CROSS-Net: Region-Agnostic Taxi-Demand Prediction Using Feature Disentanglement
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866917434899824640 |
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| author | Ozeki, Ren Yonekura, Haruki Baimbetova, Aidana Rizk, Hamada Yamaguchi, Hirozumi |
| author_facet | Ozeki, Ren Yonekura, Haruki Baimbetova, Aidana Rizk, Hamada Yamaguchi, Hirozumi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_18215 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | CROSS-Net: Region-Agnostic Taxi-Demand Prediction Using Feature Disentanglement Ozeki, Ren Yonekura, Haruki Baimbetova, Aidana Rizk, Hamada Yamaguchi, Hirozumi Machine Learning 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. |
| title | CROSS-Net: Region-Agnostic Taxi-Demand Prediction Using Feature Disentanglement |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2310.18215 |