Abstract
Accurate and reliable short- term passenger flow prediction can support operations and decision-making of the URT system from multiple perspectives. In this paper, we propose a URT multi- step short- term passenger flow prediction model at the network level based on a Transformer-based LSTM network, Depth-wise Attention Block, and CNN network, named as Spatial- Temporal Integrated Prediction Model (STIPM). The STIPM comprises three branches. The first branch takes time- series inflow data as input, and a Transformer-based LSTM network is selected to extract the temporal correlations. The second one takes timestep- based OD data as input, and many spatial and temporal features are captured using Depth- wise Attention Blocks. Meanwhile, timestep- based OD data can better include inter- station relations and global information. The third branch takes Point of Interest data (POI) as input and CNN network is utilized for spatiotemporal features extraction, which can also become the bridge between spatial and temporal features. Moreover, the“Multi-inputmulti- output Strategy”for multi- step prediction is used to obtain a longer prediction period and more detailed information under a relatively high forecasting accuracy. The STIPM is applied to two large- scale real- world datasets from the URT system, and the obtained prediction results are compared with ten baselines and four variants from itself, in which STIPM model achieves highest prediction accuracy indicated by RMSE, MAE, and WMAPE evaluations, which demonstrates the superiority and robustness of the STIPM.
Translated title of the contribution | Attention-based Multi-step Short-term Passenger Flow Spatial-temporal Integrated Prediction Model in URT Systems |
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Original language | Chinese |
Pages (from-to) | 698-713 |
Number of pages | 16 |
Journal | Journal of Geo-Information Science |
Volume | 25 |
Issue number | 4 |
DOIs | |
Publication status | Published - 2023 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Keywords
- deep learning
- features fusion
- multi-step forecasting
- short-term passenger forecasting
- spatiotemporal features mining
- traffic big-data
- urban Rail Transit