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Image-based NN model for travel time estimation for paths in road networks

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# DeepIST

*DeepIST* aims to predict the travel time of a given path (i.e., a sequence of road segments) in a road network. 
Please refer the paper [here](https://arxiv.org/pdf/1909.05637.pdf).

## Prepare Data

If you don't want to parse data from scratch by yourself, you can skip this step and use our released data.

### 1. Map matching on trajectories

parse_[city].py <raw_fname> <output_traj_fname>
 # for other cities: porto (well be released soon)

In this work, we apply [barefoot](https://github.com/bmwcarit/barefoot) for map matching based on [open street map(OSM)](https://www.openstreetmap.org/) data.

Some downloadable OSM data: 
[portugal](https://download.geofabrik.de/europe/portugal-latest.osm.pbf) 
[major cities](https://download.bbbike.org/osm/bbbike/)

Scripts we implemented for barefoot will be released soon.

### 2. Parse map matching results as paths and filter incorrect matched results

python tools/path/traj_to_path.py <traj_file> <matched_folder> <output_path_file>
 python tools/path/filter_paths.py <traj_file> <path_file> <output_path_file>

### 3. Plot paths as images

#### 3.1. Prepare hourly moving speed on road segments in average based on path data

python tools/plot/get_road_avg_speed.py <path_file> <output_speed_file>
 
#### 3.2. Plot paths as images

mkdir <output_image_folder>
 python tools/plot.py <path_file> <speed_file> <osm.pbf_file> <output_image_folder> <output_training_file>
 
## Released data

### Porto

* raw trajectory data [here](https://psu.box.com/s/mtktjfj1pv4sb4xls2qg6pkpv4turoru)
* trajectory data [here](https://psu.box.com/s/8f8qi7pl8o40cg5m8aurbq072tvpyqp6)
* path data [here](https://psu.box.com/s/zbu3jsg921fqoqqz68xuja93l19x88nb)
* speed data [here](https://psu.box.com/s/15hff7flpal3qu0nakmemw3udacafb4i)
* osm data [here](https://download.geofabrik.de/europe/portugal-latest.osm.pbf)
* images [here](https://psu.box.com/s/r6h9b267k7ceygnxdn4tbr0qsf2l3ff0)
* training file [here](https://psu.box.com/s/7oitglo76p2x7zd8czlj7d0zs1j2tuih)

### Chengdu

* Be released soon

## How to Use?

First, to configurations of experiments in config.py 
Then, to run *DeepIST* experiments, execute the following command:

python main.py <training_file>

## Citing

If you find *DeepIST* useful for your research, please cite the following paper:

@inproceedings{fu2019deepist,
        title={DeepIST: Deep Image-based Spatio-Temporal Network for Travel Time Estimation},
        author={Fu, Tao-yang and Lee, Wang-Chien},
        booktitle={Proceedings of the 28th ACM International Conference on Information and Knowledge Management},
        pages={69--78},
        year={2019},
        organization={ACM}
    }

### Miscellaneous

Please send any questions you might have about the code and/or the algorithm to <txf225@cse.psu.edu> or <csiegoat@gmail.com>.

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