1.
1
Ian Philips
Modelling adaptive capacity to fuel
shocks – an indicator for sustainable
transport policy.
Institute for Transport Studies: University of Leeds

2.
abstract
• There is a possibility that a fuel shock could occur; a severe restriction in the amount of fuel
available for transport. This would restrict the movement of people. The spatial pattern of the
capacity of individuals to adapt to a fuel shock is of concern to policy makers. Additionally the
scope for policy makers to estimate the effects of schemes to increase adaptive capacity on groups
of people at small geographies would allow them to target resources to more vulnerable areas.
An indicator is built which reports the proportion of people in an area who would have the capacity
to make a journey such as their current commute immediately after the fuel shock begins.
The model used to generate the indicator value is described. A population micro-simulation is used
to estimate characteristics of individuals living in Output Areas (OAs) across England. Characteristics
such as Body Mass Index and fitness are generated by combining census data with data from the
Health Survey for England. The physical capacity to propel a bicycle is estimated for individuals. The
nature of the road network and the topography are also considered in estimating the maximum
distance people could travel.
The model can examine the effects of policies to increase adaptive capacity to fuel shocks such as
increasing fitness, reducing obesity, increasing the availability of bicycles and reduction of barriers
to direct journeys by bicycle such as cycle and pedestrian bridges across rivers.

3.
3
Why an
indicator?
1
2
3
Modelling adaptive
capacity to fuel shocks

4.
4
•There are finite limits to
resources
•So that means …
Problem

5.
This might happen where you live
5
Problem
FUEL SHOCK
PERMENANTLY
NO FUEL

6.
• What modes could we use to get
around?
• How can we do something now
which would make a difference
in the future?
6

7.
Fuel Shock Resilience
77
Of who to what? (same normal / new normal)
•Government Keep business running
•Individuals: Quality of life?

8.
8
Personal Fuel
Shock Adaptive
Capacity
The ability of people
to continue making
journeys immediately
post shock. (at our
scale of interest).

9.
Research Questions
What level of adaptive capacity does our
current pattern of travel and
transportation provision have to a
sudden and unpredictable fuel shock?
If we want to improve things before
the shock then how do we assess a
policy in terms of its effect on
adaptive capacity?

10.
10
Planners should
consider adaptive
capacity
Policies would
affect adaptive
capacity
Assessment
frameworks
assess policies
i.e MCA or CBA
Assessment
frameworks
need indicators
Adaptive
capacity
indicator
Why an indicator?

11.
The indicator is:
the percentage of employed
people who could get to work by
walking and cycling tomorrow if
there was a fuel shock
11
What’s the
indicator

12.
Scope and guiding approach for
constructing the indicator
12
No Prediction
Not Freight
Data Assumptions
Quantitative Comparison

13.
Graph: types of adaptive capacity
13
Movehomet = fuel shock t = later
Abilitytouseadaptive
capacity
Other
Changejob
time

14.
Bicycle
characteristics
Variation
in circuity
Transport
availability for
essential
workers
Weight
fitness
Age
Current commute
distance
Bike availability
BMI
Height
Time
budget
Physical
constraint
Social
constraint
Walking
speed
Slope
Bicycling
speed
Network
Permeability
Pedalling
power
Gender
Number of
stops and
starts per
journey Maximum safe,
healthy commute
distance by active
modes
Maximum
cycling
distance
Maximum
walking
distance
interventions to
reduce obesity
Proportion of population
able to get to work post
shock
interventions to
increase bike
availability
interventions to
decrease current
commute distance
interventions to
decrease
network barriers
Cycling
acceptability
Area data
proportion of
route network
with cycle
facilities
interventions to
decrease number of
stops on cycle
journeys
interventions on
cycle
infrastructure &
speed limits
Ability to
get to
work by
PT
Ability to
get to
work by
bus
Current
train
commuters
Public
transport
interventions
3
2&3
2&3
2&3

15.
Bicycle
characteristics
Variation
in circuity
Transport
availability for
essential
workers
Weight
fitness
Age
Current commute
distance
Bike availability
BMI
Height
Time
budget
Physical
constraint
Social
constraint
Walking
speed
Slope
Bicycling
speed
Network
Permeability
Pedalling
power
Gender
Number of
stops and
starts per
journey Maximum safe,
healthy commute
distance by active
modes
Maximum
cycling
distance
Maximum
walking
distance
interventions to
reduce obesity
Proportion of population
able to get to work post
shock
interventions to
increase bike
availability
interventions to
decrease current
commute distance
interventions to
decrease
network barriers
Cycling
acceptability
Area data
proportion of
route network
with cycle
facilities
interventions to
decrease number of
stops on cycle
journeys
interventions on
cycle
infrastructure &
speed limits
Ability to
get to
work by
PT
Ability to
get to
work by
bus
Current
train
commuters
Public
transport
interventions
3
2&3
2&3
2&3
individual capacity to walk and
propel bicycles
Time budget
Supply factors
Slope
Maximum travel
distance
Commute
distance
Vs
indicator

16.
Why an individual approach?
People vary in their ability to travel by walking
and cycling. Their pedalling power is based on
personal attributes like age gender and fitness.
These attributes vary between individuals and
between locations. Access to bicycles and
needing to escort kids to school also varies
between individuals and location. The other
groups of factors like slope and commute
distance also vary geographically.
If we take the “average person” it excludes a
very large amount of people from the analysis.
So an individual approach makes sense

17.
Stage 1 spatial micro-simulation
Sample population
Individual and
aspatial
Aggregate spatial
Constraint tables
software
Synthetic
population
HSE microdata
2008
1754
individuals
(not
households)
age-sex-
economic
activity;
NSSec;
Education;
(2001 OA
census data)

18.
Stage 2 of the model
Stage 1 Synthetic individual
Can
Individual
commute
Y /N?
Draw maximum
travel distance
Draw commute
distance
OA
indicator
%Vs
Aggregate
Escort?+
+
+
+
+
Multiple
draws
Bike?

19.
Assess alternatives:
VsDo minimum Do Policy
e.g policy to increase fitness and reduce obesity
Keep same individuals – not re-draw, compare like with like avoids
noise
Is there a significant difference in indicator value for a given OA
when the policy is implemented?

20.
20
Problem 2020
Working model
20
Better way to add attributes
Dynamic? 2011?
Check / test / analyse

21.
21
Problem 2121
Thank you 21
Questions
Comments

22.
• Image credits:
• http://commons.wikimedia.org
• Further information available on request