Research & Development Project

Roads designed for
human experience

Road UX introduces a new capability for assessing how road environments and journey conditions influence heavy vehicle driver experience — and what that means for safety.

210
Lives lost in heavy vehicle
crashes (12 months to Sep 2025)
$27B
Annual social cost of
road crashes in Australia
~18%
Of road crash deaths
involve heavy vehicles

The road safety gap we're not measuring

Road UX: A Driver Experience Audit for Roads

The heavy vehicle industry remains a major focus of road safety policy, due to the severity and societal impact of crashes involving large freight vehicles. According to the Australian Government's National Road Safety Data Hub, 210 people died in crashes involving heavy vehicles in the 12 months to September 2025, representing an increase of 14.8% compared with the previous year (Department of Infrastructure, Transport, Regional Development, Communications, Sport and the Arts, 2025). National road safety strategy materials also note that heavy vehicles are involved in approximately 18% of road crash deaths in Australia, despite representing a smaller proportion of the vehicle fleet (National Road Safety Strategy, n.d.).

Beyond the human toll, road trauma carries significant economic consequences for the Australian population. The Bureau of Infrastructure and Transport Research Economics (BITRE) estimates the annual social cost of road crashes in Australia at approximately $27 billion, demonstrating the scale of the safety and economic burden associated with road incidents across the network (BITRE, 2022).

210
Lives lost in heavy vehicle crashes (12 months to Sep 2025)
+14.8%
Year-on-year increase in heavy vehicle crash fatalities
$27B
Annual social cost of road crashes in Australia
~18%
Of road crash deaths involve heavy vehicles

These figures highlight the ongoing need to strengthen safety outcomes across heavy vehicle operations. While progress has been made through improvements in vehicle design, regulatory frameworks, and road engineering, current approaches still focus primarily on infrastructure compliance, hazard identification, and crash history analysis.

However, these approaches do not systematically account for how roads and journeys are experienced by drivers over time, even though driver experience sits at the centre of many of the human factors that directly and indirectly impact safety outcomes.

Driver Experience as a Key Determinant of Safety Performance

Driver experience is not a soft or peripheral issue. It is the lived human experience of operating a vehicle across a journey, and emerges from the interaction between the driver, the vehicle, the road, the job, and the wider journey context. Research in transport psychology and human factors emphasises that driving performance stems from this interaction between the driver, the driving task, and the surrounding environment, rather than from any single factor alone (May & Baldwin, 2009; Bates et al., 2019).

In practice, driver experience is shaped by a combination of physical, cognitive, emotional, and perceptual factors. These include driver workload, attentional demand, environmental stimulation, traffic complexity, perceived safety, fatigue, and the overall effort required to maintain vigilance and respond to changing conditions (May & Baldwin, 2009; Thiffault & Bergeron, 2003). In other words, driver experience is an emergent property of the whole driving situation, rather than a single variable.

This matters because drivers do not respond only to isolated hazards or formal compliance requirements. They respond to how the driving task feels and unfolds over time. When the experience of driving is undermined by the nature of the job, road conditions, operational pressures, or the structure of the journey itself, this can create the conditions for a range of human factors issues that weaken safety performance. These include fatigue, distraction, cognitive overload, cognitive underload, reduced vigilance, lower motivation, poorer decision-making, attentional drift, and delayed responses to changing conditions (May & Baldwin, 2009; Thiffault & Bergeron, 2003).

From this perspective, driver experience is fundamental to safety. Yet road design and road safety audit processes still tend to focus more narrowly on standards, compliance, discrete hazards, and crash history than on the holistic experience of driving across a corridor or journey.

Research consistently shows that driver performance during long-haul operations is shaped not only by compliance and physical road design, but by how the driving environment interacts with the cognitive and psychological demands of the task. This existing literature shows that driver experience is influenced by multiple interacting factors that in turn affect risk of incidents, including:

Operational demands, journey conditions, and driver experience

Driver performance is influenced not only by compliance factors such as work hours or rest breaks, but by the broader operational context in which driving occurs. Heavy vehicle drivers often operate in conditions characterised by long journeys, variable traffic environments, and sustained task demands that shape how the driving task is experienced over time. Research indicates that high-risk fatigued driving among truck drivers is associated with factors such as longer working hours, poor sleep quality, and psychosocial stressors (Ren et al., 2023). These findings highlight that driver performance is shaped by a combination of physiological, psychological, and environmental influences across the course of a journey, reinforcing the need for methods that can systematically assess how road environments and journey contexts contribute to driver experience and safety outcomes.

