Road Safety and Driver Behaviour Research – Attitudinal Studies and Transport Policy Insights

Understanding why people drive the way they do is the single most powerful lever to reduce crashes, injuries and system costs. At Research Bureau we combine robust attitudinal research, rigorous behavioural measurement, and actionable policy analysis to give transport authorities, NGOs, fleet operators, and private sector clients evidence they can act on.

We translate complex datasets into clear policy decisions, targeted interventions, and measurable outcomes. If you want a bespoke proposal, share project details and we’ll provide a detailed quote. Contact us through the contact form, click the WhatsApp icon, or email us at [email protected].

What we do — from attitudes to safer roads

We design and deliver end-to-end research programs that explain driver behaviour, test interventions, and evaluate policy outcomes. Our services include:

Attitudinal surveys to measure knowledge, risk perception, norms and motivations.
Behavioural observation and naturalistic studies to measure real-world driving actions.
Integrated data linkage combining crash data, telematics, enforcement records and demographics.
Policy and cost–benefit analysis that translates findings into targeted recommendations.
Campaign testing and evaluation using experimental and quasi-experimental designs.
Stakeholder engagement and translation — workshops, policy briefs and dashboards.

Each programme is tailored to client goals: reducing fatalities, improving compliance, optimising enforcement, or building the evidence for transport policy change.

Why attitudinal research matters for road safety

Attitudes do not always equal actions, but they shape decisions, norms and reactions to enforcement and infrastructure changes. Well-designed attitudinal work reveals:

Perceived barriers to safe behaviour (e.g., “I’m late”, “the law is unfair”).
Social norms and identity drivers (peer influence, perceived acceptability).
Trust in enforcement and institutions — which affects compliance.
Messaging frames that work or backfire.

Combining attitudes with observed behaviour and contextual data produces far stronger recommendations than single-source studies.

Our approach — rigorous, mixed-methods, policy-focused

We adopt a mixed-methods approach to produce insights that are statistically robust, qualitatively rich and policy-actionable.

Quantitative: Large-scale surveys, telematics, crash data, traffic counts, and experimental field trials.
Qualitative: Focus groups, in-depth interviews, cognitive pre-testing and stakeholder workshops.
Behavioural: Naturalistic driving telemetry, video observation, and on-road hazard perception testing.
Analytical: Advanced statistical modelling, causal inference, and predictive analytics.

All research plans include ethical safeguards, data protection, anonymisation and clear data governance.

Methodologies — detailed and comparative

Below is a concise comparison of core methodologies we use, when to use them, and typical deliverables.

Methodology	When to use	Typical outputs
Attitudinal surveys (national or regional)	Measure prevalence of beliefs, self-reported behaviour and predictors	Weighted survey dataset, factor analysis, segmentation, policy recommendations
Focus groups & interviews	Explore motivations, language, and test messaging	Thematic report, verbatim quotes, messaging guidance
Naturalistic driving studies (telematics/video)	Measure real-world behaviour, near-misses, distraction	Trip-level datasets, video clips, risk maps, machine-learning risk models
Roadside observation	Fast, reliable measure of compliance (seatbelts, speeds, phone use)	Observation dataset, compliance rates, enforcement targets
Discrete choice experiments / conjoint	Test trade-offs (speed vs fuel cost, enforcement vs fines)	Utility estimates, willingness-to-pay, policy trade-off analysis
Randomised controlled trials / A/B tests	Test interventions (messaging, enforcement patterns)	Impact estimates, statistical significance, cost-effectiveness
Interrupted time series / ITS	Assess impact of policy/ campaign over time	Trend analyses, counterfactual estimates, attribution measures
Data linkage & predictive modelling	Identify high-risk locations or driver cohorts	Risk models, GIS hot-spot maps, targeting recommendations

Designing attitudinal surveys: practical details

We design surveys that reduce bias and deliver actionable metrics.

