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Smart city article details
| Title | Deep Learning For Destination Choice Modeling: A Fundamental Approach For National Level People Flow Reconstruction |
|---|---|
| ID_Doc | 17870 |
| Authors | Pang Y.; Sekimoto Y. |
| Year | 2022 |
| Published | Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022 |
| DOI | http://dx.doi.org/10.1109/BigData55660.2022.10020165 |
| Abstract | With the rapid trend of developing Smart Cities and Digital Twins, a better understanding of how humans move and perform a daily routine in the city area is vital. Benefiting from the recent rapidly growing location acquisition techniques, existing deep learning approaches can effectively model and predict human mobility with human mobility big data. However, it is still challenging to simulate human mobility at the population level because only the individuals with plentiful historical data can be well modeled. Moreover, the differences in complex city layouts and functions prevent us from applying the trained models to different cities. Therefore, in this study, we propose an alternative deep learning framework focusing on the destination choice to reconstruct nationwide human mobility at the population level. We design a new embedding mechanism for handling the traveler's demographics, mobility characteristics, travel intention, and context information of locations to enrich the representations for the model prediction. Then a neural network is trained with the People Flow Dataset. We evaluated our approach based on multiple urban areas using different training data and demonstrated the advantages of our method compared with other baseline approaches. © 2022 IEEE. |
| Author Keywords | Deep learning; Destination choice; People Flow Reconstruction |
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