| Paper ID | MLSP-22.2 |
| Paper Title |
FMA-ETA: ESTIMATING TRAVEL TIME ENTIRELY BASED ON FFN WITH ATTENTION |
| Authors |
Yiwen Sun, Yulu Wang, Tsinghua University, China; Kun Fu, Zheng Wang, DiDi AI Labs, China; Ziang Yan, Changshui Zhang, Tsinghua University, China; Jieping Ye, DiDi AI Labs; University of Michigan, Ann Arbor, China |
| Session | MLSP-22: Sequential Learning |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 15:30 - 16:15 |
| Presentation Time: | Wednesday, 09 June, 15:30 - 16:15 |
| Presentation |
Poster
|
| Topic |
Machine Learning for Signal Processing: [MLR-SLER] Sequential learning; sequential decision methods |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Estimated time of arrival (ETA) is one of the most important services in intelligent transportation systems (ITS) and becomes a challenging spatial-temporal (ST) data mining task in recent years. Nowadays, deep learning based methods, specifically recurrent neural networks (RNN) based ones are adapted to model the ST patterns from massive data for ETA and become the state-of-the-art. However, RNN is suffering from slow training and inference speed, as its structure is unfriendly to parallel computing. To solve this problem, we propose a novel, brief and effective framework mainly based on feed-forward network (FFN) for ETA, FFN with Multi-factor Attention (FMA-ETA). The novel Multi-factor Attention mechanism is proposed to deal with different category features and aggregate the information purposefully. Extensive experimental results on the real-world vehicle travel dataset show FMA-ETA is competitive with state-of-the-art methods in terms of the prediction accuracy with significantly better inference speed. |
