Melbourne SCATS Traffic Analysis 2014–2026: Congestion, Volume, Interactive Maps and OOH Billboard Intelligence

Council Traffic Intelligence Reports

This council layer is designed for fast public understanding. Residents, councillors, MPs, council candidates, journalists, community groups and transport observers can start with a familiar boundary — the local council — and then inspect the traffic evidence for that area.

The reports are not intended to replace council systems. They provide a public evidence layer built from open transport data, allowing traffic pressure, busy sites, freight signals and council-level patterns to be inspected from one public entry point.

Executive Summary

This is no longer simply a statistics page. It is a public analytical infrastructure layer for journalists, OOH media, freight/logistics observers, researchers, government readers, developers and the general public. The platform combines historical SCATS intelligence with an expanding TIRTL pathway for freeway speed, vehicle class, heavy-vehicle and corridor-performance analysis.

Unlike conventional dashboard-style summaries, this platform exposes the analytical process itself: structural diagnostics, database inventory, month-coverage auditing, duplicate-key evidence, interval-quality checks, processing-time transparency, downloadable CSV/JSON outputs and reproducible scripts.

The strongest current capability is historical SCATS intelligence: yearly totals, daily and monthly trends, time-of-day behaviour, weekday/weekend patterns, day-of-week and seasonal profiles, busiest-site rankings, site-month intelligence, OOH exposure analysis and interactive mapping. The next major expansion path is deeper SCATS + TIRTL integration for freight, freeway speed, vehicle class and corridor-performance intelligence.

Headline Metrics

Latest DTP Open Data Intelligence Briefs

This section tracks new Department of Transport and Planning SCATS and TIRTL open-data releases as they become available. The purpose is simple: turn raw open-data releases into rapid, readable traffic and freight intelligence.

When new SCATS or TIRTL data is released, the update can be processed into headline movement totals, busiest SCATS signal locations, strongest SCATS movement increases, highest-volume TIRTL truck locations, truck-share hotspot tables, mapped report locations, anomaly and watchlist candidates, publication caveats, method notes and supporting CSV evidence.

The monthly brief archive is designed to grow as new DTP SCATS and TIRTL releases are processed. The stable latest links can be updated each month while the dated release folders preserve the historical archive.

Why This Matters to Melbourne

This work gives Melbourne a rare independent view of its signalised traffic network across more than a decade. It helps explain congestion, recovery, behavioural change, commuter rhythm, seasonal pressure, commercial exposure, freight context and public infrastructure questions in a form journalists, executives and residents can inspect.

The public value is not just the charts. It is the combination of cleaned data, reproducible compute, interactive maps, visual storytelling, OOH/parcel intelligence and a question-to-answer index that turns raw traffic counts into a practical city movement intelligence platform.

Yearly Totals Intelligence — Melbourne's Macro Traffic History

The yearly totals are one of the most important public-facing parts of the platform because they compress the entire 15-minute SCATS processing pipeline into a simple historical story. Viewers can immediately see Melbourne's traffic growth, the 2020 shock, and the post-COVID recovery into a new record year.

1. Melbourne Total Vehicle Movements by Year

The flagship yearly chart. It shows the long-term traffic story: growth, COVID-period disruption, recovery, and 2025 emerging as the strongest full year.

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2. Average Daily Vehicle Movements by Year

Average daily movements make full-year comparisons easier and show the scale of the 2025 record year more clearly.

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3. COVID Low vs Record Year

A direct comparison between the 2020 low and 2025 record year. This is one of the clearest media-facing recovery visuals.

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4. Year-over-Year Change

Full-year-only percentage change. Partial 2026 is excluded to avoid implying a false traffic collapse.

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5. Traffic Growth Index

Indexed to 2014 = 100, this chart shows long-term structural growth while excluding partial 2026.

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6. Cumulative Vehicle Movements

The scale-shock chart: cumulative cleaned vehicle movements now exceed 539 billion across the available dataset.

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7. Months Loaded Per Year

A coverage and transparency check showing full years, valid loaded months, the 2018 unavailable monthly ingest source, and partial 2026.

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Why this section matters

This section gives the platform an executive-summary layer. The daily, monthly, site-level and 15-minute charts prove technical depth; the yearly totals explain the historical story in seconds. For journalists, government readers, OOH media, freight/logistics observers and the public, these yearly charts are likely to be among the most understandable and most frequently reused outputs on the page.

Expected chart directory: charts/yearly_totals_v5/

Primary files: yearly_total_vehicle_movements_v5.png , yearly_average_daily_movements_v5.png , yearly_yoy_percentage_change_v5.png , yearly_index_growth_comparable_years_v5.png , yearly_cumulative_vehicle_movements_v5.png , yearly_covid_low_vs_record_year_v5.png , yearly_valid_months_loaded_v5.png .

Confirmed Monthly Totals Result

This monthly result is now the backbone trend layer for the SCATS project. It gives the page a completed month-by-month time series that can feed the first major public charts: long-term monthly traffic, yearly totals, seasonal movement, pandemic-era disruption, post-pandemic recovery, and recent growth pressure.

Generated Monthly and Yearly Traffic Charts

Monthly Total Traffic

Long-term monthly cleaned traffic movement trend across the full SCATS archive.

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Monthly Traffic with Rolling Average

Smoothed trend view designed to make long-term growth, disruptions, and recovery easier to see.

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Yearly Total Traffic

Annual cleaned traffic movement totals derived from the completed monthly output.

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Yearly Growth Rate

Year-on-year percentage movement, useful for showing the COVID disruption and post-pandemic rebound.

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COVID Disruption and Recovery

Dedicated monthly chart showing the pandemic-era collapse and the later recovery pattern.

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Average Month / Seasonality

Seasonality view showing which calendar months tend to carry stronger or weaker traffic demand.

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Network monthly volume rebuilt from site-month totals

This V5.2 chart confirms the full network monthly trend from individual SCATS site-month rows, excluding the final partial month to avoid a misleading end-of-series drop.

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Network seasonality heatmap from site-month totals

Shows monthly network intensity by year. Neutral grey indicates a known missing or zero-row month rather than measured low traffic.

