Josh Milston

1. Traffic Generation Rates for High Density
Residential Developments
- Understanding the issues
Josh Milston

2. • Context
• The current process
• Study approach & site selection
• Data analysis & key findings
• Recommended next steps
Today
Josh Milston

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. • Context
• The current process
• Study approach & site selection
• Data analysis & key findings
• Recommended next steps
Josh Milston

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. 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. • Context
• The current process
• Study approach & site selection
• Data analysis & key findings
• Recommended next steps
Josh Milston

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. Existing
database
• 8 sites
• 770
dwellings
• 1,010
parking
spaces
Josh Milston

10. Expanded
database
• 19 sites
• 2,250
dwellings
• 2,700 parking
spaces
Josh Milston

11. • Context
• The current process
• Study approach & site selection
• Data analysis & key findings
• Recommended next steps
Josh Milston

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. Influence of dwelling and parking spaces
Trips / Dwelling Trips / parking space

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. 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. 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. 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. 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. • Context
• The current process
• Study approach & site selection
• Data analysis & key findings
• Recommended next steps
Josh Milston

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. 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