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Self-Attention ConvLSTM for Spatiotemporal Forecasting of Short-Term Online Car-Hailing Demand
| Content Provider | MDPI |
|---|---|
| Author | Ge, Hongxia Li, Siteng Cheng, Rongjun Chen, Zhenlei |
| Copyright Year | 2022 |
| Description | As a flourishing basic transportation service in recent years, online car-hailing has made great achievements in metropolitan cities. Accurate spatiotemporal forecasting plays a significant role in the deployment of a network for online car-hailing demand services. A self-attention mechanism in convolutional long short-term memory (ConvLSTM) is proposed to accurately predict the online car-hailing demand. It can more effectively address the disadvantage that ConvLSTM is not good at capturing spatial correlation over a large spatial extent. Furthermore, it can generate features by aggregating pair-wise similarity scores of features at all positions of input and memory, and thus obtain the function of long-range spatiotemporal dependencies. First, the online car-hailing trajectories dataset was converted into images after geographic grid matching, and image enhancement was performed by cropping. Then, the effectiveness of the ConvLSTM embedded with a self-attention mechanism (SA-ConvLSTM) was demonstrated by comparing it to existing models. The experimental results showed that the proposed model performed better than the existing models, and including spatiotemporal information in images would perform better predictions than including spatial information in time-series pixels. |
| Starting Page | 7371 |
| e-ISSN | 20711050 |
| DOI | 10.3390/su14127371 |
| Journal | Sustainability |
| Issue Number | 12 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-06-16 |
| Access Restriction | Open |
| Subject Keyword | Sustainability Transportation Science and Technology Online Car-hailing Demand Spatiotemporal Forecasting Convlstm Attention Mechanism Deep Learning |
| Content Type | Text |
| Resource Type | Article |