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Top 10 Months by Total Cleaned Volume

Lowest 10 Months by Total Cleaned Volume

Yearly Totals Derived from Monthly Output

Confirmed Daily Totals Result

Daily totals are one of the most public-facing layers in the entire SCATS project. They allow the archive to move from technical database scale into clear city-history questions: Melbourne’s busiest traffic days, quietest disruption days, COVID-era collapse, holiday patterns, weekday/weekend behaviour, recovery curves, and the largest one-day shocks in the road network.

Generated Daily Traffic Charts

Daily Traffic Over Time

A full date-by-date line chart showing the complete daily movement spine from 2014 to 2026.

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Daily Traffic with 7-Day Rolling Average

A smoothed public-facing trend chart that makes weekly cycles, lockdown disruption, and recovery easier to read.

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Daily Traffic with 30-Day Rolling Average

A stronger smoothing layer that reveals long-term disruption, recovery, and seasonal traffic movement.

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Top 50 Busiest Days

A ranked bar chart for media use, showing Melbourne’s highest-volume traffic days in the SCATS archive.

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Bottom 50 Quietest Days

A ranked disruption chart for lockdowns, holidays, missing coverage checks, and abnormal network-wide collapses.

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Daily Traffic Distribution by Year

Boxplot view showing how the normal daily traffic range changes year by year, without over-emphasising outliers.

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Largest Single-Day Traffic Changes

Highlights the biggest day-to-day rises and falls, including the 2025-05-27 anomaly that needs separate validation.

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Top 10 Busiest Days by Total Cleaned Volume

Lowest 10 Non-Zero Days by Total Cleaned Volume

Average Daily Volume by Day of Week

Confirmed Busiest Day Result

This is a simple, public-facing headline result: across more than twelve years of cleaned SCATS observations, the highest single-day network total currently observed was Friday 12 December 2025. The top-ranked days are strongly concentrated on Fridays, especially in late November and December, which points toward end-of-year commuter, retail, logistics, and holiday-movement pressure.

Generated Busiest-Day and Weekday Charts

Top 10 Busiest Days

Ranked daily chart showing the highest recorded traffic days in the archive.

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Busiest Day by Year

Year-by-year view showing the strongest daily traffic result for each year.

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Average Daily Volume by Weekday

Weekday profile showing how traffic demand varies across the week.

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Top 10 Busiest Days by Total Cleaned Volume

Busiest Day by Year

Confirmed Busiest Site Result

This is the first confirmed location-level headline result produced by the chunked site-ranking workflow. It matters because it turns the project from a purely network-wide scale story into a place-specific intelligence story. Readers can now point to a named SCATS site and say: this location carried more total cleaned traffic than any other currently loaded site in the archive.

Generated Busiest-Site Charts

Top 20 Busiest SCATS Sites

Ranked location chart showing the strongest loaded SCATS sites by total cleaned movements.

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Busiest Site Monthly Trend

Monthly trend for the leading site, showing how the busiest site behaved across the archive.

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Confirmed Busiest Time Bin Result

This result is especially useful for journalists because it gives the archive a simple, human-readable answer to the question: when is Melbourne's signalized road network busiest? The answer from this completed run is not a vague “PM peak”; it is a precise 15-minute bin: 5:15pm to 5:30pm.

Generated Time-of-Day Charts

Average Daily Traffic by Time of Day

Full 24-hour movement curve showing AM rise, PM dominance, and overnight low demand.

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Top 24 Busiest Time Bins

Production-ranked time-bin chart highlighting the strongest 15-minute windows in the archive, using red intensity for peak pressure.

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Top 10 Time Bins by Average Daily Volume

Strongest Monthly Time-Bin Results

Confirmed Quietest Time Bin Result

This result gives the page the natural counterpart to the confirmed busiest period. Together, the two results describe the daily movement envelope of Melbourne's signalized road network: the network is strongest around 17:15 and quietest around 03:00. That makes the story easier for a public audience to understand because it answers both ends of the same question: when does Melbourne move the most, and when does it move the least?

Generated Quietest-Time Chart

Top 24 Quietest Time Bins

Production-ranked overnight low-demand bins, using green intensity for quiet-network conditions.

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Top 10 Quietest Time Bins by Average Daily Volume

Lowest Monthly Quietest Time-Bin Results

Confirmed Peak Share Results

Generated Peak-Share Charts

AM vs PM Peak Share Over Time

Monthly comparison of morning and afternoon peak shares across the archive.

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Peak vs Off-Peak Volume

Simple public-facing split showing how much movement occurs inside and outside the two peak windows.

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This result shows that Melbourne's signalised road network is heavily concentrated into short daily demand windows. The afternoon peak is materially stronger than the morning peak, with approximately 19,534,549,560 more cleaned vehicle movements recorded in the PM peak than the AM peak across the archive.

🌦️ Melbourne Weather × SCATS Network Traffic Intelligence

The analysis joins trusted daily SCATS network totals from 2014-01-01 to 2026-04-07 with Bureau of Meteorology daily weather observations for rainfall, maximum temperature, minimum temperature and solar exposure. Days are grouped into practical weather classes such as rain, heavy rain, extreme rain, hot days, very hot days, extreme heat, cold mornings, sunny days and low-solar days.

What the headline numbers show

Melbourne’s weather story is more interesting than a simple “rain equals traffic chaos” headline. At whole-network scale, ordinary rain is associated with only a modest traffic reduction, while heavier rain, extreme rain, very hot days and low-solar days show clearer movement suppression. In other words, Melbourne does not appear to shut down every time it rains — but the network does respond when weather becomes more severe, gloomy or uncomfortable.

The warm-weather pattern is also revealing. Hot days above 30°C show a small positive network difference, which may reflect stronger general activity, outdoor movement, retail activity, beach and recreation travel, or seasonal effects. But once temperatures move into very-hot territory, the pattern reverses: traffic volumes are lower than baseline. That suggests a practical behavioural threshold where ordinary warm weather may support activity, while oppressive heat begins to discourage or reshape travel.

Network-level weather response

The whole-network result suggests Melbourne traffic is resilient to ordinary weather but more sensitive to uncomfortable or disruptive conditions. Rain days are only slightly below baseline, but heavy rain and extreme rain show progressively larger reductions. Low-solar days also stand out, which may capture the combined effect of grey, wet, cold or low-activity conditions rather than sunshine alone.

This is useful because it separates everyday weather complaints from measurable network behaviour. The data suggests that normal rain is not the same thing as a citywide disruption. The larger movement changes appear when weather becomes severe enough to alter plans, reduce optional trips, shift travel times or discourage discretionary movement.

