Traffic Benchmark

Abstract

1. Introduction

We classify the related works based on four parts: spatial topology construction, spatial dependency modeling, temporal dependency modeling and external features, as shown in the figure below, where the baselines in the benchmark are In bold type.

2. Objective

[TKDD 2023] Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution

This project demonstrates how Python can be applied to a real-world Data Science problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/tsinghua-fib-lab/Traffic-Benchmark.git
cd Traffic-Benchmark

python train_benchmark.py --model 'model_name' --data 'data_name' >> log.txt

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by tsinghua-fib-lab and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.