Unsupervised Knowledge Adaptation for Passenger Demand Forecasting
Research output: Contribution to book/Conference proceedings/Anthology/Report › Conference contribution › Contributed › peer-review
Contributors
Abstract
Considering the multimodal nature of transport systems and cross-modal correlations, there is a growing trend of enhancing demand forecasting accuracy by optimization with multiple transport modes data jointly, which can improve accuracy but be less practical when different parts of multimodal datasets are owned by different institutions who cannot directly share dat. This study proposes an Unsupervised Knowledge Adaptation Demand Forecasting framework, which forecasts the demand of one mode (i.e., the target mode) by utilizing a pretrained model based on data of another mode, but does not require direct data sharing of another transport mode (i.e., the source mode). The unsupervised knowledge adaptation strategy is utilized to form the sharing features for forecasting by making the pre-trained network and the sharing extraction network analogous. Our findings illustrate that unsupervised knowledge adaptation by sharing pre-trained models to the target transport mode can improve the forecasting performance instead of direct data sharing.
Details
Original language | English |
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Title of host publication | Proceedings of the 26th International Conference of Hong Kong Society for Transportation Studies, HKSTS 2022 |
Editors | Sisi Jian, Sen Li, Hong K. Lo |
Publisher | Hong Kong Society for Transportation Studies Limited |
Pages | 500-508 |
Number of pages | 9 |
ISBN (electronic) | 9789881581402 |
Publication status | Published - 2022 |
Peer-reviewed | Yes |
Publication series
Series | International Conference of Hong Kong Society for Transportation Studies (HKSTS) |
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Conference
Title | 26th International Conference of Hong Kong Society for Transportation Studies, HKSTS 2022 |
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Duration | 12 - 13 December 2022 |
City | Hong Kong |
Country | Hong Kong |
External IDs
ORCID | /0000-0002-2939-2090/work/161887591 |
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Keywords
ASJC Scopus subject areas
Keywords
- Demand Forecasting, Knowledge Adaptation, Unsupervised Learning