Rain severity matters

Melbourne talks about rain constantly, but the SCATS network does not treat all rain the same way. Light or ordinary rain is associated with a relatively small movement difference. As rainfall becomes heavier, the network-level reduction becomes more visible. That is the important public finding: the traffic story is not simply whether it rained, but how severe the rain was.

A likely explanation is that light rain changes comfort more than necessity. People still commute, go to school, make deliveries and move around the city. Heavier rain is different: visibility drops, roads become less comfortable, minor incidents become more likely, some discretionary trips are cancelled, and people may delay or consolidate travel. Extreme rain is where the citywide signal becomes much clearer.

Temperature and heat behaviour

Heat behaves differently from rain. Warm and hot days can coincide with higher activity, especially when people are still willing to travel for work, shopping, recreation, events or beach-related movement. But very hot days appear to cross a behavioural threshold where movement falls below baseline.

The extreme-heat result should be read carefully. Days above 40°C are much rarer than days above 35°C, so that bucket is more sensitive to weekday mix, school holidays, summer timing, public holidays and unusual activity patterns. The safest interpretation is not that 40°C heat is “less disruptive” than 35°C heat, but that very-hot weather shows the clearest negative network signal in this version of the analysis, while extreme heat remains lower than baseline but is based on a smaller and more variable sample.

Why this matters

Melbourne has a cultural obsession with weather, but most weather-and-traffic discussion is based on anecdotes: “the rain made traffic terrible,” “everyone stayed home because it was too hot,” or “sunny days bring people out.” This analysis gives those claims a measured historical frame. It does not prove weather alone caused the movement changes, but it shows that Melbourne-wide SCATS volumes do vary in consistent, interpretable ways across different weather conditions.

The most useful finding is that weather response is graded, not binary. Ordinary rain is only modestly different from baseline. Extreme rain is much more visible. Warm weather can coincide with more activity. Very hot weather is associated with lower movement. Low-solar days also show a meaningful reduction, suggesting that gloomy or low-light conditions may be part of a broader low-activity weather pattern.

• For journalists: the data turns Melbourne’s weather obsession into defensible numbers, showing which types of weather are actually associated with citywide traffic changes.
• For road authorities: the result helps separate ordinary weather from conditions that deserve closer operational attention.
• For freight and logistics operators: the network-level patterns can support planning around severe rain, very hot days and lower-reliability weather conditions.
• For researchers: this creates a foundation for more controlled modelling using matched weekday, month, season, holiday and year baselines.
• For the public: it shows that Melbourne traffic does not automatically collapse in all bad weather, but it does shift under more severe or uncomfortable conditions.

Interactive Map — Melbourne's 20 Busiest SCATS Sites

Traffic Intensity Legend

Red — Extreme traffic load

Orange — Very high traffic load

Yellow — High traffic load

Green — Lower within top 20

Circle size is scaled by total cleaned vehicle movements. Labels show rank order.

Interactive Map Site Directory

Top 20 Busiest SCATS Sites — Human-Readable Directory

Interactive Map — Full Melbourne SCATS Site Network

Scrollable Full SCATS Site Directory

The full directory is intentionally contained in a scrollable box so readers can browse thousands of mapped sites without making the page unusably long.

Interactive Map — Melbourne SCATS Site Changes After the West Gate Tunnel Opening Period

Network Coverage Map

The coverage layer now spans full SCATS network coverage, ranked busiest SCATS sites, Top 20 mapping, temporal behaviour, site-month intelligence, corridor dominance, parcel/OOH opportunity mapping, Kepler movement visualisations, COVID/recovery intelligence and reproducibility evidence.

West Gate Bridge and freeway corridor references should be read together with the TIRTL analysis page: West Gate traffic analysis.

Kepler.gl Spatial Traffic Intelligence Maps

The exported map layers below form a progressive traffic-intelligence hierarchy. Readers can begin with the full SCATS network, zoom into the CBD, narrow to the top 5% busiest arterial backbone, then isolate the top 1% most critical traffic nodes. This turns the SCATS archive into a spatial tool for journalists, planners, councils, researchers, and commercial analysts.

Static export set

Full Metro Traffic Intensity

The complete Melbourne SCATS network rendered as a weighted traffic intensity field. This is the baseline geographic view of the system.

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CBD and Inner Melbourne Zoom

A detailed inner-city view showing the CBD grid, Docklands, Southbank, inner arterial links, and high-density signal clusters.

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Arterial Skeleton / Top Sites Layer

The filtered backbone view, designed to reveal the dominant traffic corridors and the structure of Melbourne's highest-volume movement network.

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Top 5% Arterial Backbone Heatmap

This newer heatmap isolates the top 5% of SCATS sites to reveal Melbourne's main arterial movement skeleton with lower-volume noise removed.

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Top 1% Critical Traffic Nodes

This highest-pressure node view isolates the most critical 1% of mapped SCATS sites, showing the locations most likely to matter during congestion, incidents, and network disruption.

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Interactive Kepler.gl exports

Cinematic SCATS Traffic Animations

These click-to-play videos turn the completed Melbourne SCATS analytics into short cinematic movement films. They combine generated traffic animations with soundtrack layers so the data can be understood visually and emotionally, including the weekly heartbeat of Melbourne, local 7-day pulse rendering, Kepler.gl 24-hour time-bin playback, daily rhythm, seasonal behaviour, full-network coverage, Top 100 sites and COVID collapse/recovery history.

Melbourne Suburb Traffic Intensity Map

The Melbourne Suburb Traffic Intensity Map adds a suburb-level visual layer to the SCATS project. Instead of only reading suburb tables and Top 100 rankings, users can now explore Melbourne geographically and see how traffic pressure varies across suburbs.

Melbourne Commute + Traffic Pressure Map

The Melbourne Commute + Traffic Pressure Map combines modelled CBD commute estimates with the existing SCATS suburb traffic-pressure layer. It is designed for people asking practical questions such as: how bad is my commute, how much worse is peak hour, and how does my suburb compare with nearby suburbs?

Melbourne Commute + Traffic Pressure Graphs — Version 2

These Version 2 graphs summarise the suburb commute intelligence output using a metro-style filter. They show the fastest and slowest CBD commutes, PM return pressure, peak-delay penalties, traffic pain, low-traffic / high-access scoring, commute-time distributions and scatter plots connecting commute time, traffic pressure and route distance.