Use validated scales and behavioural predictors (e.g., perceived susceptibility, perceived norm).
Design balanced Likert items and reverse-coded items to reduce response sets.
Apply cognitive interviewing and piloting to ensure local language clarity and cultural relevance.
Use stratified sampling to ensure representation by geography, age, gender, vehicle type and socio-economic status.
Calculate sample size using power analysis to detect expected effect sizes for primary outcomes.

Typical sample sizes (illustrative):

National-level prevalence: 1,000–3,000 respondents.
City-level segmentation: 500–1,200 respondents per city.
Pilot studies: 150–300 respondents.

We weight datasets to adjust for non-response and sampling design, then run cross-tabulations, logistic regression, and latent class analysis to uncover attitudinal segments.

Behavioural measures & naturalistic data

Self-reports are essential, but they must be complemented with behavioural measures.

Telematics and OBD-II data for speed profiles, harsh braking, acceleration, phone usage proxies and route patterns.
Video analytics to measure glance behaviour, seatbelt use and occupant counts using privacy-preserving methods.
Roadside observations for spot compliance checks (seatbelt use, helmet use, mobile phone handling).
In-vehicle sensors to capture near-misses and micro-risk events.

We process large telemetry streams using scalable pipelines, and apply event-detection algorithms to label risky episodes. Data anonymisation and secure storage are standard.

Advanced analytics & causal inference

We combine classical and modern methods to estimate causal impacts and predict risk.

Interrupted Time Series (ITS) and difference-in-differences (DiD) to evaluate policy changes and campaigns.
Propensity Score Matching (PSM) to reduce confounding in observational comparisons.
Multilevel (hierarchical) models to separate individual, vehicle and area-level effects.
Survival analysis for time-to-event risk modelling (e.g., time to first crash after intervention).
Bayesian hierarchical models for small-area estimation and uncertainty quantification.
Machine learning (random forests, gradient boosting, neural nets) for high-dimensional risk prediction and pattern detection.

All models include transparent diagnostics, out-of-sample validation, and interpretability-focused outputs for policy use.

Behaviour change frameworks we use

Interventions are grounded in behavioural science. We commonly apply:

COM-B (Capability, Opportunity, Motivation — Behaviour) to diagnose what needs to change.
Behaviour Change Wheel to select interventions (education, persuasion, incentivisation, coercion, environmental restructuring).
Theory of Planned Behaviour for measuring intent and subjective norms.
Nudge theory for low-cost, scalable behavioural interventions.

These frameworks guide the design of communication campaigns, enforcement strategies and infrastructure interventions.

Data sources & integration

We routinely integrate multiple data streams to build a complete picture:

Police crash databases and incident reports.
Road traffic crash registry and insurance claims.
Traffic volume counts and loop detectors.
Street network and land-use GIS layers.
Weather and daylight records.
Public transport schedules and ridership.
Telematics and fleet management data.
Social media and campaign exposure metrics.

Linkage is done with strict privacy controls and, where necessary, under data-sharing agreements and MOUs.

Translating insights into policy — practical outputs

Research without translation is wasted effort. We provide outputs designed for decision-makers.

Executive policy briefs with clear, numbered recommendations.
Technical reports with methodology appendices and raw outputs.
Economic appraisals and cost–benefit analyses for proposed interventions.
GIS hot-spot maps and prioritisation matrices.
Interactive dashboards and bespoke visualisations for operational teams.
Workshop facilitation and decision-making sessions for implementing agencies.

Below is an example of how deliverables map to stakeholder needs.

Stakeholder	Priority output	How we help
City traffic engineers	Hot-spot maps, infrastructure priorities	Prioritise low-cost treatments vs capital projects
Road safety NGOs	Campaign evaluation and messaging guidance	Evidence-based communications and targeting
Transport policy makers	Cost–benefit of enforcement or new laws	Budgetary arguments and implementation roadmap
Fleet managers	Driver risk dashboards and training modules	Ongoing monitoring and targeted coaching
Insurers	Risk segmentation and predictive scores	Pricing and loss prevention strategies

Illustrative (anonymised) case examples

Note: The following are anonymised and illustrative to show typical outcomes and deliverables.

