SEVENTH FRAMEWORK PROGRAMME
THEME [SST.2010.1.3-1.]
[Transport modelling for policy impact assessments]
Grant agreement for: Coordination and support action
Acronym: Transtools 3
Full title: „Research and development of the European Transport Network Model – Transtools
Version 3
Proposal/Contract no.: MOVE/FP7/266182/TRANSTOOLS 3
Start date: 1st March 2011
Duration: 36 months
Deliverable MS63 - “Short note on impact
assessment for land transport and short-sea
shipping”
Document number: TT3_WP10_MS 63 and 64_Impact assessment_1.0
Workpackage: WP10
Deliverable nature: N/A
Dissemination level: N/A
Lead beneficiary: DTU (1), Jeppe Rich, Sigal Kaplan
Due data of deliverable: 12.09.2011
Date of preparation of deliverable: [29.08.2011]
Date of last change: [10.09.2011]
Date of approval by Commission: N/A
Abstract:
This short note introduces the main concepts underlying the socio-economic impact assessment
that to be used as a post-processing procedure in TransTools 3 (TT3) for internalization of
transport externalities in Transport projects. The socio-economic impact assessment includes the
external costs of traffic congestion, accidents, climate change, pollutant emissions, and noise.
The impact assessment is a transparent and clear framework designed from a bottom-up
approach, on the basis of input from the traffic assignment of TT3, recommended parameters and
user input. The framework complies with EU best practices for impact assessment and
recommended parameters deriving from previous EU project and state-of-the-art studies. The
impact assessment is also capable of accounting for country, region, road type, vehicle type, and
day/season effects. The impact assessment is an important addition to TT2 due to the importance
attributed to the internalisation of external costs for project assessment and policy development in
the transport sector in Europe.
Keywords:
Impact assessment, external costs, socio-economic evaluation, congestion, accidents, noise,
climate effect, air pollution.
Author(s):
Jeppe Rich, Sigal Kaplan
Disclaimer:
The contents of this report reflect the views of the author and do not necessarily reflect the official
views or policy of the European Union. The European Union is not liable for any use that may be
made of the information contained in the report.
The report is not an official deliverable under the TT3 project and has not been reviewed or
approved by the Commission. The report is a working document of the Consortium.
Short note on traffic assignment
Report version 1
2011
Kgs. Lyngby
Copyright:
Published by:
Reproduction of this publication in whole or in part must include the customary
bibliographic citation, including author attribution, report title, etc.
Department of Transport, Bygningstorvet 116 Vest, DK-2800 Kgs. Lyngby,
Denmark
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from:
www.transport.dtu.dk
1.
1.1
1.2
INTRODUCTION ................................................................................................................. 5
Milestone MS63 – modelling of impacts .................................................................................. 5
Model structure...................................................................................................................... 5
2.
IMPACT ASSESSMENT DATABASE................................................................................... 7
3.
TRAVEL TIME AND CONGESTION .................................................................................... 7
3.1
Basic concept – value of time .................................................................................................. 8
3.1.1
Passenger value of time .............................................................................................. 8
3.1.2
Freight value-of-time ................................................................................................. 9
3.2
Consumer surplus model ........................................................................................................ 9
3.2.1
Passenger .................................................................................................................. 9
3.2.2
Freight .................................................................................................................... 11
4.
4.1
4.2
4.3
CLIMATE CHANGE .......................................................................................................... 13
Road ................................................................................................................................... 13
Rail..................................................................................................................................... 14
Short-sea shipping ............................................................................................................... 14
5.
5.1
5.2
5.3
AIR POLLUTION COSTS ................................................................................................... 16
Road ................................................................................................................................... 16
Rail..................................................................................................................................... 17
Short-sea shipping ............................................................................................................... 17
6.
6.1
6.2
6.3
ACCIDENT COSTS ............................................................................................................ 19
Road ................................................................................................................................... 19
Rail..................................................................................................................................... 20
Short-sea shipping ............................................................................................................... 20
7.
7.1
7.2
7.3
NOISE ................................................................................................................................ 22
Road ................................................................................................................................... 22
Rail..................................................................................................................................... 24
Short-sea shipping ............................................................................................................... 25
1. Introduction
The following note describe milestone MS63 concerning the modelling of environmental and economic
impacts and MS64 concerned with the impact assessment database. As the modelling is largely a
function of the data it makes good sense to keep the description together. The two parts are part of
WP10 in the TRANSTOOLS 3 (TT3) project and facilitate internalizing economic, environmental, and
social externalities in transport projects. The current note focuses on tasks 10.2 and 10.3, as the main
core of work package 10.
MS64 will be discussed specifically in Section 3, whereas MS63 is discussed in Section 2 and 4-7.
1.1 Milestone MS63 – modelling of impacts
The modelling of impacts includes a range of different effects, which will be presented on a monetary
scale. That is;
-
socio-economic impact as measured through consumer surplus measures
-
environmental impacts related to accidents, noise, emissions, and climate effects.
