These findings translate the current TIRTL outputs into a journalist-friendly summary:
network-scale truck movement records, freight-dependent corridors, truck-share hot spots,
suburb/locality summaries, and the future value of combining TIRTL with SCATS.
#1
Network overview
Major finding
The TIRTL layer now gives Melbourne a truck-specific movement view
The current enriched TIRTL layer contains 3,134,471,167 vehicle movement records and 159,620,033 truck movement records, with a network-wide truck share of 5.09%.
Why it matters:
SCATS shows total movement pressure, but TIRTL adds a heavy-vehicle and freight dimension. That makes it possible to separate ordinary traffic pressure from freight-heavy road function.
Evidence:
562 site-heading records across 303 unique sites and 47 suburbs/localities.
#2
Freight corridor
Major finding
Dohertys Road ranks as the strongest freight-dependence corridor in the V1 model
Dohertys Road ranks first in the Version 1 freight corridor model, with a Freight Dependence Score of 82.38, a truck share of 21.05%, and 691,967 truck movement records from 3,286,814 vehicle movement records.
Why it matters:
This shows the value of combining relative truck share with absolute truck volume. A road can be freight-important because trucks form a large share of traffic, because it carries a huge number of trucks, or both.
Evidence:
Freight-dependence band: High freight dependence.
#3
Truck movement ranking
Major finding
The top truck movement site is a high-volume freight exposure point
M1 West Gate Bridge Outbound (WB) in PORT MELBOURNE records the highest truck movement volume in the enriched top-site ranking, with 1,279,880 truck movement records and 7.50% truck share.
Why it matters:
Absolute truck movement volume identifies where the largest heavy-vehicle loads are being observed, even when truck share is not the highest percentage in the network.
Evidence:
Source site 505 W; total vehicle movement records: 17,066,572.
#4
Truck-share ranking
Major finding
The highest truck-share sites reveal freight-dominant road segments
Dohertys Rd (WB) in ALTONA NORTH has the highest truck share in the enriched ranking, with trucks representing 22.46% of movement records.
Why it matters:
Truck-share rankings expose roads where heavy vehicles make up a large proportion of observed traffic. These are different from simple high-volume roads.
Evidence:
Source site 264 W; truck movement records: 354,021; total vehicle movement records: 1,576,084.
#5
Suburb/locality intelligence
Supporting finding
MULGRAVE ranks highest by suburb/locality truck movement records
MULGRAVE ranks first in the suburb/locality truck summary, with 18,308,122 truck movement records from 282,977,913 total vehicle movement records and a truck share of 6.47%.
Why it matters:
Suburb/locality aggregation makes the TIRTL output easier for journalists, councils, residents, and businesses to understand without needing to interpret individual sensor records.
Evidence:
32 site-heading records and 18 unique sites are represented for this locality in the current summary.
#6
Spatial enrichment
Supporting finding
The TIRTL layer now supports suburb/locality-level freight summaries
The current enriched TIRTL output covers 47 Vicmap suburbs/localities, allowing truck movement pressure to be summarised geographically.
Why it matters:
This converts technical sensor data into geography that the public can understand. It also makes future council, suburb, corridor, and media story pages possible.
Evidence:
Suburb/locality names were added using spatial joins from TIRTL site-heading coordinates to Vicmap locality polygons.
#7
Freight corridor
Supporting finding
The freight corridor model starts classifying what roads are doing economically
The Version 1 freight corridor output identifies 21 inferred monitored corridors. 0 are classified as extreme freight dependence and 9 as high freight dependence.
Why it matters:
This moves the analysis from raw traffic counts into road-function intelligence: which roads behave like freight spines, mixed commuter/freight corridors, or lower freight-dependence roads.
Evidence:
The V1 score uses 45% truck-share component, 40% truck-volume component, and 15% monitored corridor-density component.
#8
SCATS + TIRTL combined intelligence
Strategic / methodology note
The biggest future value comes from combining SCATS total traffic with TIRTL truck pressure
SCATS can identify total traffic pressure, while TIRTL can identify truck pressure and freight dependence. Together they can support a Melbourne Freight Exposure Index.
Why it matters:
This enables insights that neither dataset can provide alone: high-traffic but truck-light places, moderate-traffic but truck-heavy places, freight corridors, commuter corridors, and mixed-use pressure zones.
Evidence:
The current TIRTL outputs already include truck movement volume, truck share, site-heading coordinates, suburb/locality joins, monthly summaries, and corridor scores.
#9
Methodology / caveat
Strategic / methodology note
The public-facing wording must keep distinguishing movements from unique vehicles
Every major TIRTL page should state that vehicle movements and truck movements are sensor passings, not unique vehicle counts.
Why it matters:
This prevents overclaiming, protects the credibility of the work, and makes the outputs easier for journalists and officials to use responsibly.
Evidence:
Vehicle movements and truck movements are classified TIRTL sensor movement records / passings. They are not counts of unique vehicles or unique trucks.