Urban Speed Management Pilot — We combined roadside radar observations, resident attitudinal surveys, and ITS analysis to prioritise 12 corridors. The pilot included signage, targeted enforcement and community messaging. Deliverables: corridor risk scorecards, compliance targets and a sustainability plan for scaling.
Fleet Behaviour Programme — Using OBD telematics and anonymised driver feedback surveys, we identified three high-risk behaviours (harsh braking, late-night trips, phone interaction). Deliverables: personalised coaching modules, KPI dashboards and quarterly risk-reduction targets.
Seatbelt Compliance Campaign — A city-wide attitudinal baseline and roadside observation programme guided a year-long multimedia campaign. Evaluation was via repeated roadside checks and ITS on fatal and serious injury trends. Deliverables: campaign toolkit, evaluation report and stakeholder training.

Measuring impact — KPIs and evaluation metrics

We recommend a combination of process, outcome and impact metrics.

Process metrics:
- Reach and frequency of campaign messages.
- Number of enforcement hours or checkpoints.
- Stakeholder engagement sessions completed.
Outcome metrics:
- Changes in self-reported behaviours and attitudes.
- Compliance rates from roadside observation (seatbelt, helmet, phone).
- Average and 85th percentile speeds.
Impact metrics:
- Crash rates (per 100,000 population or per vehicle-km).
- Fatalities and serious injuries (F&SI) trends.
- Cost savings from prevented crashes (direct and indirect).

We also calculate return on investment (ROI) and benefit-cost ratio (BCR) for policy options to support prioritisation.

Ethics, privacy & data governance

Ethical practice is central to our work.

Informed consent for surveys and naturalistic studies.
Anonymisation and de-identification of personal data.
Secure storage, access controls and data minimisation.
Compliance with local data protection regulations and client data policies.
Independent ethics review on request for sensitive projects.

We never publish identifiable personal data and follow industry best practices for video and telematics data handling.

Typical project timeline and phases

A well-structured project moves through defined phases. Timelines are indicative and scaled to project scope.

Phase 1 — Project specification and sampling plan: 2–4 weeks.
Phase 2 — Instrument design, piloting and approvals: 3–6 weeks.
Phase 3 — Data collection (surveys/observations/telemetry setup): 4–12 weeks.
Phase 4 — Data processing and analysis: 4–8 weeks.
Phase 5 — Reporting, stakeholder workshops and policy translation: 2–6 weeks.

Smaller pilot projects can be completed in 8–12 weeks; national studies typically run 4–6 months. We provide a detailed timeline and resource plan in every quote.

Pricing model & what affects cost

We provide customised quotes. Major cost drivers include:

Sample size and geographic spread.
Use of naturalistic vehicle instrumentation or video analytics.
Complexity of data linkage and cleaning.
Depth of analysis (e.g., causal inference vs descriptive).
Delivery formats (dashboards, workshops, bespoke training).
Time-sensitive or accelerated schedules.

Share your project brief and constraints and we’ll return a transparent estimate with options to scale scope and costs.

How we report — clear, evidence-led outputs

All deliverables are designed for decision use:

Clear executive summaries with 3–5 priority recommendations.
Visual evidence — infographics, risk maps, and dashboard-ready tables.
Technical appendix with full methods, code and data dictionaries.
Policy translation sessions to convert evidence into action plans.
Optional training for client teams on sustained monitoring.

We follow the “one-page decision brief” principle for senior officials and provide deeper technical packages for analysts.

Implementation support and capacity building

We don’t just hand over a report. We offer:

Implementation roadmaps with timelines and responsibilities.
Help to design enforcement schedules and evidence-based patrol plans.
Training for local analysts on dashboards and model maintenance.
Guidance on procuring telematics or video analytics systems.

