Presented at the AITPM National Conference 2017 by Steven Piotrowski

1. External Factors Likely to Affect Future
Travel Patterns
Melbourne AITPM Conference Presentation
17 August 2017
Steven Piotrowski
Hugo Wildermuth

2. • This paper and
presentation is
dedicated to the
memory of Dr.
Peter Lawrence,
who passed
away on the 22nd
of April 2017.
Dedication

3. • By 2051, Perth will have grown to a metropolis
of about 3.5m people.
Overview
• How will our
transport
infrastructure
requirements
change?

4. • In May 2015, the Department of Planning WA
released the “Perth and Peel @ 3.5 million” land
use plan.
Overview

5. • In January 2017, the WA Government released
the Perth and Peel Transport Plan for 3.5m
People and Beyond.
Overview

6.  Transport modelling was
undertaken to help formulate the
transport plan.
 However, we know that there will
be many changes which may have
a material impact on our long term
forecasts.
 A series of workshops were held
with local Perth transport experts
to quantify the direction and
magnitude of these changes.
Project Background

7. The primary objective of the workshops was:
“To identify relevant, non-transport related
attributes that are likely to change over
the next 25 years or so, and to reach
some level of consensus as to the effect
of these changes on future public
transport usage and car travel (i.e.
negative or positive, significant or
minimal).
Review of Workshops

8. Feedback was obtained for six categories:
• Socio-Demographic
• Employment
• Education
• Shopping
• Advancement in Intelligent Transport
Systems
• Other Technological Changes
Review of Workshops

9. Hugo Wildermuth
created a
“scorecard” for
model
adjustments for
2051 to account
for these
influences.
Identification of External Influences

10. Socio-Demographic
Ageing Population
2006 2011 2041 2051
Age 0 - 14 19.9% 19.2% 17.3% 17.1%
Age 15 - 64 68.3% 68.7% 62.4% 61.4%
Age 65+ 11.8% 12.1% 20.3% 21.5%
Total 100% 100% 100% 100%
Total Population
in WA
2.059 m 2.352 m 3.669 m 4.088 m
Median Age
(years)
36.2 36.3 41.1 41.8
Age 85+ 1.3% 1.5% 3.7% 4.3%
Source: ABS (2012) Cat. no. 4102.0 Australian Social Trends, Data Cube – Population

11. Socio-Demographic
Modelling Implications:
• Seniors will remain more mobile for
longer.
• Assume that avg daily trip rate will
increase from 2.8 trips/day to 3.0.
• Seniors will want to be close to facilities
and services. This would favour central
sector in-fill land use scenarios.
• Increase intrazonal trips for home-based
shopping and social/rec by 10%.

12. Socio-Demographic
Increased Cultural Diversity
• In the future, it is likely that cultural
diversity will increase substantially.
• This may have implications for land use
and urban travel behaviour.
Modelling Implications:
• Increase central sector in-fill population by
7.5% relative to trend land use scenario.

13. Changes in Employment
Increased Work from Home Opportunities
• In the future, it has been assumed that more
people who can work from home will work from
home.
Modelling Implications:
• Reduce daily home-based white collar work trip
rate by 7%.

14. Changes in Employment
More Flexible or Part-time Work
• With part-time or job-sharing work
expected to increase, the average daily
work trip rate should decrease, and peak-
period travel should decrease.
Modelling Implications:
• Reduce peak period factors for home-
based white collar work trips by 6%.
• Change start time of 20% of car drivers
travelling to/from WC work from peak to
off-peak.

15. Changes in Employment
Automation and Pre-Fabrication
• Commuting by construction workers will reduce
somewhat and focus more on industrial, pre-
fabrication sites than construction sites.
• Heavy commercial vehicles will be delivering less
of the raw materials to construction sites and
more to factories, but the necessary delivery of
pre-fabricated sections together with the
requirements for cranes are likely to increase
heavy commercial vehicle movements overall.

