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Comparison of data systems – some useful features

Comparison of data systems – some useful features. Blair Turner, Principal Research Scientist. Overview. Background to project – what was done and why A note on users of crash data Examples of good features from data systems, and what can be achieved Summary / discussion .

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Comparison of data systems – some useful features

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  1. Comparison of data systems – some useful features Blair Turner, Principal Research Scientist

  2. Overview • Background to project – what was done and why • A note on users of crash data • Examples of good features from data systems, and what can be achieved • Summary / discussion

  3. Project aim and method • Funded by Austroads (www.austroads.com.au) • Aim to identify good features from crash data systems • Focus on analysis tools • Literature review, surveys, demonstrations - 20 data systems

  4. Areas of interest • Data entry • Types of data collected • Analysis of crashes – sites of interest • Analysis of crashes – policy level • Mapping • Integration with other data • Quality control issues • Monitoring sites of interest • Dissemination of data

  5. Key user groups Know who the data will be used by and for what purpose. This will help you decide what features you will need in your data system.

  6. Key data users include: • Road safety engineers • Police • Policy makers • Health sector • Researchers • Insurance companies • Motor industry • etc

  7. Road safety engineers • Need data to identify sites to treat • High crash locations (sites, routes, areas) • Crash frequency; crash costs; crash rates • Alarm reports • Need information on problems at sites to allow selection of appropriate treatments • Site summary report • Factor matrix • Collision diagrams • Access to scanned police reports • Need to monitor effectiveness of treatments

  8. Map of crash locations – by severity

  9. Analysis tools – sites of interest

  10. Factor matrix

  11. Collision diagram

  12. Analysis tools – monitoring

  13. Police • Information on crash problem types – speed, alcohol, seatbelt wearing etc • Information on network-wide data • Information on specific locations • Location • Time of day • Day of week

  14. Alcohol crashes by time of day

  15. Risk targeted patrol plan

  16. Policy makers • Information on crash problem types • Information on network-wide data • Not as interested in specific locations

  17. Policy makers

  18. Policy makers

  19. Policy makers

  20. Some other issues • Links to other data important • E.g. traffic volumes, asset data, population data • Quality control • Data entry; training • Dissemination • Access to data; provision of reports

  21. Summary • Know who will be using the data, and how this will be used • Tools can be used to identify high risk sites • Engineers, police, campaigns • Tools used by policy makers – trends, aggregate numbers • Are also other users of data • Expensive process to collect – simple ways to maximise value of this investment

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