Graph 1 — Fastest suburbs to the CBD, AM peak

This graph shows the fastest modelled morning peak CBD commutes from suburb centroids. The inner suburbs dominate: Carlton, East Melbourne, Southbank, Fitzroy, Melbourne, South Wharf, Docklands and North Melbourne all sit close to the CBD and perform strongly.

Modelled suburb-centroid drive-time estimates; individual addresses and daily conditions will vary.

Graph 2 — Slowest suburbs to the CBD, AM peak

This is where the commute-pressure story becomes very clear. Clyde, Cranbourne East, Clyde North, Pakenham, Officer, Beaconsfield, Berwick and surrounding growth-corridor suburbs appear near the top.

The south-east growth corridor stands out strongly in the modelled AM peak commute data.

Graph 3 — Slowest CBD return commutes, PM peak

This graph looks at the afternoon return trip from the CBD back to each suburb. The outer growth areas again show up strongly, especially Clyde, Doreen, Wallan, Coldstream, Pakenham, Mernda, Donnybrook and the Cranbourne/Clyde corridor.

Commute pressure is not just about getting into the city — the trip home matters too.

Graph 4 — Biggest CBD peak-delay penalty

This graph compares peak commute time with off-peak travel time. The higher the suburb appears, the more peak hour adds to the trip.

Clyde, Clyde North, Cranbourne East, Cranbourne North, Officer, Beaconsfield and Narre Warren South stand out as areas where peak conditions add substantial time.

Graph 5 — Best low-traffic / high-access score suburbs

This score combines better CBD access, lower SCATS suburb traffic pressure and lower peak delay. South Wharf, Cremorne, Princes Hill, Kooyong, Seddon, Burnley, Tottenham, Ashburton, Kingsville and Carlton North perform well on this combined score.

This does not mean a suburb is silent or free of traffic; it means it scores well on this particular low-traffic / high-access comparison.

Graph 6 — Highest traffic pain index suburbs

This is one of the most important graphs. The traffic pain index combines local traffic pressure, slower CBD commute time and/or larger peak delay. Pakenham, Cranbourne, Cranbourne East, Cranbourne West, Berwick, Cranbourne North, Clyde North and Narre Warren South are all near the top.

The south-east growth corridor is very clearly visible here.

Graph 7 — Distribution of Melbourne suburb CBD commute times

This graph shows the overall spread of modelled AM peak CBD commute times across the metro-filtered suburbs. The median is about 38 minutes, while the 90th percentile is about 57 minutes.

Rather than only looking at individual suburbs, this shows the overall shape of Melbourne’s CBD commute burden.

Graph 8 — Distribution of CBD peak-delay penalties

This graph shows how much extra time peak hour adds compared with off-peak travel. The median peak-delay penalty is about 5.7 minutes, while the 90th percentile is about 10.7 minutes.

Some suburbs are only slightly affected by peak hour, while others face a much larger delay penalty.

Graph 9 — Suburb traffic-pressure category distribution

This graph counts how many suburbs fall into each SCATS traffic-pressure category. It shows that Melbourne is not evenly loaded: some suburbs sit in lower or moderate traffic-pressure bands, while a smaller but important group falls into high, very high and extreme traffic-pressure categories.

This is based on cleaned SCATS suburb traffic-pressure data.

Graph 10 — Best low-traffic / high-access suburbs under 45 minutes to the CBD

This is a more practical version of the low-traffic / high-access score because it only includes suburbs under 45 minutes to the CBD in the modelled AM peak estimate.

Useful for people asking: where could I live with decent CBD access but less traffic pressure?

Graph 11 — Highest average-speed CBD commute routes, metro filtered

This graph shows AM peak route distance divided by modelled AM peak travel time. Outer suburbs with more freeway or arterial-style access can sometimes show higher average speeds, even if the total trip is longer.

This is not saying these are the best commutes — it shows route efficiency, not total convenience.

Graph 12 — Peak versus off-peak CBD commute comparison

Each dot is a suburb. The further above the diagonal a suburb sits, the worse the AM peak commute is compared with off-peak.

The labelled suburbs near the upper right are where longer commute times and larger peak delays combine.

Graph 13 — Local traffic pressure versus CBD commute time

This graph combines two different things: local SCATS suburb traffic pressure and modelled AM peak CBD commute time. The most interesting suburbs are toward the upper right, where high local traffic pressure and longer CBD commute times combine.

That is where traffic pain becomes more than just distance from the city.

Graph 14 — CBD commute distance versus travel time

This graph shows the relationship between route distance and modelled AM peak travel time. Distance matters, but it is not the whole story.

Some suburbs at similar distances can have noticeably different travel times depending on road access, congestion, route structure and peak-hour delay.

Important Intersection Intelligence

The older “Top 50” concept has been consolidated into the richer intersection intelligence already present on the page. Importance is now assessed using multiple lenses: cumulative movement, busiest-site ranking, Top 20 mapping, full SCATS network coverage, corridor dominance, site-month volatility and OOH opportunity value.

• Confirmed busiest SCATS site
• Top 20 SCATS map
• Full SCATS site network
• Corridor dominance analysis
• OOH parcel opportunity map

How Melbourne Traffic Actually Behaves — Time-Bin Behavioural Analytics

Behavioural Analytics Charts

Cumulative Daily Traffic Curve

Shows how quickly Melbourne accumulates its daily road-network activity and when major cumulative thresholds are reached.

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Interpretation: this is the simplest public-facing way to show how quickly Melbourne “uses up” the day’s traffic volume.

Peak Window Share

Compares morning peak, midday plateau, afternoon peak, and overnight baseline as shares of total daily traffic volume.

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Morning vs Afternoon Traffic Shape

Compares equal six-hour windows to show the difference between the morning build-up and the stronger afternoon pressure pattern.

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How Long Melbourne Operates Near Capacity

Quantifies how many hours per day the network stays above 70%, 80%, and 90% of its maximum daily load.

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Morning Ramp-Up and Evening Drop-Off

Shows how fast the network wakes up, reaches the afternoon high-pressure period, and then releases load into the evening.