Driver workload and experiential pressures during driving tasks

Driving performance is also influenced by the level of cognitive and perceptual workload experienced during the driving task. An Australian study of freight transport drivers found that 38% reported experiencing fatigue at least weekly while driving for work, while 45% reported that they had nodded off while driving in the previous 12 months (Friswell & Williamson, 2008). Importantly, longer work hours and higher subjective workload were significant predictors of fatigue experiences. These findings suggest that workload and experiential pressures encountered during driving (including factors such as prolonged monotony, variable traffic conditions, and sustained cognitive demand) can influence vigilance and driver performance. This reinforces the importance of understanding how different road environments and journey segments contribute to the overall experience of driving and the behavioural risks that may emerge over time.

These findings help demonstrate that driver safety outcomes are shaped by how the driving task is experienced across the course of a journey, more than by isolated factors alone.

The Role of Road Environments in Shaping Driver Experience

A growing body of research emphasises that road environments themselves influence driver vigilance, workload, and behavioural performance. Driving environments shape the cognitive and perceptual demands placed on drivers, influencing how the driving task is experienced across the course of a journey.

Studies examining driver cognition demonstrate that increasing environmental complexity (such as dense signage, traffic interactions, or visually complex road environments) can significantly increase drivers' cognitive workload and influence driving performance (Lyu et al., 2019).

These road environments have been found to influence driver experience through both underload and overload conditions. May and Baldwin (2009) note that task-related driving fatigue and performance deterioration can arise from two distinct forms of workload imbalance:

Underload conditions
Low-stimulation environments such as long, monotonous road segments can reduce cognitive engagement, leading to reduced vigilance and attentional disengagement. Research has shown that monotonous roadside environments are associated with greater fatigue and reduced alertness during extended driving tasks (Thiffault & Bergeron, 2003).

Overload conditions
Complex driving environments with dense traffic, high information demands, or unpredictable interactions increase cognitive demand and mental workload. Experimental research examining highway environments has shown that increases in traffic sign information and environmental complexity significantly elevate drivers' cognitive workload and can affect driving performance (Liu et al., 2019).

Research examining driver distraction and attention management further highlights how environmental and task demands compete for limited cognitive resources. Drivers must continuously allocate attention between the primary task of driving and competing demands in the driving environment, and increases in attentional demand can negatively affect performance and safety outcomes (Bates et al., 2019).

The existing empirical literature additionally highlights specific characteristics of road environments that contribute to driver alertness and behavioural performance. Key findings include:

Monotony and vigilance decline

Driving simulator studies show that monotonous roadside environments are associated with increased fatigue and reduced vigilance during extended driving (Thiffault & Bergeron, 2003).

Road geometry and driving performance

Research examining different road environments found that fatigue symptoms and performance deterioration varied across road types, with prolonged straight road segments associated with declining lane maintenance and steering quality (Oron-Gilad & Ronen, 2007).

Road design and driver engagement

Studies have also found that increased geometric variety in otherwise monotonous environments can improve vigilance and reduce fatigue-related performance deterioration (Farahmand & Boroujerdian, 2018).

Together, these findings highlight that road environments influence the cognitive and perceptual experience of driving, shaping vigilance, workload, and decision-making performance across the duration of a journey. Understanding these experience-related pressures is therefore critical for identifying road segments and corridor conditions that may contribute to behavioural risk.

Recognition of Driver Workload in Road Design Guidance

Australian road design guidance already recognises the relevance of driver workload and cognitive demand, although these concepts are not yet systematically incorporated into road safety assessment tools.

For example, the Road Planning and Design Manual (2nd ed.) published by the Queensland Department of Transport and Main Roads emphasises that driver workload has a direct impact on driving performance and that road environments should be designed to maintain appropriate levels of driver engagement and avoid abrupt increases in cognitive demand (Department of Transport and Main Roads Queensland, 2025). Guidance within the manual notes that environments that are either overly monotonous or excessively complex can negatively influence driver performance by reducing vigilance or overloading drivers with information.

