This folder (i.e., ./CNN, /CNNTCN) holds the source code of our methods.
- Pytorch 1.9.0
- Python version: 3.9
- GPU Server: GeForce RTX 3090, 2.40GHz GPU, and 24-GB RAM
- Please refer to the source code to install all required packages of libs.
- "./Geolife, ./CNN/Image, ./CNNTCN/Geolife" contains original trajectory data (data1), mapped trajectory data, trajectory data with seasonal data (data2) or partitioning(data1), and boundary information of 3 partitions (bjDistrict).
- Note, Geolife dataset is consisted of seven kinds of mode datasets.
- The format of the datasets are respectively: Trajectory data: moving object's sampling points consisted of lattitude, longitude, timestamp. Mapped trajectory data: grid images (50*50) of trajectory data. Boundary information: points on the boundaries consisted of lattitude and longetude.
- Remove redundant information (e.g. altitude) of sampling points in GeolifeDataset 1.3;
- Generate seven mode datasets and compose them as a new Geolife './Geolife';
- Get mapped trajectory by './CNN/trajectory_mapping.py';
- Train the CNN model by './CNN/main.py' and save the parameters ('./CNN/res50.py' explains how Resnet50 works)
- Load the train set and test set from seven mode datasets and Geolife;
- Run './CNNTCN/geolife_test.py' without cnnTest(), test(), test1() to train TCN model ('./CNNTCN/tcn.py' and './CNNTCN/model.py' explain how TCN works);
- Run './CNNTCN/geolife_test.py' without train(epoch) to test CNN-TCN model and evaluate our methods;