The socio-economic impact assessment includes the external costs of travel time and traffic
congestion and the evaluation of taxation policies. The environmental impacts relate to accidents,
climate change, pollutant emissions, and noise.
The combined impacts will be linked with input from the traffic assignment of TT3, recommended
parameters and user input. The framework will also comply with EU best practices for impact
assessment and recommended parameters deriving from previous EU project and state-of-the-art
studies. The impact assessment is also capable of accounting for country, region, road type, vehicle
type, and day/season effects.
The socio-economic impact assessment is largely dependent on the output of the traffic assignment
model and on the output from the scenario generator for forecasting future years. In addition, although
default parameters will be recommended for the cost elements in the calculations, the impact
assessment is highly dependent on user input with respect to assumptions regarding the background
economic conditions and assumptions in future scenarios. Hence, in addition to generating impact
assessments on the basis of the current economic conditions and political values, the framework
allows maximum flexibility for the user to offer a range of alternative assumptions. Decisions regarding
the appropriate user input for the impact assessment (either at the scenario generating stage or at the
impact assessment stage) should be made.
1.2 Model structure
The overall model structure is illustrated below;
TT3
Car and truck
mileage
Detailed vehicle
stock
Car mileage
inventory
Noise impacts per
mileage
Monetary noise
impacts
Noise
inventory
Safety impacts per
mileage
Safety
inventory
Emission
inventory
Copert emission
factors
Monetary impact of
emission and climate
effects
Monetary safety
impacts
Energy use
External cost
database
Figure 1: Illustration of impact assessment model for environmental impacts for road transport.
The process flow is straightforward. The TT3 model deliver car and truck mileage at a disaggregated
from the assignment model. This output is then distributed on vehicle classes for each country that
conform to the COPERT categories. This gives a total car mileage inventory from which the external
environmental costs can be calculated.
This consists of three steps. A step where the emissions and climate effects are calculated on the
basis of COPERT emission factors. This in turn results in a number for the energy consumption.
However, if combined with a table for the external costs of the various emissions, the complete
monetary impact of emissions results. The same process will also be used for safety and noise
impacts.
The modelling strategy is described in more details in section 4-7, where the breakdown in traffic
volume, emission factors and unit costs gives the final output.
It should be said that Figure 1 is only concerned with road traffic. However, external effects will also be
calculated for other modes including rail, short sea shipping and air transport. In principle, these
calculations will follow the same principles. However, the classification of train types, vessel types and
plane types may be more aggregated. We will explore data sources for these modes.
2. Impact assessment database
To make it possible to link standardised databases as regard emissions and climate effects to the
TRANSTOOLS model we will adopt the “vehicle fleet” as used in the COPERT model1 . In the COPERT
database vehicles are classified according to;
-
Vehicle type (passenger car, light duty truck, heavy duty truck, busses,..)
-
Motor size and propellant
-
Technology (Euro classes)
In the following WP we do not consider how this vehicle stock is calculated, whether it completely
exogenously determined or whether it is based on an internal vehicle stock model as discussed in the
passenger model WP.
The vehicle fleet national data is provided by EMISIA SA and can be downloaded from the COPERT
site. These data can be used for the compilation of national emission inventories or in connection
with the TRANSTOOLS model. As data are compatible with the COPERT model it is possible to derive
emissions by combining these databases.
Currently we are investigating whether we can get official forecast of the vehicle stock for 2010 and
onwards (2020, 2030, 2040 and 2050).
We will investigate the possibility of having similar “vehicle” databases for short sea shipping, air
transport and rail. For rail the most important distinction will be whether the trains are electrified or
diesel. This information is coded in the network database and will be directly available in the TT3
output.
3. Travel time and congestion
The impact of reduced travel time and congestion as a result of improved infrastructure is often
referred to as “consumer surplus”, namely the social monetary benefits that users experience as a
result of the infrastructure improvements.
In the TRANSTOOLS 3 model these effects are all calculated using a “rule-of-the-half” approximation
(Kidokoro, 2004).
1
http://www.emisia.com/tools/FLEETS.html
The premise for calculating a consumer surplus is that a change has occurred between the baseline
scenario (or the zero scenario) and a proposed infrastructure scenario (alternative scenario number n
where x=1,2,3,…N alternatives).
3.1 Basic concept – value of time
A central variable in the impact assessment is the value-of-time (VOT), namely the monetary value for
the society of one unit of saved travel time. The next sections discuss passenger and freight VOT
calculation.
3.1.1 Passenger value of time
The passenger VOT represents a challenge for the TRANSTOOLS model as it needs to be
represented at the country level for 42 countries. However, studies or estimates of country specific
VOT are difficult to attain n many countries, and comparisons among countries may be difficult due to
differences in VOT definitions. Hence, a model needs to be established in order to calculate the
country specific VOT from available and comparable data.
