A public transport system makes the promise ofa timetable to its commuters. In this report it shows whether melbourne’s metropolitan train and tram timetables is built around commute or whether a similar service all day all week is offered by them. Then it shows how fast are the services scheduled from travel and the concentrated areas accross the city where the service is.
The charts show what the system promisies not what passangers experience on the day, the data is scheduled services.
The GTFS Schedule data source from Transport Victoria Open Data (Department of Transport and Planning) is used for each and every chart. GTFS (and related data) is composed of a collection of linked text tables, which include routes, trips, stops and stop times. Feed contains a rolling 30 day timetable information. This report has been configured to use the Metropolitan Train and Metropolitan Tram parts, which were downloaded into a local folder.
Preparation steps:
A typical weekday is covered by the feed used here (Wednesday 04 November 2026), Saturday (31 October 2026) and Sunday (01 November 2026). It includes 39 train and tram routes with a meaningful weekday service.
The chart counts the trips that start in each hour of the day. The busiest weekday hour for train trips is 7:00 and for tram trips is 7:00.
How to read this chart: each line is one mode on one type of day. Hover to see exact counts, or click a legend entry to hide a line. Hours after midnight (00:00 to 03:00) are shown on the right, as they belong to the same service day
Data source: Department of Transport and Planning (n.d.).
Each point is a route. How many trips start per hour in the middle of the day (10 am to 3 pm) is shown by the horizontal axis & trips per hour in the morning peak (7 am to 9 am) is shown by vertical axis shows. In the peak points above the dashed line run more often. Across routes the median peak to midday ratio is 1.9 for trains and 1.1 for trams.
How to read this chart: point size shows the routes total weekday trips. Hover over a point to identify the route, or drag a box to zoom into a crowded area.
Data source: Department of Transport and Planning (n.d.).
Each bar shows a route’s Sunday trips as a percentage of its weekday trips. The median is 61.2% for train routes and 64.6% for tram routes.
How to read this chart: from the biggest weekend cut at the bottom to the smallest at the top are the routes sorted. The dashed line marks 100%, the same service as a weekday. For the Saturday and Sunday counts hover over a bar.
Data source: Department of Transport and Planning (n.d.).
The 10 train stations and the 10 tram stops with the most scheduled weekday departures is shown by the chart and mode is shown by the colr. The busiest stop overall is Southern Cross Railway Station with 1,001 weekday departures.
How to read this chart: Sorted from the busiest at the top, every stop is one bar. To see a bar for the exact number of departures hove over. A train station counts departures from all its platforms & tram stops with the same name are combined.
Data source: Department of Transport and Planning (n.d.).
Each point is a route. The typical straight line distance between a trip’s first and last stop is shown by the horizontal axis and the scheduled average speed over that distance is shown by the vertical axis. The median scheduled speed is 32.2 km/h for train routes and 12.6 km/h for tram routes.
How to read this chart: the route’s total weekday trips is shown by the point size. Hover over a point to identify the route. Speeds use straight line distance so they are lowr than true speed on winding routes but the routes are allowed to be compared with each other.
Data source: Department of Transport and Planning (n.d.).
Only planned timetables is contained in tge GTFS Schedule feed and not the actual running time, crowding or cancellations. Results depend on when the data were downloaded (25 October 2026) because it covers 30 day window. A typical weekday abd a Saturday and a Sunday was chosen so outside there days the planned works and public holidays is not shown. Metropolitan trains and trams are only included in. To keep the analysis focused buses and regional services are left out. Routed havinf fewer than 20 weekday trips are also excluded and routes shorter than 2 km are excluded aswell such as look routes where the straight line distance is not meaningful.
The data is cleaned and charted in R (R Core Team, 2026) using the tidyverse (Wickham et al., 2019), plotly (Sievert, 2020). I used ChatGPT (OpenAI, 2026) to understand R syntax and rest interpretation and analysis is my own.
Department of Transport and Planning. (n.d.). GTFS Schedule [Data set]. Transport Victoria Open Data. Retrieved October 25, 2026, from https://opendata.transport.vic.gov.au/dataset/gtfs-schedule
R Core Team. (2026). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/
Sievert, C. (2020). Interactive web-based data visualization with R, plotly, and ggplot2. Chapman and Hall/CRC. https://plotly-r.com
Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T. L., Miller, E., Bache, S. M., Müller, K., Ooms, J., Robinson, D., Seidel, D. P., Spinu, V., … Yutani, H. (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686