Rollscope: Bulk Tanker Safety
Rollscope: Bulk Tanker Safety
Learning from Near Misses and Applying the Pressure
HVSI Project 906 report
This Heavy Vehicle Safety Initiative is led by the National Bulk Tanker Association (NBTA) in partnership with participating fleets. Supported by the NHVR’s Heavy Vehicle Safety Initiative (HVSI) program and funded by the Australian Government, the project reflects a coordinated industry effort to use real-world trailer data to improve safety outcomes.
This public report brings together two complementary projects: Learning from Near Misses, which analysed EBS and roll stability events, and Applying the Pressure, which examined tyre pressure and temperature data from TPMS systems.
The projects demonstrate how large-scale telematics data can move beyond vehicle tracking, and instead be used to identify emerging risk, support targeted intervention, and improve operational awareness across fleets.
Overview
This platform presents the public findings of a multi-stage heavy vehicle safety initiative focused on making better use of trailer telematics data.
Led by the National Bulk Tanker Association (NBTA) and supported through the NHVR’s Heavy Vehicle Safety Initiative (HVSI) program, the project explores how existing data from trailer systems can be turned into practical safety intelligence for industry.
The work brings together two connected project streams. The first, Learning from Near Misses, examined EBS, harsh braking and roll stability events to better understand where elevated risk (near miss events) is occurring in real-world operations. The second, Applying the Pressure, extended the analysis into tyre pressure and temperature data from TPMS systems to better understand tyre condition, alarm behaviour, data quality, and early indicators of tyre-related safety risk.
Together, these two streams show the value of moving beyond traditional incident-based safety analysis. Rather than relying only on crashes or fire events after they occur, this project demonstrates how trailer telematics data can be used to identify patterns of risk earlier, support targeted investigation, and improve situational awareness across fleets.
The findings presented here are based on large volumes of real-world telematics data collected from participating trailer fleets over an extended period. This includes location-based EBS event data, harsh braking and roll stability activity, and wheel-level TPMS records covering tyre pressure, tyre air temperature, alarm codes and sensor reporting behaviour.
By combining these datasets, the project provides a broader picture of trailer safety performance: where near misses are occurring, how tyre conditions behave in service, how often true alarms arise, and where data or system limitations may affect interpretation.
This page is designed as a public-facing interactive report of the project findings. It combines explanatory text, headline statistics, charts and interactive maps to make the project results easier to explore.
The Learning from Near Misses section focuses on EBS-related safety events and their spatial distribution, helping show where harsh braking and stability interventions are occurring. The Applying the Pressure section focuses on TPMS data, including pressure and temperature distributions, wheel position behaviour, and the practical meaning of TPMS alarms and alert thresholds.
The purpose of the dashboard is not only to present findings, but also to demonstrate how trailer telematics can support better safety understanding when the data is properly cleaned, interpreted and visualised.
The results presented here should be understood as applied, data-driven findings drawn from participating fleets rather than a complete picture of the entire heavy vehicle industry. Even so, they provide valuable evidence of the kinds of risks, trends and data issues that can be identified through closer use of trailer telematics.
The spatial patterns shown on this page should be interpreted in the context of route coverage. Participating vehicles operated on defined freight routes, meaning some corridors were travelled repeatedly while other parts of the network were not represented in the data. For this reason, event clusters may reflect both operating exposure and risk within these routes, while areas with no events may simply indicate no fleet activity.
Overall, the project shows that existing trailer data has significant untapped value. With the right analysis, it can help fleets, industry bodies and regulators better understand emerging safety risks, improve maintenance attention, and support more proactive safety management across the heavy vehicle sector.
Learning from Near Misses
The first stage of this initiative focused on understanding safety risk using near-miss events captured through Electronic Braking Systems (EBS) and Roll Stability Systems (RSS).
Traditionally, safety analysis in the heavy vehicle industry has relied on crashes and incidents. While important, these events are relatively rare and occur after risk has already materialised.
This project demonstrates a different approach. By analysing near-miss events - including harsh braking and roll stability interventions - it is possible to identify elevated risk earlier and at much greater scale.
These events provide an objective, data-driven view of where vehicles are operating close to safety limits, enabling fleets and road authorities to better understand and respond to real-world risk.
The analysis draws on near-miss data collected from multiple participating fleets over more than 700 days of operation.
Across the project, more than 42,000 near-miss events and approximately 274,000 system alarms were recorded from 303 tracked trailers, providing one of the most comprehensive datasets of its kind in Australia.
Near Misses
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Near Misses per day
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Total Alarms
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Alarms per day
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Fleets
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Tracked Trailers
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Positions Tracked
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Recorded Days
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The map below visualises near-miss events captured across participating fleets. Each marker represents a specific EBS or RSS event, providing a spatial view of where elevated risk is occurring across the road network.
Patterns in these events can highlight high-risk corridors, intersections and operating environments, supporting both fleet-level decision making and broader road safety insights.
Importantly, note that these patterns should be interpreted alongside route exposure. Because the vehicles in this project operate on regular freight corridors, repeated activity on those routes will naturally generate more recorded events than areas where the fleets do not travel.