Austroads guidance similarly recognises driver workload and human factors as relevant to road environment safety management and road design practice (Austroads, 2017). Despite this recognition, road safety audit processes remain primarily focused on infrastructure compliance, discrete hazards, and crash history (Austroads, 2022).

The Remaining Industry Gap

Although human factors are increasingly acknowledged in road design guidance, a significant industry gap remains. Currently:

Road safety assessments do not systematically measure the impact of the journey itself on driver experience, even though this experience influences vigilance, workload, and behavioural performance.

Infrastructure evaluation does not routinely incorporate human factors science in a structured and scalable diagnostic way.

Existing data systems for heavy vehicle safety remain limited, and researchers have called for enhanced data collection and integration across driver, vehicle, carrier, and environmental factors to better understand fatigue-related risk (Stern et al., 2019).

As a result, road networks continue to be evaluated primarily through infrastructure design standards, engineering compliance and historical crash statistics, rather than through a systematic understanding of how journey environments shape driver experience and behaviour across long-haul operations.

Addressing the Need: Road UX

The Road UX project responds directly to this need established through the existing literature and research.

By establishing a Road Experience Audit Framework and developing an AI-enabled diagnostic toolkit, the project introduces a structured, data-driven method for analysing how road environments and journey contexts influence driver experience.

This capability will enable the heavy vehicle industry to:

Identify road segments associated with elevated vigilance degradation or cognitive workload.

Detect experience-related pressures before incidents occur.

Support more proactive infrastructure planning and intervention.

Complement existing hazard audits and crash analysis with experience-based diagnostics.

By making driver experience measurable and diagnosable, this project introduces a new layer of safety intelligence capable of supporting safer freight operations across the wide variety of our road networks.

References

Austroads. (2017). Guide to Traffic Management Part 13: Road Environment Safety. https://austroads.gov.au/publications/traffic-management/agtm13

Austroads. (2022). Guide to Road Safety Part 6: Road Safety Audit. https://austroads.gov.au/publications/road-safety/agrs06

Bates, L., Alexander, M., van Felius, M., Seccombe, J., & Bures, E. (2021). Final report: What is known about distracted driving? Griffith Criminology Institute; Australian Automobile Association. https://research-repository.griffith.edu.au/items/5786b9da-8e0c-4745-abc9-50a0d96d2b9e

Bureau of Infrastructure and Transport Research Economics. (2022). Social cost of road crashes. https://www.bitre.gov.au/publications/2022/social-cost-road-crashes

Department of Infrastructure, Transport, Regional Development, Communications, Sport and the Arts. (2025). Quarterly heavy vehicle road deaths. National Road Safety Data Hub. https://datahub.roadsafety.gov.au/safe-systems/safe-vehicles/quarterly-heavy-vehicle-road-deaths

Queensland Department of Transport and Main Roads. (2025). Road Planning and Design Manual (2nd ed.). Brisbane: Queensland Government, Department of Transport and Main Roads. https://www.tmr.qld.gov.au/business-industry/technical-standards-publications/road-planning-and-design-manual-2nd-edition

Farahmand, B., & Boroujerdian, A. M. (2018). Effect of road geometry on driver fatigue in monotonous environments: A simulator study. Transportation Research Part F: Traffic Psychology and Behaviour, 55, 50–62. https://doi.org/10.1016/j.trf.2018.06.021

Friswell, R., & Williamson, A. (2008). Exploratory study of fatigue in light and short haul transport drivers in NSW, Australia. Accident Analysis & Prevention, 40(1), 410–417. https://doi.org/10.1016/j.aap.2007.07.009

Lyu, N., Xie, L., Wu, C., Fu, Q., & Deng, C. (2017). Driver's Cognitive Workload and Driving Performance under Traffic Sign Information Exposure in Complex Environments: A Case Study of the Highways in China. International Journal of Environmental Research and Public Health, 14(2), 203. https://doi.org/10.3390/ijerph14020203

May, J. F., & Baldwin, C. L. (2009). Driver fatigue: The importance of identifying causal factors of fatigue when considering detection and countermeasure technologies. Transportation Research Part F: Traffic Psychology and Behaviour, 12(3), 218–224. https://doi.org/10.1016/j.trf.2008.11.005

