TraSculptor

This is the code for our paper Towards Better Decision-Making for Road Traffic Planning

TraSculptor is an interactive planning decision-making system designed to enhance the modification and comparison of road networks. It addresses two main challenges:

• Interactive Modification: TraSculptor allows experts to easily and directly modify road networks on a map through flexible interactions.
• Intuitive Comparison: It features a comparison view that includes a history tree of multiple states and a road-state matrix, facilitating intuitive comparisons of different road network states.

Installation

You need install Node.js and Python.

Python dependency Library

They're all in the requirement.txt.

flask==3.0.0
flask_cors==4.0.0
numpy==1.25.1
tqdm

Install all dependency

pip install -r requirements.txt

Data

Source Data

case_study_dataset.zip
SiouxFalls_dataset.zip
EasternMassachusetts_dataset.zip

The files can be found in the releases of the repository. Among them, case_study_dataset.zip is the data used for the case study in the paper, SiouxFalls_dataset.zip is the data used for the user study in the paper, and EasternMassachusetts_dataset.zip is the data used for larger-scale network testing.

File Path

The datasets has been included in the repository. If you find that the dataset is missing, please download the compressed package of the dataset (case_study_dataset.zip, SiouxFalls_dataset.zip and EasternMassachusetts_dataset.zip) from the release, unzip them, and then move all the folders inside to /Backend/data/.

How To Run this Project

Frontend -- Vue

Recommended IDE Setup

VSCode + Volar (and disable Vetur) + TypeScript Vue Plugin (Volar).

Customize configuration

See Vite Configuration Reference.

Enter the Folder

cd Frontend

Project Setup

npm install

Compile and Hot-Reload for Development

npm run dev

Compile and Minify for Production

npm run build

Backend -- Flask

Recommended IDE Setup

VSCode

Enter the Folder

cd Backend

Compile and Run for Development

• For the small-scale case study dataset:

python app.py --dataset mini_data

• For the Sioux Falls dataset used in the user study:

python app.py --dataset data

• For the larger-scale Eastern Massachusetts dataset:

python app.py --dataset data_EasternMassachusetts

Contact

We are glad to hear from you. If you have any questions, please feel free to contact zikun.rain@gmail.com or open issues on this repository.