A method is proposed for calculating country specific VOT on the basis of VOT studies from Denmark,
Sweden, Netherlands, United Kingdom and Switzerland, in which the methodologies are known to be
comparable. Then, the challenge is to calculate the VOT for the remaining countries. Three methods
are proposed for evaluating VOT for the remaining countries:
∙ Method 1: evaluating the VOT on the basis of gross wage rates (business value of time). The gross
wage rates per country as available from Eurostat can be assumed to be equal to the business
VOT. The remaining travel purposes can then be calculated by using a scaling derived from the
VOT sample.
∙ Method 2: evaluating the VOT on the basis of the report from the HEATCO project (Bickel et al.,
2006) as recommended by the Handbook on estimation of external costs in the transport sector
(Maibach et al., 2008). The values include VOT per country, mode, travel purpose and trip length.
∙ Method 3: evaluating the VOT on the basis of the average VOT from the VOT sample and then
create a scaling based on the purchasing power parity index. The calculation would equal

PPPi  GDP / capitak ,i
VOTkc  VOTk ,sample 
 PPP
sample  GDP / capitak , sample

1.0



Where k represents the combination of mode and travel purpose. VOT for the sampled countries can
be extracted from the studies mentioned above, or alternatively can be extracted on the basis of EU25 countries As suggested in the Handbook on estimation of external costs in the transport sector
(Maibach et al., 2008). Notably, the VOT used for TT2 can also be used to derive country specific
VOT.
Our recommendation is to use the first method or alternatively the third method.
3.1.2 Freight value-of-time
The value-of-time for freight is distributed according to commodity groups based on the NSTR/2007
classification. The HEATCO handbook (Bickel et al., 2006) does not provide a distribution by
commodity groups and we therefore propose a small literature review.
3.2 Consumer surplus model
3.2.1 Passenger
The consumer surplus calculation is conducted for origin-destination (i-j) pairs, by trip purpose (p) and
travel mode (m). The origin-destination, travel purpose and mode distinguish trips into separate
transport demand markets.
The variable definitions for the consumer surplus calculation are defined in table 1:
Table 1: Units and variable definitions
Variable
Description
Unit
I
From zone
J
To zone
P
Travel purpose
M
Transport mode
T(i,j,p,m)
Trip matrix
Trips
VoTP_Air1(i, p)
On board VoT for air passengers
Euro/Hour/Person
VoTP_Air2(i, p)
..
Euro/Hour/Person
Monetary representation of time
Euro/trip
Out of pocket cost
Euro/trip/person
Access and Egress travel time
Hours
..
LoS_Air (i,j,p,m)
..
Cost_Air(i,j,p,m)
...
AccEgr_Air(i,j,p)
…
InVehicleTime(i,j,p,m)
HeadwayTime
SailTime
TransferTime
WaitTime
CS(i,j,p,m)
..
The following variables are defined to be used in the consumer surplus calculation:
LoS_Air(i,j,p) = AccEgr_Air(i,j,p)*VoTP_Air2(i, p) + InVehicleTime_Air(i,j,p) *VoTP_Air1(i, p) +
HeadwayTime_Air(i,j,p)*VoTP_Air3(i, p) + TransferTime_Air(i,j,p)*VoTP_Air3(i, p)
LoS_Rail(i,j,p) = AccEgr_Rail(i,j,p)*VoTP_Rail2(i, p) + InVehicleTime_Rail(i,j,p)*VoTP_Rail1(i, p) +
CongestionTime_Rail(i,j,p)*VoTP_Rail3(i, p) + SailTime_Rail(i,j,p)*VoTP_Rail1(i, p)+
TransferTime_Rail(i,j,p)*VoTP_Rail3(i, p)
LoS_Car(i,j,p) = InVehicleTime_Car(i,j,p)*VoTP_Car1(i, p) + CongestionTime_Car(i,j,p)*VoTP_Car2(i,
p) + SailTime_Car(i,j,p)*VoTP_Car3(i, p) + WaitTime_Car(i,j,p)*VoTP_Car2(i, p)
The LoS_Car(i,j,p) represent both driver and passengers.
Cost_Air(i,j,p) = PriceAir(i,j,p)
Cost_Rail(i,j,p) = PriceRail(i,j,p)
Cost_CarD(i,j,p) = a*(FuelCost(i,j,p) + TollCost(i,j,p))
Cost_CarP(i,j,p) = (1-a)*(FuelCost(i,j,p) + TollCost(i,j,p))
Here, the parameter “a” represents the degree of cost sharing between driver and passengers.
