Truck Telematics

The truck telematics data project demonstrates the feasibility and value of industry sharing truck location data with government to generate multiple freight insights. This project contributes to our understanding of many enduring questions for freight, including:

  • Where is freight being moved?
  • How is freight being moved?
  • What and where are the physical and regulatory bottlenecks and barriers for the efficient and safe movement of freight?
  • What and where are the opportunities for freight movements to be more efficient and safer?
Telematics data records times and locations from trucks on our roads. The visualisations have been developed to show how useful this information could be, but it would benefit by having more contributing vehicles. Please use caution when using these visualisations as the current telematics data set is from a small sample of trucks which may not reflect utilisation patterns of the broader fleet.

Sources of telematics data

There are two sources of telematics data visualised on the National Freight Data Hub prototype website. The two sources of the data are the Bureau of Infrastructure and Transport Research Economics and Transport Certification Australia.

Industry provides the Bureau of Infrastructure and Transport Research Economics data to government voluntarily. The two largest providers are Toll and Australia Post. Their decision to share data was taken as part of their corporate social responsibility agenda to facilitate benefits for all of industry. If you are a heavy vehicle operator and interested in sharing your data please get in touch.

Transport Certification Australia is the Australian entity responsible for administering the Intelligent Access Program and other applications of the National Telematics Framework. Transport Certification Australia is independent of government and collects industry telematics data. By de-identifying and aggregating the data, Transport Certification Australia provides certainty to stakeholders that transport operator and vehicle-specific data is protected from disclosure to other parties, commercially sensitive information is securely managed, and privacy-by-design principles are upheld. For further information please visit the Transport Certification Australia website.

Objectives of visualising telematics data

This project has worked with stakeholders to obtain, process, visualise and share de-identified truck telematics data. By showcasing the capability of telematics data, this project aims to increase telematics-derived insights by encouraging industry to participate in sharing its data. By visualising data on the National Freight Data Hub and providing useful insights back to industry and governments, the benefits of sharing data can be realised for all parties.

Potential use cases for telematics data include:

  • Enabling industry to vary billing and delivery windows based on congestion factors at different times of day.
  • Facilitating truck fleet optimisation.
  • Improving strategic investment decisions by industry and governments by enabling utilisation trends and patterns to be understood.
  • Enabling transport network planners to improve their evidence base of numbers and types of trucks using the network, to develop strong business cases for where and when road maintenance and upgrade investments will provide the greatest value.
  • Enabling road managers to understand where heavy vehicles are travelling and in what numbers on the roads, to control and promote road access in a detailed way depending on the condition of the road.
  • Enabling road safety managers to track and connect information about safety incidents, understand patterns and safety issues, and improve safety outcomes.
  • Enabling local councils to access more complete and up to date information about their freight networks, including links with other local government areas.
The visualisations are not used for compliance purposes.

Congestion

Telematics data records times and locations of trucks on the network, from which vehicle speed can be calculated. In the visualisations shown on the prototype website, the highest speed on a road segment, which is usually late at night, is used to represent the uncongested free-flow speed. Congestion is then calculated, for each hour of the day, as the deviation from the free-flow speed. The longer it takes for a truck to drive through a road segment, the more congestion there is. This means:

  • Users can see changes in speed over a road segment based on time of day which provides a proxy for congestion.
  • Users can see peak congestion, calculated as the time taken for a truck to drive through the congested road segment compared to the time taken at free-flow speed. A score of 1.0 means the time taken to traverse the road segment is the same as the free-flow speed. A score of 1.5 means it takes 50 per cent longer to travel the road segment compared to free-flow. Peak congestion is calculated using the slowest hourly speed along the road during peak times.

