This is the implementation of our paper "Trajectory Simplification with Reinforcement Learning" (ICDE 2021).
- Linux Ubuntu OS (16.04 is tested)
- Python >= 3.5 (Anaconda3 is recommended and 3.6 is tested)
- Tensorflow (1.8.0 is tested)
Please refer to the source code to install the required packages that has not been installed in your environment such as matplotlib in Python for visualization. You can install packages with conda in a shell as
conda install matplotlib
Download & unzip the dataset Geolife and put its folder into ./TrajData. Note that the input data generated by preprocess.py will also be stored in this folder.
python preprocess.py
There are several hyperparameters in rl_brain.py, you may try to turn these parameters for a better performance when training, including units = 20, activation = tf.nn.tanh, learning_rate = 0.001 reward_decay = 0.99.
Run rl_main_pg.py, the generated models will be stored in the folder ./save automatically, and you can pick one model with the best performance on the validation data as your model from them.
python rl_main_pg.py
Here, we provide an interface RL.load(checkpoint), and you can load an intermediate model to continue the training from the checkpoint, which saves your efforts caused by some unexpected exceptions and no need to train again.
In addition, we implemented an incremental computation for reward update in rl_env_inc.py, which offers a very fast efficiency for the training, and you may refer the figure inc.png to get more details.
After your model is trained, we provide a fast interface called quick_time_action(observation), which replaces the function of DL tool and implements the NN forward more efficiently.
We implemented four mainstream error measurements of trajectory simplification in data_utils.py, including SED (sed_op, sed_error), PED (ped_op, ped_error), DAD (dad_op, dad_error), and SAD (speed_op, speed_error), where '_op' denotes the error on an anchor segment, and "_error" denotes the error between the orignal trajectory and its simplified trajectory. More details can be found in the paper. The default error measurement is SED, if you want to test more measurements, just simply replace the corresponding function name in rl_env_inc.py.
We provide an interface data_utils.draw(ori_traj, sim_traj, label='sed') to visualize the simplified trajectory vis.png, you can also use it to observe the model performance during the training or comment it in the codes for your purpose. Note that this parts is supported by matplotlib in Python 3.6.
final_error = F.draw(self.ori_traj_set[episode], sim_traj)
You can directly run the rl_evaluate.py once you obtain the trained model.
python rl_evaluate.py
Similarly, you can set a skipping step skip_size to train the RLTS-Skip model, which provides a trade-off between the effectiveness and efficiency.