Building local capacity ensures sustained improvement after the research ends.

Common research questions we solve

Why is seatbelt/helmet use low in certain locations despite enforcement?
Which driver cohorts are most likely to reoffend after penalties?
What messaging reduces drink-driving or mobile-phone use?
Which corridors would benefit most from speed humps vs enforcement?
How do socioeconomic factors affect exposure and risk?
What is the estimated impact of proposed policy changes on F&SI?

If your question isn’t listed, share it — we’ll propose a tailored study design.

Practical examples of interventions we evaluate

Targeted enforcement schedules using hot-spot predictive models.
Behavioural advertising tested with randomized online experiments.
Automated speed enforcement placement optimisation by cost–benefit.
Graduated licensing policy simulations using cohort models.
School-zone safety programmes with multi-stakeholder monitoring.

Each intervention is evaluated on effectiveness, equity impacts and sustainability.

What sets Research Bureau apart

Multidisciplinary team: transport planners, statisticians, behavioural scientists, GIS specialists and policy analysts.
Proven methodologies: mixed-methods designs grounded in peer-reviewed frameworks.
Policy translation focus: every output connects evidence to clear actions.
Operational support: dashboards, workshops and capacity-building for sustained change.
Local context expertise: experience with municipal and national stakeholders and an understanding of regulatory environments.

Our reputation is built on delivering clear, actionable evidence that decision-makers can implement.

Sample deliverables and formats

Policy brief (1–4 pages)
Full technical report (50–200+ pages)
Interactive web dashboard (live)
GIS risk maps (shapefiles/geoJSON)
Raw and cleaned datasets with metadata
Workshop packages and training materials

We can adapt outputs to client needs: PDFs, PowerPoint, or platform integrations.

Next steps — how to get started

Share a short project brief: objectives, timeline, budget and data availability.
We prepare a tailored proposal with methodology, timeline and fixed-price quote.
On agreement we begin with a project initiation workshop to align stakeholders.

To get a quote, please use the contact form, click the WhatsApp icon on the page or email [email protected] with project details.

FAQ — quick answers

Q: Can you work with limited budgets?
A: Yes. We offer tiered options, phased delivery and small pilots that scale.
Q: Do you handle data-sharing agreements?
A: Yes. We draft and manage MOUs and data governance frameworks.
Q: Will you share raw data?
A: We provide cleaned, documented datasets under agreed terms and safeguards.
Q: Can you integrate with our internal dashboards?
A: Yes. We deliver APIs, CSV exports or direct dashboard integration depending on client systems.

Illustrative cost–benefit logic (example)

We estimate likely benefits using conservative assumptions and sensitivity analysis. Below is a simplified illustrative table showing how an intervention’s impact is evaluated.

Element	Baseline	Post-intervention (example)	Notes
Annual serious crashes	120	96	20% reduction scenario
Average societal cost per serious crash (incl. loss of productivity)	R600,000	R600,000	Conservative estimate
Annual cost savings	—	R14,400,000	24 fewer crashes x cost
Intervention annual cost	—	R2,000,000	Programme + enforcement
Benefit-cost ratio (BCR)	—	7.2x	Illustrative only; full analysis provided in proposal

We tailor economic analyses to local cost data and include sensitivity ranges.

Ready to move from insight to impact?

Share your project brief and constraints, and we’ll prepare a detailed proposal with timelines, methodology and a transparent quote. Contact us via the contact form, click the WhatsApp icon, or email [email protected].

We look forward to helping you design evidence-led, cost-effective interventions that reduce risk and save lives.

Contact details

Email: [email protected]
Contact form: [Use the contact form on this page]
WhatsApp: Click the WhatsApp icon to start a direct chat

Provide brief project details (objectives, scale, timeline and available data) and we’ll respond promptly with a proposal and next steps.