Traffic Prediction

Abstract

1. Introduction

Traffic prediction is the task of predicting future traffic measurements (e.g. volume, speed, etc.) in a road network (graph), using historical data (timeseries).

This list can be considered outdated. For a more up-to-date list, check: https://github.com/lixus7/Time-Series-Works-Conferences

A tabular summary of paper and publically available datasets. The paper is reverse chronologically sorted. NO GUARANTEE is made that this table is complete or accurate (please raise an issue if you spot any error).

2. Objective

Traffic prediction is the task of predicting future traffic measurements (e.g. volume, speed, etc.) in a road network (graph), using historical data (timeseries).

This project demonstrates how TeX, PyTorch, TensorFlow can be applied to a real-world Data Science problem.

4. Technology Stack

• LibCity GitHub
• Tsinghua Fib Lab GitHub
• PyTorch Geometric

5. System Requirements

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

• See the project README for exact requirements
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/aprbw/traffic_prediction.git
cd traffic_prediction

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 aprbw 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.