The Transport Certification Australia and Bureau of Infrastructure and Transport Research Economics datasets are not directly comparable except when comparing the same road segment for the same time period. Specifically, Transport Certification Australia use average speed while the Bureau of Infrastructure and Transport Research Economics use median speed, the sample sizes change over time, the two data sources cover different time periods and they sample road segments differently. These differences are most pronounced in the city-wide comparisons where the Transport Certification Australia and Bureau of Infrastructure and Transport Research Economics results cover different roads and time periods.

There are other ways to calculate congestion. For example, free-flow speeds could be set equal to the posted speed limit on each road segment. However, posted speed limits are not currently available digitally for all roads in Australia. Other congestion metrics include journey time reliability (i.e. the measured variability in travel times), however, that measure has limitations, for example, reliability could decrease as both travel times improve (for example, the journey becomes faster due to an extra lane) and travel times increase (for example due to roadworks).

Some truck operators are interested in helping their customers understand the impact of congestion on their operations, and encourage greater use of freight transport services outside the most congested periods. This would assist with fleet optimisation, with trucks spending less time in congestion. Further consultation with industry will help refine the ‘congestion measures’.

Another strength of the data is that it provides a historic record of freight congestion. Other data and mapping services only provide short-lived real time congestion information on our roads.


Number of trucks on roads

While this data currently represents only a small percentage of the overall fleet, the insights demonstrate how valuable truck telematics data could be to inform investment planning, if industry participation grows and more trucks can be added to the sample.

The truck telematics data could also be used to supplement other sources of heavy vehicle traffic count data to better understand movements on the road network. For example, to calibrate information about speed and vehicle movements from traffic counters and to extrapolate movements more accurately across the network. This supports more efficient operations and planning for improvements across the network.



Rest Areas

Regular stops and the opportunity for rest breaks are essential for safe driving, and heavy vehicle rest areas are provided to help drivers manage fatigue and comply with driving hours regulations (by providing an opportunity for sleep and rest breaks).

This Insight shows over 2,020 formal rest areas and their facilities, from state and territory open data sources, and the visualisation is focused on providing information about the level of activity at these locations. Stops made outside of these locations are not included.

Limitations of the rest areas data
  • Some states and territory datasets have rest areas which don't distinguish between light and heavy vehicles. Where feasible the Hub shows only heavy vehicle rest areas.
  • Rest stop events have been classified as places where a vehicle stopped for 15 minutes or more and within 200 metres of a formal rest area location. Not all stops will necessarily be rest stop events.
  • Transport Certification Australia and Bureau of Infrastructure and Transport Research Economics data have been combined on the Rest Areas Insights page. To ensure no individual vehicle can be identified, where the number of vehicle stops at any rest area is ten or fewer, it is reported as 1-10 stops.
  • The facilities listed as being available at rest areas is reliant on information supplied by state open data sources and may be incomplete.


Truck movements by Local Government Areas 

The visualisation assists freight network planners to understand the origin and destination of heavy vehicle movements to improve network design and investment decisions.

The origins and destinations are calculated from when a truck has stopped in one location for more than one hour. However, not all stops of one hour will necessarily be the start (loading) or finish (unloading) of the freight. For example, stops of more than one hour at rest areas will break a journey and be recorded as both a trip end point (a destination) and a new trip start point (an origin).

This sample data illustrates some variations during the period. Additional analysis and data cleansing is underway to remove any potential variations due to data processing and analysis. However, the data may partly be reflecting some challenges faced by the community and transport sector between July 2019 and June 2020.

Where there are between 1-10 truck trips between two local government areas the visualisation will show ten trips were made. This is to deidentify movements between two geographies.

The telematics data doesn't include information on of the nature of the freight being moved and includes journeys made by trucks which might be empty as well as where trucks are being repositioned.

While this data currently represents only a small percentage of the overall fleet, the Insight demonstrates how valuable truck telematics data can be if industry participation grows. It is not recommended that the visualisation be used, at this time, for decision making as the data sample is too small.


