ISSUES IN THE USE OF EMME/2 AS A PLATFORM FOR THE ESTIMATION
OF GREENHOUSE GASES
A.M. Khan
Department of Civil and Environmental Engineering
Carleton University
Ottawa, Ontario K1S5B6
CANADA
Telephone: +613 520 2600 (EXT. 5786)
Fax: +613 520 3951
Email address: ata_khan@carleton.ca
ABSTRACT
The Kyoto protocol has focussed attention on the identification and implementation of
strategies and tactical measures to reduce greenhouse gas (GHG) emissions. Therefore,
urban transportation systems planners have to find an innovative way to meet this new
requirement. This paper consists of four parts. Part one is a brief introduction to the
concept of environmentally sustainable urban transportation, fuel consumption,
greenhouse gas (GHG) emissions, and the requirements of the Kyoto Protocol. Part two
describes the methodological requirements for the estimation of GHG emissions and
points out the deficiencies in the existing practice. Part three describes how to use
EMME/2 as a platform to estimate GHG emissions. Finally, in part four, conclusions
are presented.
INTRODUCTION
Planning for urban transportation systems has to address one additional requirement
than in the past. The goal of achieving long term sustainability in urban transportation is
becoming widely recognized. More recently, the Kyoto Protocol has focussed attention
on the identification and implementation of measures to reduce GHG emissions in
urban transportation. Since numerous urban areas use EMME/2 as a modelling
framework, it is desirable to investigate methodological and data requirements for the
estimation of GHG emissions. This paper describes issues in meeting this challenge.
ENVIRONMENTALLY SUSTAINABLE TRANSPORTATION
The subject of environmental impacts of transportation has been under investigation for
many decades. Since the mid-1980’s, through the sponsorship of the United Nations,
attempts have been made to reconcile present and future economic development with
the maintenance of ecological capacity and values (1,2).
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Broad definitions such as that of the Brundland Report have resulted in a wide variety
of actions both at the country level and on a multination basis: “development that meets
the needs of the present without compromising the ability of future generation to meet
their own needs” (page 387 of reference 3).
At the 1992 United Nations Conference on Environment and Development (UNCED) in
Rio de Janeiro, the theme of sustainable development played a central organizing role.
Recommendations on sustainable development were compiled and published as Agenda
21 (4).
Although the concept of sustainable development encompasses many factors, the
urgency in dealing with the climate change issue has focused attention on the reduction
of greenhouse gas emissions. Recently, in December 1997, Canada and more than 150
countries negotiated the Kyoto Protocol, which sets GHG emission targets for the post2000 period. This Protocol, if ratified, will commit Canada to reduce GHG emissions by
6% below 1990 level during the period 2008-2012. At present, work is underway to
define measures for reducing emissions of GHG in order to meet the Kyoto agreement
and to further reduce GHG emissions in years beyond.
FUEL CONSUMPTION AND GREENHOUSE GAS EMISSIONS
As a result of fuel consumption, emissions are produced. In addition to other emissions,
the following GHG emissions result from the combustion of petroleum fuels. The GHG
emissions of interest are: carbon dioxide (CO2), methane (CH4), and nitrogen oxide
(N2O). The magnitudes of these emissions per litre of fuel vary by type of fuel, engine
and emission control technologies. In order to find the CO2 equivalent of these gases,
equivalency factors are used which reflect their relative long term greenhouse effect.
The equivalency factors are 1 for CO2, 21 for CH4, and 310 for N2O.
The overall relationship between transportation, fuel consumption and GHG emissions
is shown in Figure 1 in the form of a conceptual diagram. Also shown is the method for
computing GHG emission factors for specific fuels and the estimation of GHG
emissions.
Fuel Consumption (litres) = Vehicle-kms x Fuel Consumption/Vehicle-km
GHG Emission Factor (million tonnes/giga litre) =
SUM[(Emission Level)x(CO2 Equivalency)]
GHG Emissions (million tonnes) =
(giga litres of fuel consumed)x(GHG Emission Factor)
Figure1: Relationships Between Mobility, Energy Consumption and Greenhouse
Gas Emissions (Adapted from references 5 and 6).
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METHODOLOGICAL REQUIREMENTS
It is useful to introduce the institutional as well as the technical aspects of fuel
consumption and emissions for road vehicles before discussing the effect of average
link speed on fuel consumption. Four concepts are of particular interest here. These are:
. new vehicle fuel consumption rating
. on-road vehicle fuel efficiency
. Federal Test Procedure (FTP) for regulated emissions
. Company Average Fuel Consumption (CAFC)
New passenger car and light duty truck fuel consumption is sourced from laboratory
tests specified by the Canadian Federal Government and U.S. Government agencies (7).
