fagan2888 / destination_prediction

Python code to re-produce results and illustrations of destination prediction by trajectory distribution based model.

Destination Prediction of Trajectory

Python code to re-produce results and illustrations of Destination Prediction by Trajectory Distribution Based Model detailed in publications [1] :

Dataset

Two datasets are used in the publication :

• Caltrain dataset is composed of 4127 trajectories from taxis which begin their trip at Caltrain station, San Francisco. It is a subset of the cabspotting data set [3].

• Sao Bento dataset is composed of 19423 trajectories from taxis which begin their trip at Sao Bento station, Porto. It is a subset of train dataset of the Kaggle ECML/PKDD 15: Taxi Trajectory Prediction (I) competition [3].

Trajectory Clustering and Classification

The different scripts to generate the two subsets described before and to produce the clustering of these trajectories, can be found in the following repository : https://github.com/bguillouet/trajectory_classification.

All the steps described in the README.md of the trajectory_classification repository has to be executed before to run the script of this repository.

Trajectory Prediction

1. utils/config.py: Set the variable DATA_CLUSTERING_DIR to the path of the data directory of the trajectory_classification repository.
2. generation_destination_prediction.py: Run the final destination prediction method described in [1]. Save the necessary data in order to produce the figure.
3. figure.py: Produce the following png file :

Compare methods

• [1] BESSE, Philippe C., GUILLOUET, Brendan, LOUBES, Jean-Michel, et al. Destination Prediction by Trajectory Distribution-Based Model. IEEE Transactions on Intelligent Transportation Systems, 2017.