These are the code of AAAI 2018 paper When Will You Arrive? Estimating Travel Time Based on Deep Neural Networks, but fixed some problems by OscarHsu.
This project provides the complete version of code and part of sample data in Chengdu.
Original: https://github.com/UrbComp/DeepTTE
This program run on python2.7
conda_env_CPU.yml have the list of required libraries, include pyTorch-CPU.
conda_env_GPU.yml have the list of required libraries, include pyTorch-GPU.
python main_test_CPU.py
get the test results of DeepTTE by Pytorch-CPU version. The Deep Learning Model is prepared.
python main_test_GPU.py
get the test results of DeepTTE by Pytorch-GPU version. The Deep Learning Model is prepared.
python main_train_CPU.py
get the training model of DeepTTE by Pytorch-CPU version.
python main_train_GPU.py
get the training model of DeepTTE by Pytorch-GPU version.
In *** ./data/ ***, testRemoveBeginLast have the test data that is little bit different from original test data, test, the staying GPS points in the begin and the end of a trajectory are removed.
train_00, train_01, train_02, train_03, train_04 are 5-fold training data.
- testRemoveBeginLast_5 : test trajectories that each length is less then 5 km.
- testRemoveBeginLast_5_10 : test trajectories that each length is less then 10 km but greater then 5 km.