Building a Traffic Volume Predictor
Master Time-Series Data and Deep Learning for Traffic Prediction
Created by DataLab, Bernd Schrooten
Explore how deep learning can help predict traffic volume by working with real-world data from a Minnesota highway. You'll build a neural network from scratch, learning how to handle time-series data and uncover patterns that can improve traffic management and planning.
DataLab | Mar 2025 | 47 min
What You Will Learn
You'll start by preparing and exploring actual traffic datasets, then move on to building and training an LSTM neural network using PyTorch. Each step is hands-on, guiding you through data preprocessing, model development, and evaluation so you can confidently apply deep learning to time-series data.
Key Features
- Build and fine-tune LSTM models to forecast traffic volume accurately
- Use PyTorch to preprocess data, train models, and evaluate predictions
- Apply deep learning to real-world time-series forecasting challenges
Target Audience
Perfect for data scientists, analysts, or anyone with a basic understanding of Python and machine learning who wants to tackle real-world prediction problems. No deep learning or time-series experience is needed-just curiosity and a desire to learn practical AI skills for transportation and planning.