In accordance to the self-certification agreement between industry and government, the
automobile manufacturing companies are responsible for the conducting the fuel
consumption tests according to approved Transport Canada methods. Governments may
and do conduct tests to verify results on a sample of vehicles. Fuel consumption
estimates obtained by the industry from tests are reported for urban and highway cycles.
The urban drive cycle is a simulation of the typical urban trip involving stop-and-go
operation. On the other hand, the highway driving cycle simulates a higher degree of
cruising at highway speeds.
As expected, actual fuel consumption tests suggest that on-road fuel intensity is higher
than laboratory tests. A number of reasons have been advanced, including the driving
habits of motorists, weather, road condition, the condition of the engine, and traffic
congestion. The application of a fuel adjustment factor to the new car fuel efficiency is
necessary in order to account for the actual over-the-road conditions. For this reason,
the new vehicle fuel consumption label values and the annual Fuel Consumption Guide
are adjusted accordingly. The factors that are applied to the laboratory tests are: 15% for
city and 10% for the highway cycle.
The Natural Resources Canada (NRCan) used a 20% degradation factor in Energy
Outlook studies for light duty vehicles between laboratory and on-road vehicle fuel
efficiency. NRCan sources suggest that in 1994, on-road fuel consumption for
automobiles was 21.4% higher than laboratory tests. In the case of light trucks, the
corresponding 1994 difference was even higher -- 26.3% (7).
For internal combustion (IC) engine vehicles, stop and go operations in urban areas
cause inefficiency in fuel use. In case the average speed of vehicle improves as a result
of a reduction in stop-and-go operations, the fuel economy improves as well. Logically,
fuel efficiency keeps on improving with increased link average speed until the speed for
best operation is reached (Table 1).
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For average speeds below 25 km/h, fuel consumption rises rapidly (8). On the other
hand, average speeds higher than 70 km/h cause fuel consumption to rise somewhat due
to increasing aerodynamic drag on vehicles. However, it has been pointed out that
newer model vehicles with highly sophisticated aerodynamic designs do not show the
effect of drag until much higher speeds.
Table 1: Ratios of the Fuel Consumption Rates – Automobile, Urban Driving
Conditions (For Illustration Purposes)
Link Average Speed
Fuel Consumption
Link Average
Fuel Consumption
(km/h)
Factors (Ratios)
Speed (km/h)
Factors (Ratios)
______________________________________________________________________
5
2.79
40
0.87
10
2.13
45
0.81
15
1.70
50
0.78
20
1.37
55
0.78
25
1.15
60
0.79
30
1.00
65
0.79
35
0.93
70
0.79
Notes: 1. Fuel consumption factor (ratio) = fuel consumption at a given average speed
divided by the fuel consumption at the urban test cycle average speed of about 31 km/h.
2. Link average speeds are shown . These are not cruising speeds.
Source: Reference 9.
In Table 1, for the on-road travel by a typical automobile, the fuel consumption rate at
30 km/h is set as the reference rate (i.e. approximately the same as the urban test cycle
average speed). To illustrate the effect of congestion, we take a link average speed of 15
km/h. At this very inefficient average speed, fuel consumption rate becomes 1.7 times
the reference rate.
It should be pointed out that new vehicle technologies (i.e. hybrid, all-electric, fuel cell)
differ from the internal combustion engine vehicle in many respects. In addition to fuel
consumption rate (i.e. fuel intensity), these technologies show differences in terms of
changes in fuel consumption rates with link average speed.
The example of hybrid vehicles is presented here. Depending upon design, a hybrid
vehicle may operate under battery power in congested driving conditions. The small
internal combustion engine of a hybrid vehicle is used to propel the vehicle and/or
charge the battery in the cruising mode. Since the internal combustion engine is not
used (according to design) in the inefficient low average speed mode of operation and
due to regenerative feature, the fuel consumption and CO2 emissions actually are much
lower in stop-and-go (congested) conditions than in the free flow travel environment.
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Industry sources point out that the Toyota’s Prius hybrid system (with 1.5L Atkinson
cycle IC engine, electronically variable transmission ratios, AC permanent magnet
motor & generator, special NiMH batteries) consumes less fuel and cuts CO2 emissions
in half in congested city driving. According to vehicle design, at light loads the battery
provides the energy to the motor for all-electric operation. While braking, this process is
reversed and the motor acts as a generator, thus recharging the battery (i.e.regenerative
braking). At medium loads, the IC engine comes on line and its power is split in such a
fashion that a portion is used to drive the vehicle and a smaller portion is provided to
the generator for either additional drive energy or to recharge the batteries. At peak load
condition, the battery adds its energy to the motor for maximum performance (10).
