This project includes two septerate tasks for trajectory classification. This first uses a standard supervised learning model/training setup to identify taxi plate numbers using a single-day's trajectory for a given driver. The second part of this project achieves the same task using a Siamese network for few-shot learning.
Each driver's day trajectory consists of a sequence of readings including:
| plate | longitute | latitude | time | status |
|---|---|---|---|---|
| 4 | 114.10437 | 22.573433 | 2016-07-02 0:08:45 | 1 |
| 1 | 114.179665 | 22.558701 | 2016-07-02 0:08:52 | 1 |
| 0 | 114.120682 | 22.543751 | 2016-07-02 0:08:51 | 0 |
| 3 | 113.93055 | 22.545834 | 2016-07-02 0:08:55 | 0 |
| 4 | 114.102051 | 22.571966 | 2016-07-02 0:09:01 | 1 |
| 0 | 114.12072 | 22.543716 | 2016-07-02 0:09:01 | 0 |
- Plate: Plate means the taxi's plate. In this project, we change them to keep anonymity. Same plate means same driver, so this is the target label for the classification.
- Longitude: The longitude of the taxi.
- Latitude: The latitude of the taxi.
- Time: Timestamp of the record.
- Status: 1 means taxi is occupied and 0 means a vacant taxi.
This portion of the project utilized a trajectory dataset with six months of driver's daily trajectories for 5 drivers. This was a substantial amount of data for each driver, enough to use standard learning models.
This portion of the project tested two model setups:
- Fully-Connected, Feed-forward DNN
- Ensemble Model, containing two LSTM branches and one feed-forwad branch. All branches are concattenated and fed into a feed-forward intepretation model.
This portion of the project utilized a trajectory dataset with five days of driver's trajectories for 500 drivers. Becuase of the dataset composition, standard learning models could not be used. Therefore, we implemented a meta-learning / few-shot learning methodology to classify taxi plate numbers.
- Fully-Connected, Feed-Forward Siamese network with weight sharing between inputs