16. Changes in Employment
Automation and Pre-Fabrication
Modelling Implications:
• Re-allocate 10% of construction jobs from
residential areas to industrial areas.
• Increase heavy commercial vehicle trip
rate by 2.5%.

17. Changes in Education
School Consolidation
• The consolidation of some public high
schools in established areas and the
creation of specialised schools appears to
be a universal trend.
Modelling Implications:
• Reduce intra-zonal trips for home-based
school trips by 10%.

18. Changes in Education
Online Tertiary Education
• There has been substantial growth in
courses that are delivered fully on-line.
• As online enrolment increases, there will
be a reduction in commuting trip rates to
and from tertiary institutions.
Modelling Implications:
• Reduce observed home-based
tertiary education trip rate by
30%.

19. Changes in Shopping
More Online Shopping
• Based on current growth rates, online
purchases may comprise about 33% of
total sales by 2051.
Modelling Implications:
• Reduce observed daily home-based
shopping trip rate by 9%.

20. Changes in Shopping
Deregulated Retail Hours
• There is little doubt that by 2051, retail
trading hours will have been completely
deregulated.
• Many shops would not open until after the
am peak period and close after the pm
peak, thus reducing or at least spreading
peak travel.
Modelling Implications:
• Reduce AM peak period factors for home-
based shopping trips by 6%.

21. Advances in Intelligent Transport Systems
Real-time Traveller Information
• The availability of up-to-the-minute
information on congestion levels, traffic
incidents, parking availability and public
transport services will improve
perceptions of alternatives to the car.
Modelling Implications:
• Increase highway time disutility coefficient
for commuters by 10%.
• Reduce peak period factors for all trip
purposes in the peak direction by 5%.

22. Advances in Intelligent Transport Systems
Intelligent Traffic Signals
• The development of intelligent and
dynamic signalling systems will
continuously monitor traffic flows in real
time on all approaches and on relevant
exits to an intersection, and adjust the
timing of the green phases to optimise
each cycle.
Modelling Implications:
• Increase intersection capacity by 7%.

23. Advances in Intelligent Transport Systems
Autonomous Vehicles
• By 2051, Todd Litman estimates that
about 80-100% of vehicle sales, 40-60%
of vehicles on the road and 50-80% of
vehicle-kms travelled would be fully
autonomous.
• In our view, this is a somewhat
conservative estimate. The potential
benefits of autonomous vehicles are an
order of magnitude greater than other
new technologies.

24. Advances in Intelligent Transport Systems
Autonomous Vehicles
Modelling Implications:
• Increase intersection and mid-block
capacity by 7%.
• Decrease highway time disutility
co-efficients for all trip purposes by 20%.

25. Other Technological Changes
Improved Communications Technology
• The impact of smartphones on our
lifestyles and travel patterns have been
significant.
• It has been assumed that future tech will
reduce the need or desire to travel.
Modelling Implications:
• Reduce personal business and employer’s
business trip rate by 5%.

26. Criteria (all day) Business as Usual External Influences
Car Driver Trips (mode split%) 6.396m (51.8%) 6.43m (54.1%)
Car Passenger Trips 2.668m (21.6%) 2.563m (21.6%)
Public Transport Trips 1.305m (10.6%) 1.153m (9.7%)
Cycling Trips 0.484m (3.9%) 0.447m (3.8%)
Walk Trips 1.507m (12.2%) 1.301m (10.9%)
Total Person Trips (%change) 12.361m 11.895m (-3.8%)
Avg Trip Length – Car Driver 8.97km 9.63km
Avg Trip Length – Public Transport 18.22km 17.4km
Vehicle kms – Car Drivers 93.169m 98.484m (+5.7%)
Vehicle Hours – Car Drivers 1.442m 1.477m (+2.4%)
Public Transport Passenger-kms 23.782m 20.064m
Public Transport Passenger-hours 1.155m 1.012m
Preliminary STEM Modelling Results (2051)