Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

12-2023

Abstract

In this paper, we introduce a macro-micro model for predicting taxi demands. Our model is a composite deep learning model that integrates multiple views. Our network design specifically incorporates the spatial and temporal dependency of taxi or ride-hailing demand, unlike previous papers that also utilize deep learning models. In addition, we propose a hybrid of Long Short-Term Memory Networks and Temporal Convolutional Networks that incorporates real world time series with long sequences. Finally, we introduce a microscopic component that attempts to extract insights revealed by roaming vacant taxis. In our study, we demonstrate that our approach is competitive against a large array of approaches from the literature on the basis of detailed moving logs of more than 20,000 taxis and 12 million trips per month over a three-month period. Our analysis of the effectiveness of individual components reveals that microscopic information is essential for generating high-quality predictions.

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems

Research Areas

Intelligent Systems and Optimization

Publication

2023 IEEE International Conference on Big Data: Sorrento, Italy, December 15-18: Proceedings

First Page

1395

Last Page

1402

ISBN

9798350324457

Publisher

IEEE

City or Country

Piscataway, NJ

Additional URL

https://doi.org/10.1109/BigData59044.2023.10386527

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