DeepGTT

This repository holds the code used in our WWW-19 paper: Learning Travel Time Distributions with Deep Generative Model.

Requirements

• Ubuntu OS (16.04 and 18.04 are tested)
• Julia >= 1.0
• Python >= 3.6
• PyTorch >= 0.4 (both 0.4 and 1.0 are tested)

Please refer to the source code to install the required packages in both Julia and Python. You can install packages for Julia in shell as

julia -e 'using Pkg; Pkg.add("HDF5"); Pkg.add("CSV"); Pkg.add("DataFrames"); Pkg.add("Distances"); Pkg.add("StatsBase"); Pkg.add("JSON"); Pkg.add("Lazy"); Pkg.add("JLD2"); Pkg.add("ArgParse")'

Dataset

The dataset contains 1 million+ trips collected by 1,3000+ taxi cabs during 5 days. This dataset is a subset of the one we used in the paper, but it suffices to reproduce the results that are very close to what we have reported in the paper.

git clone https://github.com/boathit/deepgtt

cd deepgtt && mkdir -p data/h5path data/jldpath data/trainpath data/validpath data/testpath

Download the dataset and put the extracted *.h5 files into deepgtt/data/h5path.

Data format

Each h5 file contains n trips of the day. For each trip, it has three fields lon (longitude), lat (latitude), tms (timestamp). You can read the h5 file using the readtripsh5 function in Julia. If you want to use your own data, you can also refer to readtripsh5 to dump your trajectories into the required hdf5 files.

Preprocessing

Map matching

First, setting up the map server and matching server by referring to barefoot.

Then, matching the trips

cd deepgtt/harbin/julia

julia -p 6 mapmatch.jl --inputpath ../data/h5path --outputpath ../data/jldpath

where 6 is the number of cpu cores available in your machine.

Generate training, validation and test data

julia gentraindata.jl --inputpath ../data/jldpath --outputpath ../data/trainpath

cd .. && mv data/trainpath/150106.h5 data/validpath && mv data/trainpath/150107.h5 data/testpath

Training

To run the python code, make sure you have set up the road network postgresql server by referring to the map server setup in barefoot. The road network server (see this file) is used to provide road segment features for the model.

cd deepgtt/harbin/python

python train.py -trainpath ../data/trainpath -validpath ../data/validpath -kl_decay 0.0 -use_selu -random_emit

Testing

python estimate.py -testpath ../data/testpath

Reference

@inproceedings{www19xc,
  author    = {Xiucheng Li and
               Gao Cong and
               Aixin Sun and
               Yun Cheng},
  title     = {Learning Travel Time Distributions with Deep Generative Model},
  booktitle = {Proceedings of the 2019 World Wide Web Conference on World Wide Web,
               {WWW} 2019, San Francisco, California, May 13-17, 2019},
  year      = {2019},
}