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Top 10 Busiest Bins Cluster Analysis

Shows that the strongest 15-minute exposure intervals are not scattered through the day; they are concentrated in a narrow afternoon window.

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Commercial interpretation: this gives OOH and media planners a highly specific high-exposure window, rather than a generic “PM peak” label.

Derived Behavioural Outputs

Production Time-of-Day Traffic Profile — When Melbourne Moves

Main Chart — Melbourne Traffic Daily Rhythm

The headline chart showing the full 24-hour movement curve, with the 17:15 peak and 03:00 quietest point annotated.

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Production Time-Bin Chart Suite

Day Segments

The same 96-bin profile coloured by behavioural period: overnight quiet, morning build, daytime plateau, afternoon peak, and evening decline.

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Broad Day Period Totals

Aggregated volume by broad traffic period, showing how much of the archive sits in each part of the day.

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Top 24 Busiest 15-Minute Bins

Red-ranked chart of the busiest 15-minute intervals, making the afternoon peak cluster visually obvious.

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Top 24 Quietest 15-Minute Bins

Green-ranked chart of the quietest intervals, confirming the overnight low-flow envelope around 03:00.

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Peak vs Quietest Network Load

A simple media-facing comparison showing the peak interval is about 15.1 times the quietest interval.

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Monthly Peak-Time Stability

Shows whether the peak month-by-month stayed aligned with the dominant 17:15 archive peak or shifted to nearby bins.

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Source Outputs

The section is based on the completed time_bin_profile.csv and time_bin_profile_final.json outputs, plus the produced behavioural metrics and derived temporal analysis layers.

Day-of-Week Behavioural Traffic Intelligence

The completed day-of-week profile turns Melbourne's SCATS archive into a weekly behavioural model of the city. Across the cleaned and deduplicated period from 1 January 2014 to 7 April 2026, the script processed 148 / 148 months, produced 1,029 analytical rows, and completed in approximately 32.5 hours. The result is a clear weekly movement signature: traffic builds from Monday through the working week, reaches maximum intensity on Friday, then falls sharply into the Saturday and Sunday rest state.

Headline Day-of-Week Charts

These charts show the core weekly movement signature: Friday peak, Sunday rest state, workweek build-up, and the long-term day-of-week heat structure.

Melbourne Weekly Traffic Heartbeat

The core weekly rhythm chart: Monday starts lower, the workweek builds steadily, Friday reaches maximum average daily movement, then the network drops into the weekend.

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Average Daily Traffic by Day of Week

Bar-chart version of the weekly hierarchy, designed for fast public interpretation and media use.

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Friday vs Sunday Differential

Shows the gap between Melbourne at full weekly intensity and its Sunday rest state.

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Day-of-Week Strength Index

Indexes each day against Friday = 100, making the relative strength of each weekday immediately visible.

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Weekday vs Weekend Share

High-level split showing that Melbourne remains structurally weekday-driven while weekends still contribute a major movement economy.

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Day-of-Week Traffic Intensity by Year

Shows the weekly rhythm across every year, making COVID disruption, recovery, and long-term growth visible in one image.

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Friday–Sunday Traffic Gap Over Time

Tracks how much stronger Friday was than Sunday month by month, revealing behavioural volatility and lockdown-era disruption.

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Melbourne Weekly Traffic Signature

A radar-style fingerprint of Melbourne’s weekly movement shape.

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Second-Wave Day-of-Week Behavioural Charts

These additional charts examine behavioural structure: Monday weakness, Friday dominance, COVID-era shape changes, seasonal day-of-week effects, cumulative contribution, and volatility.

Melbourne Workweek Build-Up

Quantifies the steady increase in movement intensity from Monday to the Friday peak.

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The Weekend Traffic Collapse

Shows how sharply traffic falls after Friday into Saturday and Sunday rest-state movement.

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Day-of-Week Behaviour Before, During and After COVID

Compares the weekly traffic rhythm across pre-COVID, COVID-shock, and recovery/new-normal eras.

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Weekly Shape Index Across Eras

Normalises each era against its own Friday peak to expose changes in the weekly behavioural shape.

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Friday Dominance Over Time

Tracks Friday’s annual premium compared with Monday and Sunday.

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Monday Weakness Over Time

Shows whether Monday remains structurally weaker than the rest of the working week.

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Weekday vs Weekend Share Over Time

Tracks whether Melbourne has become more weekday-driven or weekend-driven over time.

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Seasonal Day-of-Week Traffic Heatmap

Shows how weekly behaviour changes across the calendar year.

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Cumulative Weekly Traffic Contribution

Shows how much of the weekly traffic total has accumulated by each day of the week.

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Which Days Are Most Volatile?

Measures which weekdays fluctuate most month-to-month across the full archive.

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Weekday vs Weekend Behavioural Traffic Intelligence

Weekday vs Weekend Dashboard Summary

A one-image overview of the entire weekday/weekend behavioural layer, suitable for media, internal briefings, and social sharing.

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Overall Traffic Share: Weekday vs Weekend

Simple headline split showing the proportion of all detected SCATS movement events occurring on weekdays and weekends.

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Total Vehicle Movements: Weekday vs Weekend

Absolute scale comparison across the full archive: 404.9B weekday movements versus 134.1B weekend movements.

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Average Daily Traffic Intensity

Normalised daily comparison showing a typical weekend day reaches 82.7% of weekday daily intensity.

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Monthly Traffic Volumes Over Time

Monthly weekday and weekend movement totals showing long-term growth, disruption, and recovery patterns.

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Weekend Share of Total Traffic Over Time

Monthly weekend share plus 12-month rolling average, useful for detecting long-term behavioural change.

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Weekend Daily Intensity Relative to Weekday

Normalised monthly ratio showing weekend daily traffic as a percentage of weekday daily traffic.

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Monthly Traffic Composition

Stacked monthly composition showing weekday and weekend share within each month.

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Weekend Share Heatmap by Year and Month

Year-by-month heatmap showing seasonal and disruption effects in weekend share.

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Traffic Composition Across COVID Eras

Period comparison across pre-COVID, disruption, and recovery windows.

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Cumulative Traffic Volumes Over Time

Cumulative weekday, weekend, and total movement lines showing the archive building toward 539.0B events.

