Casey’s road network carries commuters, local traffic and commercial vehicles across Melbourne’s growing south-east. Yet the busiest surveyed point is not the point with the strongest estimated commercial-vehicle flow. Using the latest available observation at each approximate survey location, this story follows that mismatch and asks where total traffic, commercial flow and speed overlap.
Start with the geography. Larger circles represent higher 24-hour traffic volumes, while colour represents the commercial-vehicle share. The map shows that high traffic volume and high commercial share do not always occur at the same places. Hover to inspect the latest survey details at each location.
Circle size = 24-hour traffic volume. Colour = commercial-vehicle share. Each point uses the latest available survey at that approximate location.
The contrast becomes clearer when total traffic is separated from commercial-vehicle share. Evans Road records the highest 24-hour traffic volume in the latest-location view, while the strongest estimated commercial flow appears elsewhere. Bubble size adds peak-period intensity and colour adds 85th-percentile speed.
Dotted lines show the median traffic volume and median commercial-vehicle share among the latest survey observations.
Commercial-vehicle share alone can make a quiet road look commercially intensive. Multiplying that share by total 24-hour volume reveals a different ranking: Harkaway Road reaches approximately 4,889 estimated commercial vehicles per day in its latest survey observation. This is a derived estimate, not a separate classified-vehicle count.
Estimated commercial flow = 24-hour traffic volume × commercial-vehicle percentage. Survey points are ranked individually; values are not summed along a road.
Commercial flow alone is not the whole story. Survey points with similar commercial flow can sit in very different speed environments. Bubble size represents total traffic and colour represents commercial-vehicle share; the dotted guides mark the top-quartile thresholds later used in the final map.
The final view avoids an opaque weighted score. A survey point receives one flag for being in the top quartile of total traffic, estimated commercial flow and 85th-percentile speed. The map opens with the 249 two-pressure and 229 three-pressure locations emphasised; use the layer control to reveal or hide the lower-pressure background.
Top-quartile thresholds are calculated from the latest available observation at each approximate survey location: about 3,296 vehicles/day, 172 estimated commercial vehicles/day, and 55.5 km/h. This is a descriptive prioritisation device, not a safety-risk model.
Takeaway. Casey’s road pressure cannot be read from traffic volume alone. The strongest estimated commercial-flow point is Harkaway Road, while the highest-volume surveyed point is Evans Road. The final map shows where traffic, commercial flow and speed overlap rather than treating any one measure as the whole story.
The visualisations use the City of Casey Traffic Volume Survey open dataset. After filtering incomplete or implausible records, the analysis retains the latest available survey at each approximate road-and-coordinate location so that repeated historical observations are not treated as simultaneous measurements.
Unit of analysis: latest available survey
observation at an approximate location.
Interactive
encodings: hover/tap details, zoom/pan, continuous colour
scales, bubble size, and layer toggling.
Derived
measure: estimated commercial flow.
Commercial flow is estimated as:
\[ \text{Estimated commercial flow} = \text{24-hour traffic volume} \times \frac{\text{commercial vehicle percentage}}{100} \]
This derived measure should be interpreted as an approximation based on the published percentage, not as a separately observed commercial-vehicle count.
The final multi-pressure classification is deliberately simple and transparent. It flags whether each survey point falls in the top quartile for total traffic, estimated commercial flow and 85th-percentile speed. It does not claim that those locations are unsafe or that the three measures have equal causal importance.
Survey observations were collected in different years and the spatial coverage is not identical from year to year. The visualisations therefore do not interpret the dataset as a continuous time series. Using the latest available observation at each approximate location creates a useful cross-sectional view, but those “latest” observations can still come from different calendar years.
Commercial-vehicle flow is estimated from total volume and the published commercial-vehicle percentage. It should not be interpreted as a direct classified vehicle count unless the source documentation confirms that interpretation.
I used ChatGPT (OpenAI, 2026) to assist with story development, R code generation and debugging, interactive visualisation design, and editing of explanatory text. I reviewed the generated code and visual outputs and take responsibility for the final submission.
City of Casey. (2026). Traffic Volume Survey [Data set]. City of Casey Open Data Exchange. Retrieved October 28, 2026, from https://data.casey.vic.gov.au/explore/dataset/traffic-volume-survey-copy/
OpenAI. (2026). ChatGPT [Generative AI tool]. https://chatgpt.com/