demonstrate the effectiveness of CROSS-Net in accurately forecasting taxi demand, even in previously unobserved regions, thus showcasing its potential for optimizing taxi services and improving transportation efficiency on a broader scale. Comments: An accepted journal article on IEEE Transactions on Intelligent Transportation System Subjects: Machine Learning (cs.LG) Cite as: arXiv:2310.18215 [cs.LG] (or arXiv:2310.18215v2 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2310.18215 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1109/TITS.2026.3683763 Focus to learn more DOI(s) linking to related resources Submission history From: Haruki Yonekura [ view email ] [v1] Fri, 27 Oct 2023 15:42:04 UTC (15,646 KB) [v2] Tue, 7 Apr 2026 05:08:34 UTC (16,850 KB) Full-text links: Access Paper: View a PDF of the paper titled CROSS-Net: Region-Agnostic Taxi-Demand Prediction Using Feature Disentanglement, by Ren Ozeki and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2023-10 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from