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Measuring intelligent transport policy
1. Dr Susan Grant-Muller
Measuring ITS Policy
through KPIs
Susan Grant-Muller and Ben Kolosz
Dec 18th 2014
s.m.grant-muller@its.leeds.ac.uk
2. ITS and sustainability KPI’s
Assessing
the
Environmental
and
Energy
(E&E)
impacts
of
ITS
as
part
of
a
wider
evalua>on
framework
• Can
ITS
generate
E&E
impacts?
• Do
stakeholders
think
these
are
important?
• How
can
we
capture
them?
• Where
do
they
sit
in
a
wider
evalua>on
framework?
3. The big issue…
Isn’t whether ITS has energy and carbon impacts,
but the size of these and how best to capture
them. For example….
4. ITS
scheme
S t u d y
Context
Reported
Carbon/Energy
Impacts
Electronic
charging
UK
London
a)
2003-‐
2006,
↓
16%
carbon
emissions
in
central
Zone
b)
2007
western
extension
zone
↓6.5%
by
2008
Hard
shoulder
running/variable
speed
limits
UK
M42:
↓
4%
-‐
10%.
Fuel
consump>on
↓
4%.
Similar
findings
from
studies
of
VSL
on
M25
In-‐vehicle
control
and
performance
systems
USA,.
normal
condi>ons.
↓
5%
carbon
no
feedback/coaching,
↓
10%
carbon
with
feedback/coaching
Eco-‐driving
Field
trials
↓
average
10%
carbon
emissions
In
vehicle
(overridable)
speed
control
UK
Motorway
–
average
↓
6%
benefit
CO2
Other
road
types
–
lible
benefit
or
small
disbenefit
on
low
speed
urban
roads.
20%
difference
in
emissions
between
lowest
and
highest
emidng
drivers
Dynamic
systems
u>lising
RTTI
simulated
↓
10%-‐20%
carbon
emissions
and
fuel.
Real
world
experiments
-‐
slightly
lower
findings
Eco-‐driving
NL
↓
0.3%-‐0.8%
fuel
consump>on
1999-‐2004
In
vehicle
technology/other
measures
EU
↓
5%
-‐
25%
carbon
Platooning
and
road-‐trains
Lab
↓
Up
to
20
%
fuel
consump>on
and
carbon
emissions.
Teleworking
UK
↓
2.4
%
of
carbon
emissions
from
cars
by
2050
Personalised
travel
planning
JP
↓ 20% carbon emissions through changes to route/
5. • Managing excess demand on parts of the system -
mitigates environmental impacts of congestion
• Speed smoothing - reduces vehicle emissions by shifting
demand (e.g. to off peak) through economic measures.
• Route management - supports adaptation during climate
extremes
• Enforcement systems - maintain expected environmental
and other benefits from infrastructure improvements such
as dedicated lanes.
• Driver behaviour - directly impacts on driver behaviour and
performance of the vehicle to reduce emissions
• Trip substitution - demand reduction
Drivers
on
ITS
E&E
impacts
(1):
6. Further set of impacts arise from:
• Constructing and maintaining the roadside infrastructure
e.g. gantry’s, roadside kit (and decommissioning)
• Energy supply for ICT elements (VMS etc)
• Datacentre/control centre for operation and traffic
management
• Enforcement equipment
Drivers
on
ITS
E&E
impacts
(2)
8. General Methodologies to capture E&E impacts
• Cost Benefit Analysis (CBA) and Cost Effectiveness
Analysis (CEA)
• Multi-Criteria Analysis (MCA)
• Ecological Footprint (EF)
• Environmental Impact Assessment (EIA) and
Environmental Risk Assessment (ERA)
• Standardised Environmental Management System (EMS)
• Life Cycle Assessment (LCA) and Life-cycle Cost Analysis
(LCC)
• Regional/Strategic Environmental Assessment (R/SEA)
9. Where do ITS E&E impacts sit in wider framework?
• Need to capture both ITS benefits and costs
• KPI should cover installing and running ITS + estimate of
impacts from changes in road user behaviour and choices
• Need to consider specific indicators and measurement
covering local environment, global environment and energy
draw
• E&E impacts are only one element of a broader
assessment – also need include (at a minimum) social,
safety, efficiency (travel time), reliability components
10. Specific indicators to capture ITS E&E impacts?
Kolosz (2013)
Criteria
Sub-group
Unit
Description
Scheme Lifecycle
Emissions
Environment
Kg CO2 Eqv.
Global warming potential of road-side ITS infrastructure from
production to disposal.
Road User Emissions
Environment
t CO2 Eqv.
Global warming potential of vehicles using the highway.
Kg of GWP covered
by IT Certificates
Environment
Kg CO2 Eqv.
Global warming offset due to certified green operational standards.
Kg of GWP per IT
task or resource
Environment
Kg CO2 Eqv.
Global warming potential at the software level. Hardware (IT
equipment) if software consumption unavailable.
Energy used per Task
or Resource
Energy
kWh
Electricity consumed at the software level. Hardware (IT equipment) if
software consumption unavailable.
Annual DCIE/PUE of
Data Center
Energy
<Range 1 –
2.5> or <%>
Power Usage Effectiveness (total facility energy / IT equipment
energy) or Data Center Infrastructure Efficiency (the reverse of the
PUE equation).
Roadside Energy
Consumption
Energy
kWh
The total energy consumption of the ITS scheme situated on the
highway.
Scheme Compliance
Social
%
The road users adherence to the ITS enforcement messages if
applicable (speed etc).
Safety
Social
<Range 0-2>
Killed and Seriously Injured Ratio.
Scheme Cost
Economic
£ (or currency
applicable)
The capital cost of the scheme. May also include operational,
maintenance and disposal cost if available.
11. Recommendations for IRF KPI (minimum)
KPI 1: Changes in road user emissions (t CO2 Eqv).
KPI 2: Roadside energy consumption (KWh)
(scaled by size of scheme, e.g per km for scheme
comparison)
12. s.m.grant-muller@its.leeds.ac.uk
References
Grant-Muller S M and Usher M (2013) ‘Intelligent Transport systems: the
propensity for environmental and economic benefits’. Technological Forecasting
and Social Change, Vol 82, pp 149–166
Newman-Askins, R., L. Ferreira, and J. Bunker, Intelligent transport systems
evaluation: From theory to practice. 2003.
Kolosz, B. W. (2013). Assessing the Sustainability Performance of Inter-Urban
Intelligent Transport. Institute for Transport Studies. Leeds, University of Leeds.
PhD: 333
Kolosz, B.W.; Grant-Muller, S.M., "Appraisal and Evaluation of Interurban ITS: A
European Survey," Intelligent Transportation Systems, IEEE Transactions on ,
vol.PP, no.99, pp.1,18 doi: 10.1109/TITS.2014.2351253
Kolosz, BW and Grant-Muller S.M (2014). "Extending Cost-Benefit Analysis for the
Assessment of Inter-urban Intelligent Transport Systems". Environmental
Impact Assessment Review. doi:10.1016/j.eiar.2014.10.006
Thank you – any Questions?