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Month-of-Year Seasonal Traffic Intelligence

The completed month-of-year workflow converts the full deduplicated Melbourne SCATS archive into a seasonal citywide movement profile. Instead of asking only which individual month was largest, this section asks how Melbourne behaves across the calendar year: which months are structurally busiest, which months are quietest, how COVID reshaped seasonal behaviour, and where the strongest recovery-era traffic pressure now appears.

Melbourne seasonal traffic curve

The flagship calendar-year curve showing the average daily SCATS movement profile from January to December.

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Busiest traffic months ranked

Calendar months ranked by average daily traffic intensity, with colour coding from quieter green to busier red.

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Seasonal traffic index

A normalized index where 100 represents the typical seasonal baseline. Values above 100 indicate stronger-than-average months.

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Month-by-year heatmap

A compact visual history of seasonal traffic intensity from 2014 to 2026, including the COVID-era disruption and recovery period.

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Seasonal traffic pattern by year

Each line shows one year’s monthly traffic rhythm, revealing how seasonal movement patterns shifted over time.

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Average share of annual traffic

Shows how much of each year’s SCATS traffic typically occurs in each calendar month.

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Traffic volatility by month

Identifies months whose average daily traffic has varied most strongly from year to year.

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Long-term monthly traffic growth

Tracks each calendar month across the full period, showing recovery, growth and long-term divergence by season.

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Busiest vs quietest month

A simple media-friendly comparison between February and January, showing the scale of Melbourne’s seasonal swing.

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Calendar-year intensity strip

A compact seasonal strip that can be used as a quick visual summary or media graphic.

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Total SCATS volume by calendar month

Cumulative cleaned traffic volume grouped by calendar month across the archive. March leads on total cumulative volume because it has more days and full-month coverage across more years.

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Seasonal traffic pattern by era

Compares pre-COVID, COVID-era and recovery/recent seasonal traffic behaviour in one chart.

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Traffic Archetype Intelligence — Melbourne Road Behaviour Classification

This new CSV-only intelligence layer classifies Melbourne SCATS sites into behavioural road archetypes using the already-computed site rankings, site-month totals, growth, volatility, stability, spike behaviour, and mapped site metadata. It does not require another DuckDB pass. Instead, it turns the hundreds of hours of completed CSV outputs into a reusable classification model for transport, OOH media, journalism, and infrastructure analysis.

Traffic Share by Archetype

Shows which behavioural road classes carry the greatest share of analysed Melbourne movement.

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Number of Sites by Archetype

Shows the structure of the classified SCATS universe after splitting mixed sites into more useful behavioural road classes.

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Behavioural Score Heatmap

The heatmap gives each archetype a behavioural fingerprint across scale, stability, volatility, growth, OOH exposure, transformation, and strategic importance.

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Scale vs Volatility by Traffic Archetype

One of the strongest system charts: it separates reliable backbone/exposure corridors from high-volume volatile corridors and local disruption-sensitive sites.

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Scale vs Growth by Traffic Archetype

Separates mature high-scale corridors from emerging growth corridors and lower-scale local growth zones.

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Pure OOH Exposure Score

Pure exposure ranking: the top sites remain dominated by Metropolitan Backbone corridors because the score rewards scale and stability.

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Diversified OOH Exposure Sites

Commercially useful diversified view that surfaces strong candidates across multiple behavioural road classes.

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Traffic Transformation Index

Highlights growth, volatility, spikes, and structural change rather than simple traffic volume.

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Strategic Importance Score

Balances traffic scale, stability, growth, and volatility into one network-importance ranking.

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Traffic Archetype Output Files

Top 10 Site-Month Insights — Named Sites and Hidden Network Nodes

This media-ready module turns the completed site-month charts into plain-English findings for journalists, advertisers, planners, and public readers.

1. Melbourne traffic has grown steadily over 12 years. Despite short-term disruptions, total network volume shows a clear long-term upward trend.
2. COVID caused the largest visible traffic collapse in the archive. The network monthly chart shows a dramatic 2020 drop followed by staged recovery.
3. Traffic has exceeded pre-COVID levels. By 2023–2025, Melbourne traffic recovered and pushed above earlier highs.
4. A small number of locations dominate the network. Sites such as Princes near Canning and Eastern Freeway / Hoddle carry enormous cumulative volume.
5. Growth is concentrated in specific corridors. The fastest-growing site-month outputs point to structural changes in how Melbourne moves.
6. Some of the most volatile records are network nodes, not named intersections. This exposes hidden traffic-control infrastructure in the SCATS network.
7. Traffic patterns are strongly seasonal. The heatmap shows annual rhythms, quieter periods, and stronger high-volume months.
8. Location value changes over time. A site’s importance is not only its total volume but how its monthly pattern changes across the archive.
9. Melbourne is thousands of micro-systems. The overall network can look stable while individual sites and nodes behave very differently.
10. The project now combines scale with human meaning. Friendly names, ranked sites, and network-node labels turn 539B movement events into a readable model of the city.

Second-Wave Traffic Intelligence Charts

Daily Calendar Heatmap

A year-by-year calendar heatmap showing cold, normal, and hot traffic days across the full archive. This is one of the most visually powerful ways to see lockdowns, holidays, weekly rhythm, and recent intensity.

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Monthly Year Heatmap

A month-by-year heatmap showing seasonal and long-term traffic intensity. It makes high-volume recent months, COVID disruption, and partial/data-gap months immediately visible.

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Weekday vs Weekend Evolution

Shows how average weekday and weekend traffic evolved year by year, highlighting behavioural shifts, COVID-era disruption, and post-pandemic recovery.

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Average Traffic by Weekday

Ranks the average traffic load for each day of the week. This is a simple public-facing chart for explaining which days carry the heaviest citywide movement.

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Day-of-Year Seasonality Profile

Shows the average traffic rhythm across the calendar year, smoothing daily totals to reveal seasonal traffic behaviour, holiday effects, and recurring demand cycles.

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Yearly Daily Percentile Bands

A statistical view showing median daily traffic and the normal daily range by year. This is stronger than a simple average because it shows how the whole distribution moves over time.

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Monthly Year-on-Year Growth Rate

Shows whether each month was higher or lower than the same month one year earlier. Green indicates growth; red indicates contraction.

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Top 20 Busiest Weeks

Ranks the highest-volume weeks in the SCATS archive. This is useful for identifying sustained pressure periods rather than one-day spikes.

