Melbourne foot traffic: the Metro Tunnel, and forecasting a week ahead
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Did the Metro Tunnel change the streets around it?
Each dot is a footpath sensor, coloured by how its weekday 7–10am count changed from to : blue busier, orange quieter, bigger dots for bigger changes. Dashed rings mark m around each new station entrance. Select a dot for details.
- busier on weekday mornings
- quieter on weekday mornings
- not enough working days to compare
- Metro Tunnel entrance
- m from an entrance
Median change per site, vs . Near: within m of an entrance. Far: m or more. Permutation p-values over site labels.
Forecast explorer
A gradient-boosting model forecasts each sensor's hourly count up to a week ahead, using only what's known a week in advance. Pick one of the busiest sensors and a week of the test window to compare it with the actual count and with the naive "same hour last week" forecast.
WAPE = total absolute error divided by total actual count. *Uses observed weather as a stand-in for a weather forecast, so it's an upper bound on what weather adds.
The model knows a public holiday is coming, but not which kind. Gap = actual vs forecast.
How it works
Metro Tunnel comparison
- Compares with the same dates a year earlier, so seasonality cancels out.
- Sites within m of a State Library or Town Hall entrance ("near") against sites m or more away ("far"). Sensors under m apart count as one site.
- A permutation test over site labels asks whether the near/far gap could be chance.
Week-ahead forecast
- Gradient boosting (scikit-learn, Poisson loss) trained on , tested on : sensors, sensor-hours.
- Features: hour, day of week, month, Victorian public holidays, and the same hour weeks earlier.
- Scored against "same hour last week" and "average of the last 4 weeks".
Messy sensor data
- The feed leaves out empty hours, so a missing hour on a working day counts as zero pedestrians.
- A day missing any hour between , or with a zero total, is a sensor outage and is dropped.
- Indoor sensors (library and community-hub doors) are left out.
Caveats: these are footpath sensors, so they measure street activity, not station ridership. There's no measurable overall Metro Tunnel effect yet, and one-off events like the AFL Grand Final break the forecast.
Files
README.mdThe full write-up, with limits and what went wrong firstmetro_tunnel.pyNear vs far comparison and permutation testforecast.pyGradient boosting, baselines and event daysfetch.pyDownloads and cleans the sensor and weather dataresults/Every table and chart behind this pagedemo/export.pyBuilds this page's data.json