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Traffic generation rates for high density residential developments - understanding the issues

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Josh Milston

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Traffic generation rates for high density residential developments - understanding the issues

  1. 1. Traffic Generation Rates for High Density Residential Developments - Understanding the issues Josh Milston
  2. 2. • Context • The current process • Study approach & site selection • Data analysis & key findings • Recommended next steps Today Josh Milston
  3. 3. Context • Cities are growing…..upwards • Urban infill near public transport nodes • Traffic assessments have significant implications for the feasibility of new development • Adopting an appropriate traffic generation rate therefore critical! Josh Milston
  4. 4. • Context • The current process • Study approach & site selection • Data analysis & key findings • Recommended next steps Josh Milston
  5. 5. • ‘Standard rate’ using RMS guidelines • Based entirely on quantum of dwellings • 0.19 vehicles / dwelling (AM peak hour) • 0.15 vehicles / dwelling (PM peak hour) • Determined via surveys at eight high density residential developments across Sydney Josh Milston As it now stands…..
  6. 6. Limitations…. • Rate based on a single factor (# dwellings) • Determined by surveys at only eight sites • High variability • Non-weighted average used to determine the ‘standard’ rate Josh Milston A more robust approach to forecasting traffic generation from high density residential developments is required.
  7. 7. • Context • The current process • Study approach & site selection • Data analysis & key findings • Recommended next steps Josh Milston
  8. 8. Peak hour Dwellings Parking Recommend approach to forecasting traffic generation Multi-linear regression Josh Milston Data collection Location Influencing factors Review of existing data Site selection
  9. 9. Existing database • 8 sites • 770 dwellings • 1,010 parking spaces Josh Milston
  10. 10. Expanded database • 19 sites • 2,250 dwellings • 2,700 parking spaces Josh Milston
  11. 11. • Context • The current process • Study approach & site selection • Data analysis & key findings • Recommended next steps Josh Milston
  12. 12. Influence of peak hour • ‘Paired-t’ test • Tests wether there is a statistical distinction between trips generated in the AM / PM peaks • Analysis returned p-value of 0.98 AM peak hour: 482 trips PM peak hour: 483 trips No statistical distinction between AM/PM peak hour traffic generation rates Josh Milston
  13. 13. Influence of dwelling and parking spaces Trips / Dwelling Trips / parking space
  14. 14. Influence of dwelling and parking spaces • Both quantum of parking and dwellings display strong relationship to generated traffic • Difficult to use both variables in a single trip generation formula • Rate of parking investigated as influencing factor Josh Milston
  15. 15. 1: Journey to work car mode share 2: Car/PT travel time to Sydney CBD 3: Accessibility to public transport score (PTAL) 4: Walking distance to the nearest railway station and bus stop 5: Employment and population density Influence of location ? Josh Milston
  16. 16. 0 0.2 0.4 0.6 0.8 1 JTW Travel time PTAL Walk distance Emp/Pop density Dwellings only Inc parking rate Inc location Influence of location R2value Josh Milston
  17. 17. Key Findings Traffic generation formula: Ln(Total trips) = -0.95+ 0.01*totaldwellings+ 1.34*parksperdwelling + 1.67*JTWcarmodeshare • Clear relationship between traffic generation and the following factors: • Number of dwellings • Rate of parking in development • Various accessibility factors
  18. 18. Predicted vs actual trips RMS rates (per dwelling) Per dwelling Per dwelling, parking space & accessibility 82% 58% 42% 31% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% Per dwelling & parking space
  19. 19. • Context • The current process • Study approach & site selection • Data analysis & key findings • Recommended next steps Josh Milston
  20. 20. • Collect more data to increase sample size • Gather data at a wider geographical spread of sites • Gather data at sites with greater variability in public transport accessibility • Determine appropriate measure to assess how location of a site influences the rate at which traffic is generated Next Steps
  21. 21. Josh Milston • Forecasting traffic generation is complex, but important! • Dependent on a number of factors • Using a rate based on a single variable is simplistic • Surveying similar sites in nearby areas should be undertaken Summing it up

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