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One Model Fits All: Cross-Region Taxi-Demand Forecasting

Published: 22 December 2023 Publication History

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 generalizability to unseen areas. This paper presents a novel taxi demand forecasting system that leverages a graph neural network to capture spatial dependencies and patterns in urban environments. Additionally, the proposed system employs a region-neutral approach, enabling it to train a model that can be applied to any region, including unseen regions. To achieve this, the framework incorporates the power of Variational Autoencoder to disentangle the input features into region-specific and region-neutral components. The region-neutral features facilitate cross-region taxi demand predictions, allowing the model to generalize well across different urban areas. Experimental results demonstrate the effectiveness of the proposed system 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.

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Cited By

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  • (2024)Restoring Super-High Resolution GPS Mobility DataProceedings of the 2nd ACM SIGSPATIAL International Workshop on Geo-Privacy and Data Utility for Smart Societies10.1145/3681768.3698501(19-24)Online publication date: 29-Oct-2024
  • (2024)A Hierarchy-Aware Approach to Cross-Region Spatial-Temporal Inference of Unarchived Event in Urban Mobility InfrastructureDatabase Systems for Advanced Applications10.1007/978-981-97-5552-3_14(214-224)Online publication date: 2-Jul-2024

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cover image ACM Conferences
SIGSPATIAL '23: Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems
November 2023
686 pages
ISBN:9798400701689
DOI:10.1145/3589132
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 22 December 2023

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Author Tags

  1. region agnostic
  2. cross-region
  3. region-independent
  4. taxi demand prediction
  5. representation learning
  6. multitask learning

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Cited By

View all
  • (2024)Restoring Super-High Resolution GPS Mobility DataProceedings of the 2nd ACM SIGSPATIAL International Workshop on Geo-Privacy and Data Utility for Smart Societies10.1145/3681768.3698501(19-24)Online publication date: 29-Oct-2024
  • (2024)A Hierarchy-Aware Approach to Cross-Region Spatial-Temporal Inference of Unarchived Event in Urban Mobility InfrastructureDatabase Systems for Advanced Applications10.1007/978-981-97-5552-3_14(214-224)Online publication date: 2-Jul-2024

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