M2-CNN: A macro-micro model for taxi demand prediction
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...
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| Main Authors: | , |
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| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
2023
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/8543 https://ink.library.smu.edu.sg/context/sis_research/article/9546/viewcontent/taxi_demand_bigdata23__1_.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | 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. |
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