The passenger consumer surplus is now calculated by the different modes as:
CS_Air(i,j,p) = (T0(i,j,p,m=air) + T1(i,j,p,m=air))*0.5*[(LoS_Air0(i,j,p) + Cost_Air0(i,j,p)) –
(LoS_Air1(i,j,p) + Cost_Air1(i,j,p))]
CS_Rail (i,j,p) = (T0(i,j,p,m=rail) + T1(i,j,p,m=rail))*0.5*[(LoS_Rail0(i,j,p) + Cost_ Rail0(i,j,p)) – (LoS_
Rail1(i,j,p) + Cost_ Rail1(i,j,p))]
CS_CarD(i,j,p) = (T0(i,j,p,m=carD) + T1(i,j,p,m= m=carD))*0.5*[(LoS_Car0(i,j,p) + Cost_CarD0(i,j,p)) –
(LoS_Car1(i,j,p) + Cost_CarD1(i,j,p))]
CS_CarP(i,j,p) = (T0(i,j,p,m=carP) + T1(i,j,p,m= m=carP))*0.5*[(LoS_Car0(i,j,p) + Cost_CarP0(i,j,p)) –
(LoS_Car1(i,j,p) + Cost_CarP1(i,j,p))]
3.2.2 Freight
The dimensions of the consumer calculation for freight is represented by origin-destination pairs (i-j)
and commodity group (q). The origin-destination pairs and commodity groups define a separate
transport market. The calculation of the freight consumer surplus is based on commodity flows (in
tons).
The variable definitions for the consumer surplus calculation are defined in table 1:
Table 2: Units and variable definitions
Variable
Description
Unit
i
From zone
Nuts 3
j
To Zone
Nuts 3
q
Commodity group
NSTR/2007
m
Transport mode
T(i,j,q,m)
Ton matrix
Ton
VoTF_Truck1(i,q, m)
…
Euro/Tonnekm
LoS (i,j,p,m)
Monetary representation of time
Euro/trip
Cost(i,j,p,m)
Out of pocket cost
Euro/trip/person
AccEgr(i,j,p,m)
Access and Egress travel time
Hours
InVehicleTime(i,j,p,m)
..
..
HeadwayTime
SailTime
TransferTime
WaitTime
CS(i,j,p,m)
LoS_Rail(i,j,q) = AccEgr(i,j,q) + OnboardTime(i,j,q)
LoS_Truck(i,j,q) = OnboardTime(i,j,q)*VOT + CongTime0 + SailTime0 + WaitTime0
LoS_IWW (i,j,q) = OnboardTime(i,j,q)*VOT + CongTime0 + SailTime0 + WaitTime0
LoS_SSS(i,j,q) = OnboardTime(i,j,q)*VOT + CongTime0 + SailTime0 + WaitTime0
Cost_Truck(i,j,q) = TonDrivingCost0(i,j,q) + TollCost(i,j,q)
Cost_Rail (i,j,q) = TonDrivingCost0(i,j,q) + TollCost(i,j,q)
Cost_IWW (i,j,q) = TonDrivingCost0(i,j,q) + TollCost(i,j,q)
Cost_SSS(i,j,q) = TonDrivingCost0(i,j,q) + TollCost(i,j,q)
CS_Rail(i,j,q) = (TM_Rail0(i,j,q) + TM_Rail1(i,j,q))*0.5*[(LoS_Rail0(i,j,q) + Cost_Rail0(i,j,q)) (LoS_Rail1(i,j,q) + Cost_Rail1(i,j,q))]
CS_Truck(i,j,q) = (TM_Truck0(i,j,q) + TM_Truck1(i,j,q))*0.5*[(LoS_Truck0(i,j,q) + Cost_Truck0(i,j,q)) (LoS_Truck1(i,j,q) + Cost_Truck1(i,j,q))]
CS_IWW (i,j,q) = (TM_IWW0(i,j,q) + TM_IWW1(i,j,q))*0.5*[(LoS_IWW0(i,j,q) + Cost_IWW0(i,j,q)) (LoS_IWW1(i,j,q) + Cost_IWW1(i,j,q))]
CS_SSS (i,j,q) = (TM_SSS0(i,j,q) + TM_SSS(i,j,q))*0.5*[(LoS_SSS0(i,j,q) + Cost_SSS0(i,j,q)) (LoS_SSS1(i,j,q) + Cost_SSS1(i,j,q))]
4. Climate change
Climate change costs include the risk of flooding, major events and extreme weather events, as well
as negative impacts on human health, water supply, agriculture, ecosystems and wildlife. The two
prominent approaches for evaluating climate change costs are marginal damage costs and avoidance/
mitigation costs. The damage costs approach attempts to quantify possible damages in monetary
terms, while the avoidance approach evaluates the costs of mitigating carbon emission levels to a predefined level, set in international agreements. The damage costs approach is mostly recommended
for long-term forecasting beyond 2030, while the avoidance cost approach is recommended for shortterm forecasting between the years 2010-2020 (Maibach et al., 2008).
