TY - GEN
T1 - UNSUPERVISED KNOWLEDGE ADAPTATION FOR PASSENGER DEMAND FORECASTING
AU - Li, Can
AU - Bai, Lei
AU - Liu, Wei
AU - Yao, Lina
AU - Waller, S. Travis
N1 - Publisher Copyright:
© 2022 Proceedings of the 26th International Conference of Hong Kong Society for Transportation Studies, HKSTS 2022. All Rights reserved.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Demand Forecasting
KW - Knowledge Adaptation
KW - Unsupervised Learning
UR - http://www.scopus.com/inward/record.url?scp=85175399154&partnerID=8YFLogxK
M3 - Conference article published in proceeding or book
AN - SCOPUS:85175399154
T3 - Proceedings of the 26th International Conference of Hong Kong Society for Transportation Studies, HKSTS 2022
SP - 500
EP - 508
BT - Proceedings of the 26th International Conference of Hong Kong Society for Transportation Studies, HKSTS 2022
A2 - Jian, Sisi
A2 - Li, Sen
A2 - Lo, Hong K.
PB - Hong Kong Society for Transportation Studies Limited
T2 - 26th International Conference of Hong Kong Society for Transportation Studies, HKSTS 2022
Y2 - 12 December 2022 through 13 December 2022
ER -