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Network Time-of-Day Profile

Melbourne-wide 15-minute traffic profile showing how cleaned SCATS movement volume is distributed across a typical day.

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Peak Window Share

Share of total cleaned Melbourne SCATS traffic volume occurring during the AM and PM peak windows.

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Third-Wave High-Impact Traffic Intelligence Charts

1. Melbourne Traffic Momentum Index

Shows whether Melbourne traffic is accelerating or slowing using a 3-month rolling month-to-month change index. This acts like a city-scale traffic pulse indicator.

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2. Top 20 Rising Sites / Network Nodes

Ranks the SCATS sites and network nodes with the strongest long-term growth by comparing the first 12 observed months with the latest 12 observed months.

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3. Top 20 Declining Sites / Network Nodes

Shows where average monthly vehicle movement volumes have fallen most strongly, helping identify corridors whose role in the network may be changing.

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4. Network Stability Index

Measures how variable traffic is across SCATS sites each month. This is a system-level stability metric rather than a single-location chart.

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5. Traffic Concentration Curve

Shows how much of Melbourne’s total traffic is carried by the highest-volume share of SCATS sites. This is one of the clearest charts for infrastructure dependency and OOH planning.

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6. Commercial Traffic Value Ranking

Combines total volume, growth and stability into an OOH-style commercial traffic value score. This is a decision-support chart, not official advertising pricing.

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7. Weekday vs Weekend Traffic Fingerprint

Compares average daily network volume by day of week, making the work-week versus weekend behavioural structure immediately visible.

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8. Monthly Seasonality Fingerprint

Shows which months run hotter or cooler on average across the archive, turning monthly totals into a clean seasonal behaviour profile.

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9. SCATS Site Lifecycle Clusters

Classifies sites and network nodes into growth, stable, volatile and declining groups. This turns raw site-month data into a strategic network portfolio view.

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10. Traffic Shock Detection Timeline

Flags months where Melbourne traffic broke sharply away from the rolling historical pattern. This is one of the strongest public-facing story charts on the page.

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OOH Parcel Intelligence Method

This section is designed for digital and traditional out-of-home advertising companies, media owners, property partners, and major advertisers. The current SCATS intelligence layer already supports a portfolio view of roadside exposure: ranked sites, traffic concentration, campaign portfolio sizing, commercial coverage thresholds, break-even CPM modelling, and tier-based revenue scenarios.

OOH Portfolio and Campaign Strategy Charts

Campaign Portfolio Size vs Traffic Captured

Shows how much total ranked traffic is captured as more top-ranked SCATS sites are selected.

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Portfolio Efficiency Ratio

Traffic share divided by site share. Smaller portfolios punch far above their weight; larger portfolios become reach-driven.

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City-Level Traffic Concentration

Shows that site selection matters: traffic share rises faster than site share across the ranked network.

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Sites Needed for Traffic Thresholds

Decision chart for portfolio planning: how many top-ranked sites are needed to reach 10%, 25%, 50%, 75%, 90%, and 95% traffic capture.

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Commercial Portfolio Ladder

A sales-friendly view of traffic captured by different top-site portfolio sizes.

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Break-Even CPM by Portfolio Size

Scenario model showing the CPM required to break even across utilisation levels and portfolio sizes.

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ROI Heatmap — 1,000-Site Portfolio

Scenario heatmap for a 1,000-site portfolio using CPM and utilisation assumptions.

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Tier-Based Revenue Model

Illustrative revenue model using tiered CPM assumptions across ranked traffic tiers.

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OOH Source Files Now Wired Into This Section

Corridor Dominance Analysis

Individual sites reveal local exposure. Corridors reveal strategic market coverage. This section groups the Top 100 OOH opportunity sites into recognisable Melbourne traffic corridors and ranks them by combined long-term vehicle exposure.

SCATS Site Intelligence, Portfolio Efficiency and Revenue Modelling

1. Network Structure and Site Ranking

These charts show the structure of the ranked SCATS site network: concentration, rank decay, site activation, and the relationship between elite sites and the long tail.

Traffic concentration curve

Cumulative share by site rank. Shows the network is strongly hierarchical, with a minority of sites capturing a disproportionate share of total traffic.

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Rank decay by total volume

Shows the steep high-volume head and long lower-volume tail of the SCATS network.

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Rank decay — log scale

Log-scale view confirming heavy-tailed, power-law-like structure rather than a flat or random distribution.

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Monthly network volume

Historical monthly traffic volume. The COVID-era collapse and recovery are clearly visible, validating the real-world sensitivity of the archive.

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Active SCATS sites over time

Shows the active site count rising from roughly 3,400 to around 4,600, proving that coverage density increased materially over the archive.

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Traffic share by percentile band

Shows how traffic is distributed across percentile layers, proving that mid-tier and broad-network sites matter as well as the elite top sites.

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Traffic share captured by top-ranked sites

Shows the Top 1000 sites capturing about 46.3% of the traffic, creating a clear portfolio planning threshold.

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Top 25 SCATS sites

The strongest named site hierarchy, led by SCATS 4415 — PRINCES NR CANNING — with 674,498,771 movements.

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Average monthly volume per active site

Shows per-site average volume over time and highlights the same real-world traffic disruption visible in the network total.

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2. Strategic Tiering and Coverage Efficiency

These charts translate rankings into strategy: how much traffic each tier captures, how efficiently each tier performs, and how quickly marginal gains decline.

Coverage efficiency curve

Site share vs traffic captured. This is the core optimisation curve: the red curve above the equality line proves selective portfolio design beats random coverage.

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Marginal traffic gain by rank

Shows how much each additional ranked site contributes. Early sites carry high marginal value; the long tail produces diminishing returns.

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Marginal gain — log scale

A technical validation of diminishing marginal contribution across the full rank range.

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Traffic share by strategic tier

Tiered share model showing that the long tail has the most sites, but the ranked tiers carry disproportionate strategic value.

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Site count by strategic tier

Shows the network footprint behind the tiering model: a small core, a major middle layer, and a large long tail.

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Efficiency ratio by tier

Traffic share divided by site share. Tier 1 punches roughly four times above its footprint, proving premium-site leverage.

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Top 1000 vs remaining network

Top 1000 sites capture about 46.3% of total traffic while the remaining sites carry about 53.7%.

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Top 500 / next 500 / remaining

Top 500 capture about 28.0%; ranks 501–1000 add about 18.3%; remaining sites add about 53.7%.