4.1 Road
The risk of climate change deriving from carbon emissions of road traffic is mainly related to traffic
volumes, fuel type, vehicle type in terms of emission standards, and travel speed. The external costs
of climate change are computed as follows (Maibach et al., 2008):
ECC ijk  Vijk * EFijk * EUC ijk
Where
ECC - the external climate change cost (Euros)
V - Traffic volume (vehicle kilometres)
EF – Emission factor (gram per kilometre)
i – Vehicle type (i.e., passenger petrol, passenger diesel, truck)
j - Vehicle size (engine size)
k - Euroclass (i.e, Euro-0-Euro-6).
EUC – CO2 unit cost (Euro / tonne)
The traffic volumes result from the TT3 traffic assignment model. The emission factor is provided the
Copert database for specific vehicle type, size and Euroclass (refer to Section 2). The emission unit
costs reflect attitudes and political views towards climate change risk reduction, and can be calculated
according to the marginal damage costs approach or the carbon emissions avoidance approach.
Moreover, the emission unit costs may differ across countries and regions, although they can also be
calculated assuming the same cost across European countries. External unit costs factors are
recommended by the Handbook on estimation of external costs in the transport sector (Maibach et al.,
2008) and Heatco project (Bickel et al., 2006). The recommended unit costs on the basis of damage
costs start from € 26 per tonne CO2 in the years 2010-2019 and increase € 8 per tonne every decade
until 2050. In 2050 external cost of € 83 per tonne CO2 is recommended. The recommended unit costs
on the basis of avoidance costs are € 15 per tonne CO 2 in 2010, about € 65 per tonne in 2030, and
about € 120 per tonne in 2050.
For the base year 2010, the Handbook on estimation of external costs in the transport sector (Maibach
et al., 2008) provides climate change cost factors in Euros per cubic tone per vehicle-kilometres that
can be directly multiplied by the traffic volume in vehicle kilometres.
4.2 Rail
For rail, the Handbook on estimation of external costs in the transport sector (Maibach et al., 2008)
recommends the evaluation of climate impacts based on train-kilometres as follows:
rail
ECCijkl
 Vijkl * EUCijkl
Where
ECCrail - the external climate change cost for rail (Euros)
V - Traffic volume (train-kilometres)
i – Train type (i.e., passenger, freight)
j - Service type (metropolitan, urban, interurban)
k - Rolling stock (i.e, locomotive, railcar, high-speed train).
L – Propulsion (i.e., electric, diesel)
EUC – CO2 unit cost (Euro / tonne)
Climate change cost factors in Euros per train-kilometres are recommended by the Handbook on
estimation of external costs in the transport sector (Maibach et al., 2008). Average cost factors and
their range for direct and indirect emissions are provided for the year 2010 by train type (i.e.,
passenger, freight), service type (i.e., metropolitan, urban, interurban), propulsion (i.e., electric,
diesel), rolling stock (i.e., locomotive, railcar, high-speed train). For long-term prediction purposes
beyond 2030, the values need to be adjusted considering future rail technologies and future emission
unit costs.
4.3 Short-sea shipping
For short-sea shipping the Handbook on estimation of external costs in the transport sector (Maibach
et al., 2008) recommends the evaluation of climate impacts based on ship-kilometres as follows:
ECCijship  Vij * EUCij
Where
ECCship - the external climate change cost for shipping (Euros)
V - Traffic volume (ship-kilometres)
i – Cargo type (i.e., dry cargo, push barge, Tanker)
j – Ship size (i.e., tonnage categories)
EUC – CO2 unit cost (Euro / tonne)
Climate change average cost factors and their range in Euros per ship-kilometres for freight transport
on inland waterways are recommended by the Handbook on estimation of external costs in the
transport sector (Maibach et al., 2008). The cost factors are provided for the year 2010, by cargo type
(i.e., dry cargo, push barge, Tanker) and ship size (in tonnage). For long-term prediction purposes
beyond 2030, the values need to be adjusted considering future maritime vessels technologies and
future emission unit costs.
5. Air pollution costs
Air pollution in the transport sector consist mainly of particulate matter (PM10, PM2.5), Nitrogen oxides
(NO2, NOx), Sulphur oxide (SO2), Ozone (O3) and Volatile organic compounds (VOC). Air pollution
costs consist of health costs, building damages, crop losses, soil and water contamination, and
damages to wildlife and ecosystems. Of these negative externalities, health costs are currently
considered as the most important negative externalities and are by far the most researched topic
(Maibach et al., 2008).
5.1 Road
For road transport the emission costs are determined by the vehicle emission standards, vehicle age,
speed, fuel type, load factors and driving patterns, as well as meteorological conditions.