National Road Safety Strategy. (2021). Fact sheet: Heavy vehicle safety. https://www.roadsafety.gov.au/nrss/fact-sheets/heavy-vehicle-safety

Oron-Gilad, T., & Ronen, A. (2007). Road characteristics and driver fatigue: A simulator study. Traffic Injury Prevention, 8(3), 281–289. https://doi.org/10.1080/15389580701354318

Ren, X., He, J., Liu, J., Maple, M., Duncanson, K., Tynan, R., & Du Plessis, V. (2023). Factors associated with fatigued driving among Australian truck drivers: A cross-sectional study. International Journal of Environmental Research and Public Health, 20(4), 3350. https://doi.org/10.3390/ijerph20043350

Stern, H. S., Blower, D., Cohen, M. L., Czeisler, C. A., Dinges, D. F., Greenhouse, J. B., Guo, F., Hanowski, R. J., Hartenbaum, N. P., Krueger, G. P., Mallis, M. M., Pain, R. F., Rizzo, M., Sinha, E., Small, D. S., Stuart, E. A., & Wegman, D. H. (2019). Data and methods for studying commercial motor vehicle driver fatigue, highway safety and long-term driver health. Accident Analysis & Prevention, 126, 37–42. https://doi.org/10.1016/j.aap.2018.02.021

Thiffault, P., & Bergeron, J. (2003). Monotony of road environment and driver fatigue: A simulator study. Accident Analysis & Prevention, 35(3), 381–391. https://doi.org/10.1016/S0001-4575(02)00014-3

Useche, S. A., Gómez, V., Cendales, B., & Alonso, F. (2021). More than just "stressful"? Testing the mediating role of fatigue on the relationship between job stress and occupational crashes of long-haul truck drivers. Psychology Research and Behavior Management, 14, 1211–1221. https://doi.org/10.2147/PRBM.S305687

See the audit in action

Explore the Road Experience Audit prototype below. Click road segments or draw areas on the map to generate experience impact scores, domain breakdowns, behavioural risk indicators, and cumulative load profiles.

roadsense — melbourne corridor audit
Interactive

A new diagnostic layer
for road safety

Road UX combines human factors science, behavioural research, data integration, and AI-enabled diagnostics to make driver experience measurable and actionable.

01

Road Experience Audit Framework

A structured method for assessing how corridors and journey segments shape driver experience — across monotony, workload, attentional demand, perceptual complexity, and more.

02

AI-Enabled Diagnostic Engine

An interpretable modelling approach that links road attributes and journey conditions with behavioural indicators — generating experience impact scores grounded in evidence.

03

Practitioner-Facing Platform

A prototype digital tool with maps, dashboards, and corridor comparison views — designed so non-technical stakeholders can interpret and act on experience-related risk insights.

What shapes the driving experience?

Driver experience emerges from the interaction between the driver, the road, the vehicle, the job, and the journey context. These are the key dimensions the framework will assess.

Monotony Exposure

Low-stimulation road segments reduce cognitive engagement, leading to vigilance decline and attentional disengagement over extended driving periods.

Perceptual Demand

Complex environments with dense signage, traffic interactions, and visual clutter increase cognitive workload and affect driving performance.

Workload Transitions

Abrupt shifts between low- and high-demand environments create critical vulnerability points where drivers must rapidly recalibrate.

Interaction Intensity

Traffic complexity, intersection frequency, and multi-modal interactions compete for limited cognitive resources and increase decision-making load.

Road Geometry

Straight segments degrade steering quality and lane maintenance, while geometric variety can improve vigilance and reduce fatigue-related performance decline.

Journey Context

Time of day, cumulative drive time, rest history, operational pressures, and environmental conditions all shape the experience envelope of a journey.

Three pillars of capability

The project builds three interlocking capabilities that together create a new layer of safety intelligence for the heavy vehicle sector.

01

Establish the Road Experience Audit Framework

Develop the conceptual and practical foundation for assessing driver experience across road corridors and journey segments — including descriptors, journey context variables, driver-state indicators, and audit logic.