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3. Campaign Portfolio Simulation

These charts answer the practical question: if a campaign or infrastructure strategy selected the top N sites, how much of Melbourne's measured traffic would it capture?

Campaign portfolio size vs traffic captured

A decision chart showing how traffic share grows as more top-ranked SCATS sites are selected.

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Campaign coverage efficiency

Portfolio site share against traffic share. Shows which portfolio sizes outperform a non-targeted coverage strategy.

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Incremental traffic gained

Shows how each expansion step contributes extra traffic coverage, useful for campaign upsell or rollout staging.

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Diminishing returns per added site

Shows the average incremental gain per added site falling as the portfolio expands.

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Portfolio efficiency ratio

Traffic share divided by site share. Small portfolios are extremely efficient; broad portfolios become coverage-driven.

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Traffic captured by portfolio size

Client-friendly bar chart showing milestones: Top 500 about 28%, Top 1000 about 46.3%, Top 2000 about 71.9%, Top 3000 about 88.9%.

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Remaining network traffic

The inverse portfolio chart: after selecting 2000 sites, about 28% of traffic remains outside the selected group.

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4. Concentration Thresholds and Commercial Portfolio Ladder

These charts provide client-friendly threshold visuals: how many sites are needed for 10%, 25%, 50%, 75%, 90%, and 95% traffic coverage, and how the network breaks into rank groups.

Top 100 vs remaining network

Top 100 sites capture 7.8% of traffic; this proves elite sites matter, but most movement sits beyond the very top layer.

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Top 500, next 500, remaining

Top 500 = 28.0%, ranks 501–1000 = 18.3%, remaining = 53.7%. This is one of the cleanest tier-structure visuals.

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City-level concentration curve

Signature concentration curve showing 20% of sites capture close to half of total traffic and 50% of sites capture roughly 80%.

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Sites needed for traffic thresholds

Shows how many top-ranked sites are required to capture 10%, 25%, 50%, 75%, 90%, and 95% of traffic.

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Remaining traffic after selection

Shows how quickly the unselected traffic pool falls as portfolios expand.

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Average traffic volume per site group

Top 100 sites average about 419.4M movements each; 3001+ sites average about 34.0M each.

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Traffic share by rank group

Ranks 1001–2000 form the largest single contribution group at about 25.6%, a non-obvious but important insight.

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Commercial portfolio ladder

Clear portfolio thresholds from 100 sites to full network, useful for packaging traffic coverage products.

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5. Revenue Simulation V1 — Simple CPM and Cost Model

The first revenue model uses simple placeholder assumptions to convert measured traffic into impressions, revenue, cost, and profit. These are scenario assumptions, not asserted market prices.

Revenue vs portfolio size

Initial CPM model showing revenue rises quickly then flattens as lower-yield sites are added.

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Cost vs portfolio size

Linear cost model based on a configurable cost-per-site assumption.

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Profit vs portfolio size

Initial profit model combining traffic-derived revenue and site costs.

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Revenue per site

Shows high early yield and predictable decline as broader portfolios include lower-ranked sites.

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6. Revenue Simulation V2 — CPM, Utilisation and Tier Scenarios

The second revenue model stress-tests multiple CPM levels, utilisation levels, break-even CPM, ROI, and tier-based pricing assumptions. These charts are best treated as scenario modelling for OOH media, infrastructure planning, or market-sizing discussions.

Revenue by CPM scenario

Sensitivity model for $2, $4, $8, $12, and $16 CPM scenarios across portfolio sizes.

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Profit by CPM scenario

Shows profitability under multiple CPM assumptions. This is a scenario model, not a claimed market price.

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Profit by utilisation

Stress-test of $8 CPM under 25%, 50%, 75%, and 100% utilisation.

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ROI by utilisation

Shows ROI sensitivity across utilisation assumptions and portfolio sizes.

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Break-even CPM required

Break-even CPM remains very low in this simplified model, indicating strong margin potential if assumptions hold.

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Revenue per site by CPM

Compares per-site yield under multiple CPM levels, reinforcing the importance of early high-ranked sites.

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ROI heatmap — 1000-site portfolio

Heatmap showing ROI sensitivity across CPM and utilisation assumptions for the 1000-site portfolio.

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Tier-based revenue model

Tiered CPM model showing the 101–500 group as the largest revenue contributor under the sample assumptions.

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OOH Buyer Pitch Pack — Why Media Companies Should Care

This section reframes the SCATS analysis for out-of-home advertising executives. It explains the commercial buying logic in their language: exposure, site ranking, audience reliability, campaign coverage, and acquisition prioritisation.

Suggested commercial language for the page

Recommended Build Roadmap — Turning This Into a Commercial OOH Intelligence Product

The analytical foundation for an OOH (Out-of-Home) intelligence product is now substantially complete. The platform already includes SCATS exposure rankings, temporal campaign intelligence, parcel opportunity discovery, corridor dominance analysis, site-month intelligence, interactive mapping, reproducibility evidence and downloadable diagnostics.

What is already commercially supportable?

Custom SCATS Traffic Intelligence Reports

These reports use the independently processed SCATS signal-volume intelligence layer covering Melbourne traffic movement patterns across 15-minute intervals, daily and monthly trends, yearly totals, site rankings, suburb profiles, before/after comparisons, time-of-day behaviour, weekday/weekend patterns and public evidence outputs.

This service is designed for journalists, councils, community groups, OOH media, property analysts, transport observers, local businesses, researchers and organisations that need a fast, plain-English evidence brief from the available SCATS traffic data.

What a custom SCATS report can investigate

Report packages

Every report is written in plain English and includes a clear distinction between what the data shows, what it suggests, what it does not prove, and what additional data would be required for a stronger conclusion.

To request a custom SCATS report, describe the suburb, road, intersection, corridor, time period or traffic question you want analysed. Include any relevant dates, roads, screenshots, council issue, media article, planning question or commercial use case.

Request a Custom SCATS Report

Questions the SCATS Database Can Answer

This section is based on the SCATS question bank and is intended to show the analytical range of the unified SCATS system once the database is fully populated and query-ready.

Questions the Combined SCATS + TIRTL System Can Answer

This section is based on the combined-system question bank. It is where the analysis shifts from intersection behaviour to true network behaviour.