The fuel efficiency improvements offered by hybrid vehicle technology, such as the
Toyota Prius, are very attractive. Demonstrations by Toyota have indicated that the
Prius consumed 17% less fuel compared to a typical 1.5L IC engine over the Federal
Test Procedure (FTP) Highway cycle. In the case of urban driving, up to 50% less fuel
was used in spite of severely congested traffic conditions (10).
The foregoing information clearly suggests that for the estimation of GHG emissions in
urban transportation, fuel consumption estimates for the entire urban network must be
disaggregated by vehicle type. Since fuel intensity of a vehicle is affected by the link
average speed, it is necessary to estimate this variable as a part of the network analysis.
CHOICE OF EMME/2
A review of existing modelling frameworks suggests that a number of key issues are to
be addressed in modelling fuel consumption and GHG emissions. Microscopic models
simulate explicit vehicle movements and thus tend to implement fuel consumption
modules that are based on vehicle-following and lane-changing behaviour of drivers.
Macroscopic traffic analysis models consist of fuel consumption functions that are
consistent with the more aggregate performance measures, such as average speed.
Micro-simulation models, owing to their design, provide more accurate estimates of
fuel consumption for facility and limited network application contexts. This category of
model is most suitable for evaluating individual intersections, sections of road, or small
parts of complex networks. In their present form, these models do not provide CO2 or
other greenhouse gas emission information. Fuel consumption outputs, however, can be
used to derive GHG emission estimates.
At the present level of technological development, a modelling framework designed to
incorporate macroscopic representations of traffic is better suited for large area-wide
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studies of multimodal networks. Such models are used for transportation planning, and
their results can be used as inputs to the fuel and emission estimation macros. For the
sake of realism, it is a requirement that reliable estimates of average speed are obtained
at the link level.
Macroscopic models based on average speed philosophy relate fuel consumption to link
average speed. These models normally share network codes with transportation
planning and sometimes with geographic information system (GIS) models. These are
generally best suited for estimating total fuel consumption for large urban traffic
networks. Given that models of this type are suitable for assessing the impacts of
transportation management strategies that result in changes in demand and average
speed, their application for the estimation of fuel and GHG emissions is beneficial.
These models are reasonably accurate within a wide speed range. With appropriate
theoretical formulations and empirical calibration factors, these become applicable to
both very low and freeway cruising speeds.
In the U.S.A., the modelling requirements for air quality studies are under study.
Research in this area is jointly sponsored by the U.S. Department of Transportation, the
Environmental Protection Agency, and the Department of Energy. Improvements are
being made to the macroscopic type of model described above. Publications of this
Travel Model Improvement Program (TMIP) describe the latest information on travel
model speed estimation and post processing methods for air quality analysis.
It is expected that macroscopic transportation planning models will be modified to
develop regional estimates of fuel consumption and greenhouse gas emissions. In
parallel, more detailed micro-simulation models are expected to be developed. Even at
their present state of development, the microscopic models can be used for the analysis
of selective sub-networks.
The interface of urbanized region level transportation models and fuel
consumption/emission models is not without problems. Most travel demand forecasting
models were not developed to provide reliable forecasts of vehicular speeds/delay at the
link level. In addition to their limitation in providing congestion-related changes in
speed, most transportation models do not provide information on vehicle operating
modes (e.g. cold start, hot start, etc.).
Improvements are underway to average speed estimation subroutines of travel
simulation models. Post-processors with potential to further improve speed estimates
are under study. Research in progress indicates that assignment post-processors are one
way of overcoming software and data limitations. A number of post-processor
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techniques that have been used in the U.S.A. are based on the simplification of the
Highway Capacity Manual techniques or incorporate simulation model features.
Urbanized region travel modelling frameworks , such as EMME/2, have been adapted
successfully to track cold start vehicles on a link-by-link basis.
THE USE OF EMME/2 AS A PLATFORM
The EMME/2 offers a framework for travel forecasting. It is used widely for the
simulation of travel demand by all modes on an urban transportation network (i.e.auto,
transit, walk, bicycle, etc.). For the study of demand, demographic and socio-economic
variables as well as characteristics of the transportation network (configuration, travel
time, costs, etc.) are used.
For the estimation of GHG emissions, the starting point is to start with the abstraction of
the urban transportation system in the form of modal networks. The EMME/2 serves as
a framework for this purpose. The detailed coding and network representation
capabilities enable an analyst to define links and vehicle types. From these, as a result
of travel simulation and post-processing of assignment outputs, fuel consumption and
GHG emissions can be found (Figure 2).
Urban
. Fuel
Transportation => Modal Networks => Links & Vehicle Types => Consumption
System
. GHG
Emissions
Figure 2: Conceptual Representation of GHG Emission Estimation Requirement
The following are the steps for the computation of GHG emissions.