02

Develop an AI-Enabled Diagnostic Toolkit

Engineer an interpretable modelling approach that links road and journey characteristics with behavioural indicators and generates meaningful experience-related risk outputs.

03

Deliver a Practitioner-Facing Digital Platform

Integrate the framework and modelling components into a user-facing prototype capable of generating experience impact scores, behavioural indicators, maps, and corridor comparison views.

Two years to a new capability

From evidence and framework design, through data integration and AI modelling, to prototype build, industry testing, and final delivery.

Year 1 · 2026–2027
Stage 1

Framework Definition & Research

Literature review, core descriptor development, taxonomy of road segments and driver-state indicators, industry consultation with fleet operators and road authorities.

Dec 2026 → Apr 2027
Stage 2

Data Integration & Dataset Development

Road network dataset integration, behavioural indicator structuring, data validation protocols, and establishment of a foundational modelling-ready dataset.

Mar 2027 → Aug 2027
Stage 3

AI Model Development

Experience-response modelling framework, initial model training, corridor testing, impact scoring refinement, and behavioural risk indicator development.

Jul 2027 → Feb 2028
Year 2 · 2027–2028
Stage 4

Digital Platform Development

Platform architecture, UI design, diagnostic system integration, experience impact maps, dashboards, and corridor comparison functionality.

Dec 2027 → May 2028
Stage 5

Validation & Industry Testing

Pilot corridor assessments with industry partners, usability feedback, framework and model refinement, prototype improvement.

Jun 2028 → Sep 2028
Stage 6

Final Outputs & Industry Dissemination

Final framework documentation, methodology and guidance, published dataset structure, finalised prototype, and project report delivery to NHVR and stakeholders.

Sep 2028 → Nov 2028

A multidisciplinary collaboration

The project brings together behavioural science, human factors, road safety engineering, data integration, and digital development expertise.

Project Lead

Opposite

Overall project oversight, stakeholder engagement, design integration, project management, and delivery coordination. Specialists in human experience, human-centred design, and behavioural science.

Research Partner

Murdoch University

Research collaboration and technical support in research design, data integration, evaluation, and modelling development.

Advisory Partner

Safe System Solutions

Specialist expertise in road safety engineering, infrastructure assessment, and safe system thinking.

The people behind Road UX

A multidisciplinary team spanning behavioural science, human factors, road safety engineering, autonomous systems, fleet operations, and human-centred design.

Opposite
Dr Nicholas Duck
Project Oversight & Design Lead

Dr Nicholas Duck

Overall project oversight, strategic direction, and design leadership across all workstreams. Ensures the project aligns with NHVR's objectives, guides key decisions, and maintains coherence across the behavioural, technological, and operational elements of the work.

Ashleigh Fleming
Project Manager & Partner Engagement Lead

Ashleigh Fleming

Leads project management across the full lifecycle, from planning through to delivery. Coordinates day-to-day activities, manages timelines, risks, and reporting, and acts as the primary liaison with Murdoch University, project partners, and specialist contributors.

Damien Colabattista
Human-Centred Design & Driver Experience Lead

Damien Colabattista

Leads the behavioural and cognitive design of the framework, digital tools, industry resources, and training elements. Ensures all outputs are practical, intuitive, and grounded in the realities of heavy vehicle operations.

Luke Carroll
Technology & Innovation Lead

Luke Carroll

Leads the technology workstream for Opposite, overseeing solution design, usability, and integration of the digital components. Acts as the primary technical interface between Opposite and Murdoch University.

Murdoch University
Dr Amirmehdi Yazdani
Autonomous & Safety-Critical Systems

Dr Amirmehdi Yazdani

Senior Lecturer in Electrical Engineering with expertise in autonomous systems, control systems, and artificial intelligence. Extensive experience in designing and prototyping advanced technologies for transportation, automation, and safety-critical systems, including intelligent anti-sway stabilisation for heavy articulated vehicles.

Professor Farhad Shahnia
Power, Electric Vehicles & Intelligent Control

Professor Farhad Shahnia

Professor of Electrical Engineering specialising in power electronics, electric vehicles, artificial intelligence, and optimisation. Deep expertise in power management and intelligent control strategies that enhance the safety and efficiency of heavy vehicles.

