Road freight origination-destination inference

This project will develop a data fusion framework to infer road freight origin-destination (OD) flows using information from multiple sources with varying levels of quality, coverage and detail. The framework will be applied using traffic count data from the National Freight Data Hub (NFDH) and commodity flow data from the 2014 Road Freight Movement Survey (RFM).

The quality of the inferences will be validated against other estimates in the literature. Based on the findings, the project will develop a preliminary strategy to identify what other information on road freight movements should be collected in the future to improve these inferences.

Participants

• Department of Infrastructure, Transport, Regional Development, Communications and the Arts (DITRDCA)
• University of South Australia

Project background

Road freight movements are the bloodlines of economic activity in Australia. Over the year 2021-22, 234.6 billion tonne kilometres (tkm) of freight were moved by road within Australia, and freight movements by road made up roughly 30 per cent of total domestic freight movements (Bureau of Infrastructure and Transport Research Economics Research Report 154, 2022).

Road freight has grown by roughly 2 per cent per annum over the 10 years to 2020, and road freight volumes are projected to grow by 77 per cent between 2020 and 2050 (Bureau of Infrastructure and Transport Research Economics Statistical Report, 2022). In order to design, operate and maintain safe, reliable and efficient road networks, it is important to understand how these networks are used by freight-carrying road vehicles.

Despite the magnitude and significant expected growth in the Australian freight task, state transportation departments and other public agencies often do not have freight forecasting tools at their disposal capable of analysing relevant scenarios for the impact of future freight movements on their transportation networks, population and the environment.

The absence of robust freight models can be partly attributed, however, to irregular and inconsistent collection of data and hence poor availability of useful statistics on freight movements across the country. In some cases where good data is collected, such as by shipping agencies or logistic companies, it is not able to be accessed in its entirety due to its proprietary or confidential nature. Despite the existence of a few good available data sets, a quick exercise of mapping all of them to the transportation models for which they could be the basis for estimation reveals significant gaps …

McHugh et al., 2021. Freight modelling in Australia in 2021 – A data availability perspective

Based on a review, UniSA were only able to locate two sources of publicly available information on road freight movements, and both of these have major limitations. The first source is the Road Freight Movement Survey (RFMS) conducted by the ABS, which estimates origin-destination (OD) road freight movements at an SA3 level. However, the survey has been discontinued by the ABS, and the last period for which data is available is 2013-14.

The second source is the National Freight Data Hub (NFDH), which provides harmonised daily truck traffic counts based on data collected from road counters that are able to distinguish between light and heavy vehicles. This data is still being recorded, and the earliest records are available from 2008. The dataset offers useful information on road use by freight vehicles at a link-level, but does not collect any information on where these vehicles are coming from, or where they are going to, limiting the insights it can offer on OD flows.

While these two datasets are limited by themselves, the information contained within them could potentially be combined to produce valuable insights on road freight OD flows. For example, the relationship could be modelled between link-level counts, as estimated by the NFDH, and OD flows, as estimated by the RFMS, using data collected in 2013-14. Assuming that the relationship is temporally stable, it can infer OD flows for future years based on link-level counts estimated by the NFDH.

A similar methodology has previously been developed by Sun et al. (A flexible and scalable single-level framework for OD matrix inference using IoT data, 2022) to combine information from different IoT sources to infer OD flows for private car travel within the Greater Adelaide metropolitan area.

This project will build on the framework developed by Sun et al. (2022) to infer road freight OD flows using publicly available data on freight movements, namely the RFMS and NFDH datasets described previously.

Project objectives

This project has the following three inter-related objectives:

1. Explore the quality and robustness of publicly available datasets on road freight movements, namely the RFMS and NFDH datasets;
2. Develop and test a framework to infer road freight OD flows that combines insights from the RFMS and NFDH datasets; and
3. Design a preliminary strategy for future freight data collection efforts that can improve the quality of these inferences.

Please note …

This page will be a living record of this project. As it matures, hits milestones, etc., we’ll continue to add information, links, images, interviews and more. Watch this space!