The external pollution costs are calculated as follows (Maibach et al., 2008):
EPC ij  Vi * EFij * EUC j
Where:
EPC - the external air pollution cost (Euros)
V - Traffic volume (vehicle kilometres)
EF – Emission factor (gram per kilometre)
i – Vehicle category according to emission standards
j – Pollutant
EUC – emissions unit cost (Euro / tonne)
The traffic volumes derive from the TT3 model assignment results, while the emission factors are
provided by car manufacturers and the emission unit costs is a matter of political decisions at the
European level. Air pollution unit costs per tonne for metropolitan and rural areas are recommended
by the Handbook on estimation of external costs for the EU-25 countries. The country values are
based on considerations of population densities, meteorological conditions and traffic patterns.
The Handbook on estimation of external costs in the transport sector (Maibach et al., 2008) also
provides total air pollution costs considering all pollutants per cubic tonne per vehicle-kilometre so that
the cost can be directly calculated, without the need to separately input the emission factor and unit
cost parameters. The values are provided per vehicle type (passenger petrol, passenger diesel and
truck), vehicle size and Euroclass, while differentiating between metropolitan, urban and interurban
areas. The values are based on the assumption of an average speed of 37 km/h in metropolitan
areas, 75 km/h in interurban areas and 106-125 km/h on motorways.
Interestingly, emissions for electric vehicles and Bio-fuel vehicles are currently unavailable. Depending
on the market share of electric vehicles and the share of renewable energy generation in power plants
in future scenarios, costs should be derived for electric vehicles and bio-fuel vehicles.
5.2 Rail
The external pollution costs for rail are calculated on the basis of train-kilometres as follows (Maibach
et al., 2008):
rail
EPCijkl
,p  Vijkl * EUC p
Where:
EPCrail - the external air pollution cost for rail (Euros)
i – Train type (i.e., passenger, freight)
j - Service type (metropolitan, urban, interurban)
k - Rolling stock (i.e, locomotive, railcar, high-speed train).
L – Propulsion (i.e., electric, diesel)
p – Pollutant
EUC – emissions unit cost (Euro / tonne)
Air pollution cost factors in Euros per train-kilometres are recommended by the Handbook on
estimation of external costs in the transport sector (Maibach et al., 2008). Average cost factors and
their range for direct and indirect emissions are provided for the year 2010 by train type (i.e.,
passenger, freight), service type (i.e., metropolitan, urban, interurban), propulsion (i.e., electric,
diesel), rolling stock (i.e., locomotive, railcar, high-speed train). For long-term prediction purposes
beyond 2030, the values need to be adjusted considering future rail technologies and future emission
unit costs.
5.3 Short-sea shipping
The external pollution costs for short-sea shipping are calculated on the basis of ship-kilometres as
follows (Maibach et al., 2008):
ECCijship  Vij * EUCij
Where
ECCship - the external climate change cost for shipping (Euros)
V - Traffic volume (ship-kilometres)
i – Cargo type (i.e., dry cargo, push barge, Tanker)
j – Ship size (i.e., tonnage categories)
EUC – air pollution unit cost (Euro / tonne)
Air pollution average cost factors (including all pollutants) and their range in Euros per ship-kilometres
for freight transport on inland waterways are recommended by the Handbook on estimation of external
costs in the transport sector (Maibach et al., 2008). The cost factors are provided for all pollutants for
the year 2010, by cargo type (i.e., dry cargo, push barge, Tanker) and ship size (in tonnage). Pollutant
specific cost factors are not provided. For long-term prediction purposes beyond 2030, the values
need to be adjusted considering future maritime vessel technologies and future emission unit costs.
6. Accident costs
The accidents costs are calculat\ed separately for passenger and freight transport, as well as by mode
considering road and rail transport.
6.1 Road
Road accident costs mainly concern the loss of life, medical costs and production loss. A bottom-up
approach for the estimation of marginal accident costs depending on vehicle kilometres driven is
applied in TT3, in accordance with former EU projects UNITE and GRACE. The bottom-up approach
is recommended not only for its clarity and transparency, but also since it enables to embed
assumptions regarding vehicle safety improvements, urbanization, changes in gross domestic product
(GDP) per capita, different insurance policies and trends in accident rates into the analysis.
Nevertheless, the bottom-up approach is relatively data intensive. The external road accident costs
can be evaluated according to the following formulation (Maibach et al., 2008):
EAC ijk  Vijk * REijk * AUC ijk * EPijk
Where:
EAC - External accident costs
i - Area type (i.e., urban, interurban, rural)
j - Vehicle type (i.e., private car, heavy trucks)
k – Accident severity (i.e., damage and light injury accidents, severe accidents, fatal accidents)
V - Traffic volume (vehicle kilometres driven)
RE - Risk elasticity (number of accidents per vehicle kilometres driven)
AUC - Unit cost per accident (Euros).
EP - External part of accident costs (percent)
While traffic volumes result from the TT3 traffic assignment model, the risk elasticity rates, the
accident unit costs and the external part of accident costs are country/region specific parameters that
are subject to changes by the TT3 users, although recommended values are provided within the
current framework.
