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[AAAI 2023] GRLSTM: Trajectory Similarity Computation with Graph-based Residual LSTM

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# [AAAI 2023] GRLSTM: Trajectory Similarity Computation with Graph-based Residual LSTM

This is a PyTorch implementation of GRLSTM.

Author list: Silin Zhou, Jing Li, Hao Wang, Shuo Shang, and Peng Han.

## Framework
<div align=center>
<img src="framework.png"/>
</div>

## Experiment
<div align=center>
<img src="exp.png"/>
</div>

## If you want to reproduce the results, please follow the steps below

#### Attention
1. If you want to use other dataset, please **do not** apply map matching on trajectories. Only need to do is align the coordinate with road segment or intersection.
2. How to obtain trajectory similarity labels, please refer to [Trajectory Similarity Join in Spatial Networks](https://dl.acm.org/doi/10.14778/3137628.3137630)

###

### construct knowledge graph
```shell
cd utils
python construct_KG.py
```

### move, rename, create files
1. move file ***bj(or ny)_e_map.txt*** into ***KGE/datasets/beijing(or newyork)/kg*** and rename ***e_map.txt***

2. move file ***bj(or ny)_KG_graph.txt*** into ***KGE/datasets/beijing(or newyork)/kg*** and rename ***train.txt***

3. create a empty file and name ***test.txt***

4. create a file named ***r_map.txt*** and copy the follow text
```shell
road 0
traj_in 1
traj_not_in 2
```

### train knowledge graph embedding
```shell
cd KGE
python run_knowledge_representation.py
```

If you want to change hyper-parameters, please adjust them in ***KGE/jTransUP/models/base.py***.

The output file of KGE is in ***KGE/log***.

### construct fusion graph
```shell
cd utils
python construct_fusion_graph.py
```

## Train
```shell
python Train.py
```

## Validation
```shell
python Validation.py
```

## Test
```shell
python Test.py
```

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