Journal
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Volume 33, Issue 5, Pages 2096-2108Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2019.2948005
Keywords
Predictive models; Neural networks; Spatiotemporal phenomena; Data models; Correlation; Logic gates; Multi-step demands prediction; convolutional neural network; LSTM; multi-level attention
Categories
Funding
- National Key Research and Development Program of China [2018YFC0831604]
- NSFC [61772341, 61472254, 61602297]
- STSCM [18511103002]
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This paper proposes an end-to-end deep neural network model for predicting citywide passenger demands, capturing spatiotemporal influence and temporal dependencies with a multi-level attention model, achieving higher accuracy than state-of-the-art approaches.
For the emerging mobility-on-demand services, it is of great significance to predict passenger demands based on historical mobility trips towards better vehicle distribution. Prior works have focused on predicting next-step passenger demands at selected locations or hotspots. However, we argue that multi-step citywide passenger demands encapsulate both time-varying demand trends and global statuses, and hence are more beneficial to avoiding demand-service mismatching and developing effective vehicle distribution/scheduling strategies. Furthermore, we find that adaptations of single-step methods are unable to achieve robust prediction with high accuracy for further steps. In this paper, we propose an end-to-end deep neural network model to the prediction task. We employ an encoder-decoder framework based on convolutional and ConvLSTM units to identify complex features that capture spatiotemporal influence and pickup-dropoff interactions on citywide passenger demands. We introduce a multi-level attention model (global attention and temporal attention) to emphasize the effects of latent citywide mobility regularities and capture relevant temporal dependencies. We evaluate our proposed method using real-world mobility trips (taxis and bikes) and the experimental results show that our method achieves higher prediction accuracy than the state-of-the-art approaches.
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