The formulation differentiates between:
∙ Urban areas, interurban traffic on main roads and rural areas since accidents risk rates are
associated with traffic volumes, travel speed, road type and infrastructure conditions.
∙ Accidents involving private cars and heavy trucks due to differences in their risk elasticity and unit
cost per accident.
∙ Accidents based on their severity since the risk elasticity and unit costs per accidents vary across
accident severity categories.
Following the recommendation of the Handbook on estimation of external costs in the transport sector
(Maibach et al., 2008), the unit cost per accident is based on the value of statistical life (VSL), with
severe injuries value of 13% of the VSL and light injuries value of 1% of the VSL. These values
conform with Bickel et al. (2006). The VSL per country/region is calculated based on a recommended
European average of € 1.574 million (WHO, 2011) and GDP per capita PPP in order to derive
country/region specific VSL. For future years the VSL should be updated by considering inter-temporal
changes in the GDP per capita and price levels.
The Handbook on estimation of external costs in the transport sector (Maibach et al., 2008)
recommends the use of values provided by Bickel et al. (2006) for country/region specific direct and
indirect road accident costs. The main problem with these data is that they are somewhat outdated as
they are based on information from 2002 and that they cannot be readily extrapolated to derive future
year scenarios.
The assumption on internal and external parts of the risk value is related to national insurance
systems and varies between 59% and 76% for road transport (Maibach et al., 2008).
6.2 Rail
Literature regarding rail accident costs is scarce. Only few studies exist on average rail accident costs
there are no studies available concerning risk elasticity for rail transport. However, important factors
that have been identified as drivers of rail accident costs are traffic volumes, maintenance level,
segregation between road and rail, and separation of passenger and freight rail traffic (Maibach et al.,
2008). Two approaches are possible for evaluating rail accident costs.
The number of rail accidents, the number of fatalities and severely injured people for significant
damage accidents are provided for EU-27 countries by Eurostat for passenger and freight rail traffic.
Hence, rail accidents risk rates per kilometres driven can be calculated. The unit cost of accidents can
be calculated on the basis of VSL and production loss for severe and light injuries, since 88% of rail
accident victims result from rolling stock in motion and at-grade collisions between rail and road traffic
(Bialas-Motyl, 2009). In this approach, the average costs of rail accidents is calculated similarly to road
accidents.
An alternative approach is to evaluate directly the average external accident costs for rail transport for
train kilometres driven. The unit costs of rail accidents range between € 0.08 - € 0.30 per train
kilometre in European countries (Maibach et al., 2008). This approach is recommended hereby due to
the relatively small share of rail fatalities in the total accident fatalities in Europe.
6.3 Short-sea shipping
The external costs of maritime accidents for passenger vessels result from loss of human life and
production, and the external costs of freight vessels derive from the cargo type, the extent of pollution
and cleaning efforts in the case of spill. The probability of maritime accidents is fairly low and statistics
can be retrieved from maritime accident databases (e.g., MAIB, Lloyd, EMSA). The number of
fatalities is negligible in comparison with other modes. Between the years 2007-2010, about 7-10 lives
were lost per year on passenger vessels and about 15-25 lives were lost on cargo vessels (EMSA,
2010). Overall in 2010, there were 4 major incidents of fright ships that fortunately ended without
major pollution events (EMSA, 2010). The main problem with maritime transport is the cost factor
since for maritime transport information on accident costs is almost non-existent (Maibach et al.,
2008).
7.
Noise
The monetary costs associated with noise emissions are related to nuisance, health effects, reduction
of property values, and damage to natural wildlife habitats. The noise threshold above which monetary
impacts are considered ranges between 45-60 dB and varies across countries and studies. The
values of 50 dB for road traffic noise and 55 dB for rail road noise are usually considered as
thresholds (Maibach et al., 2008; Nijland and van Wee, 2008).
7.1 Road
The main contributors to road noise emissions are traffic volumes, traffic speed, the share of heavy
trucks, road surface and maintenance conditions. The main factors underlying noise impact are
population density, the existence of sensitive wildlife, and the distance from the infrastructure. There
are two methods for quantifying the external cost of noise from road transport: (i) based on population
exposure, (ii) based on vehicle kilometres driven.
The first method calculates the external cost by considering the exposure of population and property
to noise levels as follows (Maibach et al., 2008):
ENC  Leq  A * POP * NUC
Where:
ENC - external noise costs (Euros)
Leq(A) - noise emission function (dB(A))
POP – number of affected people
NUC –Yearly noise emission unit cost (Euros per person per year/ dB(A))
A prominent noise emission function calculates noise emissions as function of traffic volumes, vehicle
mixture, average speed and slope on highways and main roads and arterial roads, while assuming the
road is located in a built environment (Mayeres et al, 1996):
Leq (A)  53.9  10Log  Qvl  EQpl   K
Where:
Leq(A) – daily noise level equivalent 2 meters from the road (dB(A))
Qvl – Passenger vehicle flow (vehicles per hour)
Qpl – Heavy truck flow (vehicles per hour)
E – Equivalence factor. Assuming a normal slope of 2%, a truck is equivalent to is 10 passenger cars.
