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Victoria Pyta ARRB Group Disadvantage and Road Safety.

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Presentation on theme: "Victoria Pyta ARRB Group Disadvantage and Road Safety."— Presentation transcript:

1 Victoria Pyta ARRB Group Disadvantage and Road Safety

2 Contents About this project Overview of literature review Preliminary findings Next steps

3 PROJECT OVERVIEW Background Objectives Definition of disadvantage

4 Background  Austroads project SS1761 (2012 – 2015) – Literature review – Data analysis and modelling – Consultation  Project Team: – Project Technical Leader: Victoria Pyta (ARRB) – Project Manager: Anita Baruah (VicRoads) – Quality Manager: Dr Peter Cairney (ARRB)

5 Project Objectives 1. Determine how the incidence, severity, nature and location of crash involvement varies between persons from more versus less disadvantaged areas. 2. Gain an understanding of how road environment, vehicle and behavioural factors are related to crash likelihood and severity outcomes among persons from disadvantaged areas. 3. Recommend evidence-based strategies for addressing the issues that are identified through this process.

6 What is disadvantage?  What is disadvantage? – Low income relative to others and/or expenditure on necessities – Barriers to education, social opportunities or work – A ‘relative’ and ‘multi-dimensional’ concept  How is disadvantage related to road safety? – Socio-economic disadvantage is associated with higher injury rates due to transport-related injuries.

7 LITERATURE REVIEW Effects of disadvantage on road trauma Factors that are associated with both disadvantage and road trauma Interventions

8 Victoria, Australia Victorian hospital data (2005 to 2007) Persons assigned to LGA of residence LGA ranked by Index of Relative Social Disadvantage (IRSD) Victorian Injury Surveillance Unit Transport injuries represent 14% of all injuries in the hospital admissions data Persons with greatest risk come from the 2 nd and 3 rd quintiles

9 New South Wales, Australia  Remoteness and low SES associated with increased risk of death among young drivers Rural fatalities  Higher posted speed limits  Fatigue  Drink-driving  Seatbelt non-use Low SES fatalities  Higher posted speed limits  Fatigue  Driving an older vehicle

10 Indigenous populations of Australia and New Zealand Australia  Drink driving  Unlicensed driving  Remoteness amplifies problems and accounts for much of the disparity New Zealand  Among most severely disadvantaged  High road fatality rate compared to non-indigenous populations  Cultural and language differences  Over-representation is particularly strong among year olds  Disparities persist after accounting for differences in SES

11 International  Many studies (UK, Europe, Israel, USA)  Disadvantage associated with higher risk, particularly for child pedestrians  Concomitant factors: – Environmental, e.g. location (especially remoteness), exposure – Behavioural factors, e.g. unlicensed driving, drug and alcohol use – Socio-cultural factors, i.e. peer group and culture – Personal factors, e.g. health, self-efficacy

12 Existing interventions  Low income earners (registration discounts and discounts on drink drive programs)  Indigenous communities (wide range)  CALD communities (translation, education and awareness raising, licensing assistance)  Young drivers (supervised practice, first car safety)  Children (proper restraint use and early childhood road safety education)  Engineering treatments  Enforcement and diversionary programs  Partnerships and community engagement

13 DATA ANALYSIS Data sources Results so far (exploratory descriptive analysis) Next steps, methods and data sources

14 Data sources (Australia)  Crash data with postcode of crash involved persons – Vic, NSW, SA – NZ (needs to be geocoded)  SES data – ABS Index of Relative Social Disadvantage (IRSD)  Remoteness data – ABS remoteness index  Potential for inclusion of travel survey data – e.g. ABS Survey of Motor Vehicle Use (SMVU)

15 Index of Relative Social Disadvantage (Australia)  Takes into account: – Income – Household occupancy – Vehicle ownership – Illness and disability – % of residents speaking LOTE – % of residents of indigenous origin – Etc.

16 Preliminary results (drivers and riders, Victoria)

17 Preliminary results (drivers and riders, NSW)

18 Preliminary results (drivers and riders, SA)

19 South Australia (All road users, 10 years) IRSD Quintile Population (2006) Number of personsRate per 100,000 population Killed Seriously Injured TotalKilled Seriously Injured Total Q Q Q Q Q TOTAL

20 YEARS 2 AND 3 Looking forward

21 Remainder of 2013 – Modelling  Develop model for crash risk associated with SES that takes into account: – Demographic profile of area – Remoteness – Environmental factors (e.g. speed limits) – Individual demographic factors (age, gender etc.) – Behavioural factors (e.g. restraint use) – Other explanatory factors (e.g. vehicle age)

22 2014/15  detailed consultation regarding the operation of programs for disadvantaged groups or locations  develop recommendations for actions to address these issues

23 Acknowledgements  Data providers in road agencies  SS1761 Project Team: – Dr Peter Cairney, Principal Behavioural Scientist (ARRB) – Anita Baruah, Senior Policy Analyst, Road Safety and Network Access (VicRoads)  Project steering committee  Supervisor – Dr Lyndon Walker, Swinburne University


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