Road freight OD inference: Fixing Australia’s freight data deficit

The iMOVE R&D project Road freight origination-destination inference has been completed, and its final report is available for download below.

The outcomes provide the Department of Infrastructure, Transport, Regional Development, Communications, Sport and the Arts (DITRDCSA), and other transport agencies, with a comprehensive, three-pillar strategic toolset which enables operational estimation, long-term forecasting and survey cycle optimisation.

Background

Road freight is the lifeblood of the Australian economy. In 2021-22 alone, road transport moved an estimated 234.6 billion tonne-kilometres of freight, representing nearly 30% of the total domestic freight task.

Volumes are projected to grow by 77% between 2020 and 2050, meaning the need for robust, evidence-based planning tools has never been greater. Yet, despite the size of this challenge, Australia’s freight modelling capabilities still face a data deficit, characterised by the disconnect between outdated structural intelligence and fragmented modern observation.

The primary aim of this report is to demonstrate that valid, actionable freight intelligence can be generated in a data-sparse environment using road freight origin-destination (OD) inference, rather than undertaking expensive new surveys.

The challenge

Effective transport planning requires both observational reality and structural knowledge. Observational reality helps understand where freight is moving, observing actual traffic flows on the network. Structural knowledge helps understand why freight moves, which includes the economic relationships between industries, land use and supply chains that determine origin-destination demand.

The last comprehensive national snapshot of structural knowledge, the ABS Road Freight Movements Survey (RFMS), was conducted in 2014. In the decade since, the Australian freight task has evolved significantly.

Conversely, observational traffic movement data is abundant and current but lacks the OD logic required for strategic planning. This leaves transport agencies and planning authorities with a forced choice: rely on a decade-old structural snapshot that doesn’t account for recent shifts in transport dynamics, or rely on traffic counts that provide sparse traffic volume without context.

The solution

This project addressed this impasse by developing an integrated origin-destination matrix (ODM) inference framework that systematically fuses the structural legacy of the 2014 survey with the continuous reality of modern traffic monitoring.

Data Robustness Analysis systematically evaluates the utility of publicly available datasets (ABS RFMS, National Freight Data Hub traffic counts) and complementary sources (CSIRO TraNSIT, State Government surveys) for network-wide OD inference.

The solution involves developing and testing a scalable inference framework using Tasmania as a closed-system pilot region to validate the methodology.

It also includes strategic monitoring capability by designing a quantitative method to monitor the “shelf life” of survey data, providing transport agencies with an evidence-based trigger for determining when new data collection cycles are required.

Methodology

Rather than viewing the 2014 survey as obsolete, the survey acts as a foundational structural prior, representing a repository of economic logic that can be extracted, projected forward and calibrated against observational traffic data.

Building on established single-level optimisation principles, this framework fuses three critical components:

• Structural learning: extracting the fundamental geo-economic logic (the relationship between land use, employment, freight generation and distribution) from the 2014 survey.
• Baseline projection: projecting this logic forward using current socio-demographic data to create a Baseline ODM.
• Empirical calibration: applying a rigorous optimisation process to adjust this baseline against observed traffic counts, generating a Calibrated ODM that respects both economic fundamentals and actual road network usage.

Tasmania case study

The framework demonstrated high efficacy in generating validated 2024 freight matrices when using Tasmania as a pilot region.

The 2024 Calibrated ODM achieved near-perfect symmetry between origin and destination flows, a key marker of internal validity. For the vast majority of regions, the estimated freight generation remained statistically consistent with historical error bands, confirming the stability of the model.

Crucially, longitudinal analysis (2015–2024) revealed a measurable, clear “drift” between the economic baseline and observed reality. This confirms that the 2014 structural knowledge is slowly losing its predictive power, with the deviation growing at a consistent annual rate.

Conclusions and next steps

This study proved that it is possible to bridge the gap between historical surveys and modern data streams. The framework is modular, scalable and designed to ingest emerging data sources (such as GPS telematics and Bluetooth) to further improve accuracy. It is recommended to scale this methodology to mainland Australia to establish a continuous, national-scale freight monitoring capability.

The framework provides the DITRDCSA, state and territory transport agencies, and planning authorities with the following immediate strategic capabilities:

• Continuous estimation: generating “best-estimate” OD matrices for any year where traffic count data exists, removing the blind spots between major survey cycles.
• Long-term projection: Because the framework is anchored in structural economic drivers (employment and land use), rather than just historical trends, it serves as a robust forecasting engine.
• Evidence-based survey planning: By quantifying the structural decay rate, transport agencies can move away from arbitrary survey schedules. The framework can also provide an early-warning system, identifying exactly when the 2014 baseline estimates have become too unreliable, thereby providing a robust business case for commissioning the next generation of freight surveys.

Download the report

Download your copy of the final report, Road freight OD Inference: Methodology and pilot study using Tasmania data, by clicking the button below.