1. From EMME/2 assignment, obtain average speed for links (or a series of links),
traffic volume for links disaggregated by vehicle type.
2. Find vehicle-kms for each vehicle category (product of link distance and volume).
3. From average speed and vehicle type, find fuel intensity (by fuel type).
4. From fuel intensity (litres/100km) and vehicle-kms, find total fuel consumption for
each fuel type.
5. From total fuel consumption for each fuel type and vehicle category, find GHG
emissions.
The opportunities and constraints (issues) in implementing these steps are discussed
below.
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Step 1
The EMME/2 assignment (actual or imputed) outputs for a network are:
. simulated vehicles/hour of travel
. riders/hour
. link speeds
. travel times.
From these outputs, we can obtain average speed for links (or a series of links), traffic
volume for links disaggregated by vehicle type.
Step 2
For each link, vehicle kms of travel (VKT) are found according to vehicle categories
(product of link distance and volume). Also, from travel time and link distance, average
travel speed can be computed for each vehicle category (Figure 3). The traffic
information is generated by the EMME/2. The vehicle characteristics that are taken into
account are: weight, engine size. For characterizing new technology vehicles (e.g.
hybrid vehicles), additional identifying codes will be required.
Step 3
From a knowledge of average speed and vehicle type, fuel intensity (by fuel type) can
be found (Figure 4). From the outputs of the equilibrium assignment and other inputs,
volumes and link speed are found. These are based on travel time and distance matrices
generated by the EMME/2. Next, fuel consumption rate is calculated based on average
speed and vehicle characteristics.
For each vehicle type, fuel intensity functions/tables can be incorporated. As noted in
step 2, the average speed is calculated from travel time and distance matrices that reflect
link characteristics. A desirable feature of the post-processor would be to allow the user
to change values of parameters to reflect local conditions of the case study (e.g. vehicle
fleet fuel intensity, the presence or absence of emission inspection programs, cold and
hot start splits, assumptions regarding engine tune ups, etc.).
EMME/2
Traffic Assignment
. Link Average
=> Speed
=> Vehicle Kms of Travel (VKT) by Link
. Traffic Volume
(and Vehicle Type & Link Avg. Speed)
by Vehicle Type
. Fuel Intensity
Figure 3: Estimation of VKT, Vehicle Type, and Link Avg. Speed.
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Step 4
From fuel intensity (litres/100km) and vehicle-kms, total fuel consumption for each
type of fuel is found (Figure 4).
Step 5
From total fuel consumption for each fuel type and vehicle category, GHG emissions
are computed (Figure 4).
.VKT
. GHG Emissions
. Fuel
=> . Fuel Consumption => CO2, CH4, N2O
Intensity
by Fuel Type
Figure 4: Estimation of GHG Emissions
=> GHG
(CO2 Equivalent)
The methodology described above is intended to test the effectiveness of various
mitigation measures in reducing GHG emissions (Figure 5). Additionally, this type of
information is also required to check the cost-effectiveness of GHG mitigation
measures (Figure 6).
Base Case
Network
=> Baseline Fuel Consumption => Baseline GHG
Network
=> Fuel Consumption Change => Mitigation
With Measures
Scenario GHG
(to reduce GHG)
Figure 5: Estimation of GHG Reduction
=> GHG
Reduction
GHG
Mitigation => EMME/2 Assignment => GHG Reduction => Cost- Effectiveness
Measures =====================> Cost of
Mitigation
Figure 6: Cost-Effectiveness Analysis
CONCLUDING REMARKS
1. At the present level of model development, the macroscopic average speed type of
models are best suited to the urbanized region level GHG estimation task.
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2. The EMME/2 provides an appropriate platform for the estimation of GHG
emissions.
3. A number of issues need to be addressed for effective use of the EMME/2 as a
platform. These are: improved post-processors for accommodating the fuel intensity
characteristics of various vehicle types, and the inclusion of computations for the
GHG emissions. A user-friendly macro (post-processor) can be developed for the
automation of repetitive and frequently used commands/calculations.
ACKNOWLEDGEMENTS
This paper is based on research sponsored by the Natural Sciences and Engineering
Research Council of Canada.
REFERENCES
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4 United Nations, “United Nations Agenda 21: Programme of Action for Sustainable
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Berkeley, California and International Energy Agency, Paris, 1997.
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California and International Energy Agency, Paris, 1998.
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Efficiency Standards”, Ottawa, 1995.
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Report”, Prepared for Transport Department, Hong Kong, May 1989.
10 Toyota Motor Company (Canada), “Prius – Toyota Hybrid System”, 1998.
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