Each marker represents a recorded near-miss event. Harsh braking events (blue) indicate instances of severe deceleration, while roll stability events (orange) represent system interventions to prevent rollover.
By analysing the spatial distribution of these events, the project provides a practical way to identify high-risk corridors, operational patterns, and potential infrastructure-related risk factors.
Near-miss events provide a direct indication of when vehicles are operating at or near stability limits.
Unlike crash data, these events occur frequently enough and has the potential to provide a more meaningful and scalable evidence base. Furthermore, unlike crash data, they are not subject to the same reporting biases or under-reporting issues, providing a more objective view of where risk is emerging in real-world operations.
An important and unexpected outcome of the project was the identification of faults within RSS systems themselves.
In many cases, persistent alarms or system faults indicated that safety systems were not operating correctly, reducing their effectiveness in preventing rollover events.
This highlights a critical insight: monitoring the health and functionality of safety systems is just as important as analysing the events they generate.
The RollScope platform has demonstrated strong practical value across participating fleets.
Feedback from operators indicates that the platform has improved safety awareness and enabled more informed decision-making at both operational and management levels.
Applying the Pressure
This project extended the telematics data analysis into tyre pressure and temperature data collected from TPMS (Tyre Pressure Management System) equipped trailers.
More than 4.2 million wheel-level records were analysed over the 745 days to establish baseline operating conditions, identify abnormal behaviour, and assess the practical value of TPMS data for safety monitoring.
TPMS data was available only for a small subset of participating trailers and has been used here to provide an indicative view of tyre pressure, temperature and alert behaviour in service.
The analysis focuses on:
Most pressure readings fall within expected operating ranges, with a strong concentration between 87-116 PSI (6–8 bar).
However, a meaningful proportion of readings fall below optimal levels, including a subset of severe under-inflation events that represent elevated risk and operating costs when occurring in-service.
It should be noted that tyre pressure readings are also affected by several environmental and operational factors. The most significant of these is ambient temperature, and pressure will naturally increase as tyres warm during operation and decrease in colder conditions.
Most temperature readings sit within expected operating ranges, with very few elevated temperature events observed.
The dataset shows consistent temperatures, with the majority of readings between 20–60°C. Extreme temperatures associated with tyre-fire risk were not observed in the data, indicating that such events are rare in normal operations.
Tyre temperature readings are also influenced by factors such as ambient temperature, road conditions, and tyre load. Elevated temperatures can indicate potential issues such as under-inflation or excessive friction at the wheel, which may increase the risk of tyre failure if not addressed. No dangerously high temperatures were observed in the dataset, suggesting that extreme overheating events are rare, but this does not diminish the importance of monitoring for high temperature as part of a comprehensive tyre safety strategy.
Inner and outer tyres were observed to differ in pressure and temperature. The comparison below shows that outer tyres run at higher average pressure, while inner tyres have a higher low-pressure rate.
Clear and consistent differences exist between inner and outer tyres on dual-wheel axles.
Outer tyres operate at higher pressures and temperatures, while inner tyres show a significantly higher rate of under-inflation.
Outer tyres run at higher pressures (median 106 psi vs 96 psi), while inner tyres have a higher low-pressure rate. This reflects known operational challenges, where inner tyres are more difficult to inspect and maintain.
Outer tyres also recorded higher temperatures and higher sensor error rates, suggesting that wheel position influences both physical behaviour and data reliability.
An important finding is that the vast majority of TPMS messages are routine system snapshots rather than true alarms.
The charts below show the overall balance between routine messages and actual alarm events, followed by the breakdown of the alarm events themselves.
Less than 0.2% of messages represent genuine alarm conditions, indicating that TPMS systems generate large volumes of data but relatively few high-risk signals.
This highlights a key challenge: without appropriate filtering and configuration, meaningful safety events is difficult to distinguish from normal system noise. Depending on how TPMS systems are configured, the majority of messages (and optionally email notifications) may represent routine snapshots of tyre conditions rather than true safety alarms, which can lead to alert fatigue and reduced attention to critical warnings.
Further, default alert thresholds are often not aligned with specific vehicle configurations, tyre sizes, or operating conditions. As a result, systems may generate alerts that are not directly associated with critical safety risks such as tyre failure or fire.
The map below shows the locations of TPMS events collected aggregated from participating operating fleets transmitting TPMS data.
The markers on the map show the locations of the recorded TPMS events. The grey markers represent routine TPMS messages, while the red markers indicate extreme pressure events and the orange markers over/under pressure events.
The analysis demonstrates that TPMS systems generate high-volume operational data with significant potential safety value.
However, extracting meaningful safety insights and time sensitive alerts requires appropriate filtering, configuration, and interpretation.
Key findings include:
Future work should focus on refining alert thresholds, improving sensor reliability, and developing methods to better distinguish meaningful safety signals from routine operational data.
Context & limitations
The findings presented are based on a subset of available fleet data as sourced from filtered telematics providers and should be interpreted in this context.
While the dataset is large in terms of data points, it represents a limited number of participating fleets and brake systems and may not be fully representative of the broader industry.
These limitations do not diminish the value of the analysis but highlight the importance of careful interpretation and the need for continued data improvement across the industry.