driver licences. This means that these drivers will be picked up in our statistics. Kings Highway major review. – RMS reports for ideas on presentation of statistics. 31% of Eurobodalla rate payers live in ACT (i.e. holiday-house owners). ACT has surveyed residents on awareness of road safety campaigns. Actions Obtain reports and survey results. Acknowledge possible of driver licence data and vehicle registrations ‘contamination’ from non-ACT residents. Topic Other influences on crash rates – Discussion There are many influences on travel patterns and crashes. These include weather patterns, employment, fuel prices, average income, population growth, location of trip generators (particularly in relation to home residences), availability of alcohol user’s and other drugs, driver age and gender, perceptions of police enforcement, expectations regarding the perceptions of other drivers, peer group and significant others. Actions Where possible, take these factors into account in modelling either directly (e.g. fuel price) or indirectly (e.g. population demographics). Acknowledge that there may be other influences unaccounted for. Further thoughts Travel patterns of ACT residents is out of scope but would provide much greater insights. prevent geo-referenced mobile Do Australia’s privacy laws telecommunications data from being used to map travel? How would you distinguish between ACT residents and non-ACT residents? Would it matter if you couldn’t? You distinguish between different vehicle types. Might be better to do targeted couldn’t vehicle counts in this context. Topic Next steps – Actions Further mapping and descriptive statistics. Time series modelling.
driver licences. This means that these drivers will be picked up in our statistics. Kings Highway – major review. RMS reports for ideas on presentation of statistics. 31% of Eurobodalla rate payers live in ACT (i.e. holiday-house owners). ACT has surveyed residents on awareness of road safety campaigns. Actions Obtain reports and survey results. Acknowledge possible ‘contamination’ of driver licence data and vehicle registrations from non-ACT residents. Topic – Other influences on crash rates Discussion There are many influences on travel patterns and crashes. These include weather patterns, employment, fuel prices, average income, population growth, location of trip generators (particularly in relation to user’s home residences), availability of alcohol and other drugs, driver age and gender, perceptions of police enforcement, expectations regarding the perceptions of other drivers, peer group and significant others. Actions Where possible, take these factors into account in modelling either directly (e.g. fuel price) or indirectly (e.g. population demographics). Acknowledge that there may be other influences unaccounted for. Further thoughts Travel patterns of ACT residents is out of scope but would provide much greater insights. Do Australia’s privacy laws prevent geo-referenced mobile telecommunications data from being used to map travel? How would you distinguish between ACT residents and non-ACT residents? Would it matter if you couldn’t? You couldn’t distinguish between different vehicle types. Might be better to do targeted vehicle counts in this context. Topic – Next steps Actions Further mapping and descriptive statistics. Time series modelling.
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