Traffic volume forecasting for a city corridor

The brief

A city traffic cell wants to time signals and deploy staff based on expected traffic. Forecast hourly traffic volume on a corridor using time of day, weather and holidays, and compare with a bike-sharing demand dataset. Good looks like: a forecast that beats the 'same hour last week' baseline, and clear charts of rush-hour and weather effects.

What you will build

1. Explore traffic patterns by hour, day and weather
2. Build a seasonal baseline
3. Train a gradient-boosting forecast
4. Compare errors and test on unseen months
5. Show the effects in a short dashboard

Free, open data to build it with

• Metro Interstate Traffic Volume ↗ UCI Machine Learning Repository
• Seoul Bike Sharing Demand ↗ UCI Machine Learning Repository

Check each dataset's licence before you publish your work. Your mentor gives your team its own version of the brief (data, scope or region), so every team's project is its own.