Associate Professor Hai Wang
Control Systems, Automation & Vehicle Safety

Associate Professor Hai Wang

Associate Professor of Electrical Engineering specialising in control and autonomous systems and Industry 4.0. Research focuses on vehicle safety and efficiency, including driver fatigue detection, steer-by-wire technology, and intelligent vehicle control. Recipient of the 2023 Murdoch University Vice Chancellor's Excellence Award in Research.

Safe System Solutions
Kenn Beer
Principal Engineer

Kenn Beer

A leading practitioner in Safe System–aligned road safety engineering in Australia. Chartered and Registered Professional Engineer (RPEng, RPEQ, RPEV) with extensive experience working with road authorities, local government and industry. Under Kenn's leadership, Safe System Solutions has conducted more than 1,500 Road Safety Audits across Australia, including audits in every jurisdiction.

Jamie Robertson
Road Design & Heavy Vehicle Operations

Jamie Robertson

Senior road design specialist working alongside Kenn on road safety and Safe System projects. A highly experienced highway designer and technical specialist, and formerly the VicRoads Head of Traffic Engineering and Design and licensed heavy vehicle driver.

Specialist Contributors
Ben Bailey
Fleet Operations Expert

Ben Bailey

Draws on his experience in fleet operations with SCT Logistics to advise on day-to-day operational realities, implementation constraints, and the practical usability of project outputs in live freight settings.

Professor Simon Moss
Research Design Specialist

Professor Simon Moss

Expert guidance on research and evaluation design, drawing on many years of experience leading projects across Monash, Charles Darwin, and Wollongong universities. Ensures methodological rigour and meaningful assessment of project impact.

Lex Hanegraaf
General Manager, HSEQ at Built Environs

Lex Hanegraaf

Executive leader with over 18 years' experience managing safety, risk and quality across major construction and infrastructure projects used by heavy vehicles. Chair of the South Australian Construction Safety Alliance (SACSA).

Sanzid Ahmed
Group Executive People, Capability & Safety at ANC Delivers

Sanzid Ahmed

A seasoned HR leader with a strong track record of shaping people strategies that drive transformation, performance, and long-term growth across Australia, Asia, and Europe. Focuses on building future-ready, inclusive organisations through organisational design, cultural transformation, and leadership development.

Danica Pilgrim-Younger
National Safety Manager, Sadleirs

Danica Pilgrim-Younger

Broad experience from senior Health, Safety and Environment roles across a range of industries. A strong advocate for continual improvement in safety systems and management. Has played a key role in securing three innovative safety grants for the transport and logistics sector and has led internal transformation projects across safety systems, training, and risk assessment.

Anthony Fewster
GM Zero Harm at Downder

Anthony Fewster

Extensive experience in safety, security, and operational leadership across transport, infrastructure, and public sector environments. A strong advocate for practical safety culture and frontline engagement. Has led initiatives to strengthen risk management, workforce awareness, and system-wide safety performance across complex operational settings.

Who benefits from Road UX?

The project creates value across the heavy vehicle safety ecosystem — from infrastructure planning to on-road outcomes for drivers and other road users.

Direct Beneficiaries

Road authorities and infrastructure planners

Fleet operators and safety leaders

Road safety practitioners

Regulators and policy stakeholders

Human factors and transport researchers

Indirect Beneficiaries

Heavy vehicle drivers — through better-informed interventions and route design

Other road users — through improved freight corridor safety

Future researchers and technology developers — through access to a new framework and method

What the project will produce

The long-term value sits not only in the prototype itself, but in the underlying framework, logic, and reusable knowledge base created through the work.

1

Road Experience Audit Framework describing the structure, dimensions, and logic of driver experience assessment

2

Taxonomy of road segment types, journey context variables, and driver-state indicators

3

Foundational integrated dataset linking road characteristics, journey variables, and behavioural indicators

4

Interpretable modelling approach for estimating experience-related pressures and behavioural risk

5

Prototype digital platform with experience impact scoring and corridor visualisation

6

Guidance materials, methodology documentation, and final project report

Let's build the future of road safety together

Road UX is a practical industry innovation with the potential to strengthen infrastructure planning, corridor prioritisation, and safety management across the freight task. We welcome engagement from industry partners, road authorities, and safety practitioners.