/DeepTTE

The code is owned by the authors of the AAAI 2018 paper When Will You Arrive? Estimating Travel Time Based on Deep Neural Networks. Changes were made to the code, after which Python 3.10 and the most recent dependencies' versions can support it.

Primary LanguagePython

DeepTTE

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 NadaBadawi.

This project provides the complete version of code and part of sample data in Chengdu.

Original: https://github.com/UrbComp/DeepTTE and Updated Version: https://github.com/oscarhsu/DeepTTE

Environment :

This program run on python3.10

Usage:

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.

Data

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.