K – Speed correction factor. 1 dB(A) is added for each 10 kilometres per hour.
above a speed of 60 kilometres per hour.
Noise pollution decreases with the distance from the source. As a general rule of thumb, doubling the
distance from the road contributes to a 6 dB noise reduction (not considering the existence of noise
barriers). The existence of visual obstruction leads to a further reduction of 5 dB.
The traffic volumes for passenger cars and heavy vehicles derive from the TT3 traffic assignment
results. The population can simply be calculated by multiplying the residential density in the area of
interest. The residential density can be calculated with the aid of GIS interface for calculating zone
residential densities. Noise emission unit cost per person increase with the increase in noise
emissions, and largely varies between European countries due to differences in income, lifestyle and
considered monetary costs. Noise cost estimates for Europe are provided by Bickel et al. (2006).
The second method calculates the noise emission costs according to the vehicle kilometres driven as
follows (Maibach et al., 2008):
ENC ijk  Vijk * NUC ijk
Where:
MENC - Marginal external noise costs (euros),
i - Area type (i.e., urban, interurban, rural)
j - Vehicle type (i.e., private car, heavy trucks)
k – Time of day (i.e., day, night)
V - Traffic volume (vehicle kilometres driven)
NUC – Yearly noise emission unit cost (Euros/vehicle-kilometre).
The vehicle kilometres driven are base on the TT3 model assignment results while the average noise
emission unit cost values for Europe are provided by the Handbook on estimation of external costs in
the transport sector (Maibach et al., 2008).
The main advantage of the first method is its suitability for prediction of future scenarios as it can
account for population changes and urbanization, changes in the vehicle mixture, and assumptions
related to future costs associated with noise on the basis on income and lifestyle changes.
The advantage of the second methodology is its simplicity, this methodology has three main
limitations. Firstly, while the noise emission unit cost values provide reasonable estimates for the base
scenario, it would be difficult to provide estimates for future years. Secondly, the values are European
averages while noise emission unit cost are largely dependent on income and national preferences for
a quiet lifestyle (Nijland and van Wee, 2008).
A major deficiency of the two methods is the under-estimation of noise impacts in rural areas with
sensitive ecosystems. The first method in its current form is oriented towards quantifying the health
and property value effects, while neglecting impacts on wildlife. The second method does not
differentiate between rural areas and noise-sensitive rural areas, in which the existence of wildlife
contributes to higher noise externalities. Since the consideration of noise externalities in sensitive
ecosystems is an emerging policy issue, the third issue is still not considered in evaluation of road
transport (Nijland and van Wee, 2008). However it should be considered in future year scenarios.
7.2 Rail
Noise generated by rails is generally considered as less a nuisance than noise generated by roads.
The noise emissions from rail mainly depend on the rolling stock characteristics for passenger and
freight trains, train frequency, train speed, and time of day.
The two methods for calculating the noise level for road transport are valid also for rail transport.
Nevertheless, using the first method, a different formula is needed in order to calculate the noise
emissions from rail transport. For this purpose there are various prominent formulations, based on the
same principle. First, the noise for a single train passing event on the basis of speed and technology
is calculated. Then the noise exposure is calculated based on the frequency of the train passing
events per time period (e.g., daily, hourly). Obviously, although detailed models exist for precise
calculation of rail noise and vibration (see DOT, 2005), a parsimonious model would be preferred for
the purpose of strategic forecasting due to lack of data and uncertainties associated with long-term
planning.
Levinson et al. (1997) for example provide a table for assessing rail noise according to the train
technology and speed, and provide a simple formulation for rail noise assessment based on speed as
follows:
Leq (A)l 25m  19.4  29.72Log0.6V
Where Leq(A)l=25m is the noise level at a distance of 25 meter from the middle of the track (dB), and V is
train speed (Kilometres per hour). The noise level decreases with distance D as follows (Levinson,
1997):
Leq (A)l D  Leq (A)l 25m  6.01ln D 
The noise exposure level (NEF) for N multiple trains passing is as follows (Levinson, 1997):
NEF  Leq (A)l 25m  10Log10N  88
The noise exposure level serves as an input for estimating the marginal external costs of noise
according to the first method.
Considering the second calculation method, the average noise emission unit cost values for railroads
in Europe are provided by the Handbook on estimation of external costs in the transport sector
(Maibach et al., 2008).
7.3 Short-sea shipping
The noise costs of shipping are typically not taken into account due to the strong emphasis on the
impact on population and unfortunately neglect of impact on marine wildlife in popular maritime routes.
In fact, the marginal noise costs due to maritime shipping transport are considered negligible due to
relatively low noise emissions and distance from densely populated areas (Maibach et al., 2008).
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