External Factors Likely to Affect Future

Travel Patterns Melbourne AITPM Conference Presentation

17 August 2017

Steven Piotrowski

Hugo Wildermuth

• This paper and

presentation is

dedicated to the

memory of Dr.

Peter Lawrence,

who passed

away on the 22nd

of April 2017.

Dedication

• By 2051, Perth will have grown to a metropolis

of about 3.5m people.

Overview

• How will our

transport

infrastructure

requirements

change?

• In May 2015, the Department of Planning WA

released the “Perth and Peel @ 3.5 million” land

use plan.

Overview

• In January 2017, the WA Government released

the Perth and Peel Transport Plan for 3.5m

People and Beyond.

Overview

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

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

Feedback was obtained for six categories:

• Socio-Demographic

• Employment

• Education

• Shopping

• Advancement in Intelligent Transport

Systems

• Other Technological Changes

Review of Workshops

Hugo Wildermuth

created a

“scorecard” for

model

adjustments for

2051 to account

for these

influences.

Identification of External Influences

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

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%.

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.

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%.

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.

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.

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%.

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%.

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%.

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%.

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%.

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%.

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%.

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.

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%.

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%.

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)