Appendices and References

Brief summary of methods and measures

For this paper, BITRE used freight telematics to collate speeds experienced by freight vehicles on individual road segments ranging in length from a few to several hundred metres. BITRE defined routes and identified the segments that make up those routes. Median travel times were determined by calculating the time taken if a vehicle experienced the median travel speeds across all segments on the route, and the same method was applied for the interquartile range with speeds at the 1^st and 3^rd quartiles.

The Mean Excess Time Ratio (METR) is calculated as the mean hourly ratio of median travel times to the best observed median travel time. The Mean Excess Uncertainty Ratio (MEUR) is calculated as the mean hourly ratio of interquartile range to the smallest observed interquartile range.

The aggregate measures for each city are calculated as the mean of these two measures for a city, weighted by the distance and volumes of traffic observed on each route. This ensures congested, but relatively short and unimportant routes for freight, such as the M1 in Sydney, do not overly affect results.

Some data sparse segments required Bayesian estimation. Bayesian estimation was implemented via the Stan modelling language for Bayesian analysis (Stan Development Team 2020), implemented through the ‘rethinking’ package for R (McElreath 2020).

The segments making up routes were identified with a lightly modified version of the OSRM routing engine (Luxen and Vetter 2011).

Summary data for all routes and segments on this report will be available on data.gov.au, and the analysis code is available at BITRE (2021b).

About the BITRE freight telematics program

This paper uses data from the BITRE telematics project. This project transforms GPS traces from freight vehicles of private road freight operators into data about Australia’s road freight industry and road freight network, to help inform industry, government and other interested parties. This data can help inform planning and investment in the road network and rest areas, inform industry and government on economic activity and assist trip planning among other things. The project uses BITRE’s independently developed Yulo framework (Green and Mitchell, 2018, BITRE 2021b). By tracking the entirety of vehicles’ journeys it can generate data on more parts of the road network than is practical using conventional road data collection means such as fixed cameras or pneumatic tubes. This report is based on nearly 190 million observations from over 5 000 road segments whilst the database contains billions of observations on over 1 million road segments.

Previous publications using this data include an analysis of the effect of COVID 19 lockdowns on freight route performance in 2020 (BITRE 2020) and a display of the freight catchments served by Australian ports (BITRE 2021c).

References

BITRE 2020, Freight route performance under COVID-19, Information Sheet 107, BITRE, Canberra. URL: https://www.bitre.gov.au/publications/2020/freight-route-performance-under-covid-19

BITRE 2021a, Freight vehicle congestion in Australia’s five major cities - 2019, BITRE, Canberra. URL: https://www.bitre.gov.au/publications/2021/freight-vehicle-congestion-australias-five-major-cities-2019.

BITRE 2021b, BITRE Yulo telematics data project repository. URL: https://github.com/BITRE-Telematics/Yulo.

BITRE 2021c, Regional port catchment for road freight, Information Sheet 110, BITRE, Canberra. URL: https://www.bitre.gov.au/publications/2021/regional-port-catchments-road-freight

Green, R and Mitchell, D 2018, ‘Adapting truck GPS data for freight metrics,’ Paper presented at the Australian Transport Research Forum, Darwin, ATRF 2018 Paper 18.

Luxen, D. and Vetter, C 2011, ‘Real-time routing with OpenStreetMap data,’ Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, URL:

https://github.com/Project-OSRM/osrm-backend

McElreath, R 2020, ‘rethinking,’ URL: https://github.com/rmcelreath/rethinking.

Stan Development Team 2020, Stan Modeling Language Users Guide and Reference Manual, 2.26. URL: https://mc-stan.org.

ISSN 1440-9593

November 2021

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This publication should be attributed in the following way; Bureau of Infrastructure and Transport Research Economics (BITRE), Freight vehicle congestion in Australia’s five major cities - 2020, BITRE, Canberra.

Acknowledgement

This information sheet was prepared by Richard Green and Weihua Chen, under the direction of David Mitchell. Image credit Department of Infrastructure, Transport, Regional Development and Communications.

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