About the data


Bureau of Infrastructure and Transport Research Economics telematics
  • Dataset name: Bureau of Infrastructure and Transport Research Economics telematics - rest areas and congestion.
  • Data owner: Bureau of Infrastructure and Transport Research Economics.
  • Operator coverage: Australia wide.
  • Date range: January 2019 to June 2021.
  • Frequency: Quarterly.
  • Description: Bureau of Infrastructure and Transport Research Economics have provided aggregated and deidentified telematics data on Australian roads and rest areas collected from truck operators who have collaborated in sharing data for research purposes. The data contains information from 5,000 heavy vehicles.

Transport Certification Australia telematics
  • Dataset name: Transport Certification Australia telematics – congestion, rest areas and truck count.
  • Data owner: Transport Certification Australia.
  • Operator coverage: Australia wide.
  • Date range: 1 July 2019 to 30 June 2020.
  • Frequency: To be confirmed.
  • Description: Transport Certification Australia have provided aggregated and deidentified telematics data on Australian roads and rest areas collected from vehicles enrolled in access applications. The data contains information from 6,700 heavy vehicles.

The Transport Certification Australia data is visualised using Geoscape Australia base map road geometries, copyright and disclaimer information is available online. The Bureau of Infrastructure and Transport Research Economics data is visualised using Open Street Map road geometries. It is not possible to combine the Bureau of Infrastructure and Transport Research Economics and Transport Certification Australia road datasets as the base map geometries used by the two datasets are different, and the de-identification and aggregation of the data makes projecting to another base map difficult. Rest area data for Transport Certification Australia and Bureau of Infrastructure and Transport Research Economics was able to be combined as both datasets use longitude/latitude to specify the locations.

The visualisation was built using telematics data from heavy vehicles. Telematics uses satellite tracking and wireless communication technology to remotely monitor where, when and how heavy vehicles are being operated on the road network. Truck position data is generated by telematics devices which generate position records using the Global Navigation Satellite System, such as the Global Positioning System.

The majority of the Transport Certification Australia vehicles are enrolled in the Intelligent Access Program which have restricted access to the network. Further details about the classes of trucks with restricted access can be found on the National Heavy Vehicle Regulator page.

The telematics data was de-identified and aggregated to ensure individual trucks or operators couldn’t be identified in the data.

The geographical areas for the capital cities are based on the Australian Bureau of Statistics Greater Capital City Statistical Areas.

Future improvements to the Insights

Telematics is a proven technology already used by multiple road managers to improve access decisions in Australia. Telematics offers road managers improved assurance in making access decisions through better visibility of restricted access heavy vehicles. This facilitates an expanded restricted access heavy vehicle network improving efficiencies and productivity for industry with greater visibility and assurance for road managers and communities.

Industry currently use telematics data to track in real time the location of their trucks and to assist with truck maintenance, for example to understand tyre wear and improve safety.

However, only a small proportion of transport operations see their historic vehicle movements which enables an analysis of trends. This is because it costs money to gather, store and visualise data for uncertain returns on time and investment.

Growing our small sample and making it more representative of the heavy vehicle fleet is a priority as it will improve the accuracy and usefulness of the visualisations. We are keen to work with fleet operators of both heavy trucks and light commercial vehicles moving freight to add more data, and help answer industry needs with new insights from the data. If you are interested in participating please get in touch.

Limitations

Please refer to specific limitations of the visualisations above.

  • The numbers of heavy vehicles in the telematics datasets are small compared to the total number on the road network and not representative of the truck fleet.
  • There is a small proportion of duplicated trucks across the Bureau of Infrastructure and Transport Research Economics and Transport Certification Australia datasets.
  • The de-identification process involves aggregating data either by road segment, time and heavy vehicle type. Trips on road segments below ten are shown as 1-10.
  • The underlying de-identified data used to build the three telematics data visualisations is not publicly available.
  • The Transport Certification Australia data has a small number of heavy vehicles which don’t move freight e.g. concrete pumps and cranes.