7.2 GHG Emissions Modelling Procedures
7.2.3 Considerations for Model Development
There are essentially two ways to incorporate GHG emissions modelling capabilities into the TRANS model update. Emissions modelling can be undertaken as a separate ‘post assignment’ process, utilizing assignment results on a global basis or in a specific area, to generate forecasts of emissions. A second approach would be the development of a GHG module within the TRANS model architecture to automate the processes required to generate GHG emissions forecasts. Each approach is discussed in further detail below.
Post Assignment Process of GHG Emissions Modelling
The “post assignment” process to forecast GHG emissions essentially involves the use of two separate software platforms and two separate modelling work streams. The traditional EMME model software (used by the TRANS model) would continue to be used to provide travel forecasts in the National Capital Region (NCR) for each mode of travel covered by the model. Currently the model is being structured to forecast auto and transit trips. Commercial vehicle trips are planned to be treated explicitly as a separate demand matrix based on observed truck counts and commercial vehicle trip matrices. The commercial vehicle demands could then be assigned to the road network and estimates of commercial vehicle VKT can be derived from the assignment results.
The emission model (MOBILE 6.2C software or equivalent) would then be used to generate standard emission rates for GHG and other air pollutants by speed range, vehicle class, and roadway type. Ideally, these emission rates would be based on customized data reflecting conditions in the Ottawa-Gatineau area such as local ambient temperatures, barometric pressure and relative humidity, vehicle fleet composition (type, age, and fuel type), etc., and could be generated for annual average conditions or specified for each month of the year. A forecast of the emissions produced could then be provided on a project specific basis.
This approach would benefit from consistent application of the base assumptions that go into the emissions forecasts that are not related to transportation model outputs. Consistent assumptions with respect to vehicle fleet composition, average temperatures, and other ‘non usage’ factors would minimize the variations between several different analysts applying a wider range of assumptions in undertaking air quality analysis.
A ‘post assignment’ process has been used by the NYSDOT4, where emission rate tables were developed for use in localized and area-wide air quality analysis. The tables provide emission rates for each
3 The Computation of Emissions with MOBILE 6, Yolanda Noriega, University of Montreal and Michael Florian, INRO Consultants Inc., Montréal, QC, Canada, 2005-10-21, 19th International EMME/2 Users' Conference
4 NYSDOT Environmental Procedures Manual, Chapter 1.1, Environmental Analysis Bureau, January, 2001
compound in grams/mile by road class and by speed range, allowing the analyst to estimate the total emissions of each compound by calculating the number of vehicle miles traveled on each type of road facility, in each speed range. Custom tables are provided for each County in the State, allowing the assessment to reflect local weather conditions as well as vehicle fleet composition.
Development of a GHG Module within the TRANS Model
The development of a GHG module within the TRANS Model architecture should be relatively straightforward, based on a review of the past work by J. Armstrong and M. Florian in the Ottawa-Gatineau and Montreal areas respectively. Both studies, discussed above, have implemented custom designed data entry programs and/or EMME macros to extract travel demand forecasting results for use in emissions modelling.
There are a number of different approaches to structuring a process that integrates travel demand forecasting with the estimation of GHG emissions. One possible approach is illustrated in Exhibit 7.1, below.
Exhibit 7.1 Generic Process for Integration of Transportation Model & Emission Model.
Emissions Forecasting Process
There are a number of different processes that may need to be undertaken at various stages of the emissions forecasting process, depending on the degree that the user wishes to customize the MOBILE 6.2C data inputs. Some of these steps could be omitted if the default data in the emissions model is representative of local data, or if reliable local data is not available. A brief discussion of some of these processes is provided below.
Treatment of Engine Starts, Soaks, and Diurnal Assumptions
Engine starts refers to the number of trips that are made in a given period. The use of transportation model can readily provide this data for use in emissions modelling. If the model is not structured to provide 24 hour forecasts, hourly trip departure assumptions or peak hour to daily conversion factors will need to be provided.
Soaking refer to emissions that are released during periods when a vehicle is not running, and are categorized as soaks or hot soaks (referring to a vehicle that has just been turned off). The emissions are highest immediately after the engine is shut down and decrease over time, reaching a baseline level in about an hour.
Diurnal emissions vary with the length of time a vehicle has been soaking (the length of time it has been parked). Diurnal soak time distributions represent the distribution of the length of time that vehicles have been soaking during the analysis period.
The MOBILE software relies on default values to replicate the distribution of travel over the course of a typical day which may be suitable for use in many emissions modelling applications, although results from a tour or activity based transportation model may be able to provide forecasts of these parameters that better reflect future local conditions in the study area.
Vehicle Class Distributions
The MOBILE software calculates emissions for 28 different vehicle classes. While information on the proportion of each vehicle type registered in the study area (NCR) may be available from the provincial transport ministries (Ontario and Québec), the TRANS model will not have the ability to predict vehicle demands for each of the 28 vehicle classes.
The Urban Transportation Emissions Calculator (UTEC), developed by Transport Canada, developed emission rates for 5 on-road vehicle types and 3 rail based types, based on a weighted average of the related MOBILE6.2C vehicle classes. The UTEC requires inputs of light duty automobiles (including trucks, SUV’s and Vans), Light Duty Commercial Vehicles, Heavy Duty Commercial Vehicles, Urban Buses and Trolley Buses, in addition to 3 classes of rail vehicles.
At best, the model will be able to provide forecasts for three aggregate classes: auto, truck and transit vehicles5. The use of multi-class assignment techniques may improve emission forecasting results by allowing the transportation model to provide the estimates of vehicle demands that use each different class of road, by vehicle type (auto, truck). By doing this, the estimates can better reflect the projected mix of traffic using different types of roadways. Thus, roadways that attract a greater share of truck traffic would be expected to generate more emissions than roads with similar operating conditions that are dominated by cars.
Generating transportation forecasts at this level of detail would require well developed commercial vehicle demand matrices capturing both local truck movements and interregional truck movements operating within
5 Transit vehicles will need to be estimated based on service parameters and occupancy factors as the model forecasts transit person-trips rather than vehicle trips.
the NCR. Some data on commercial vehicle movements in the NCR may be available from the provincial transport ministries (Ontario and Québec Commercial Vehicle Surveys), recently undertaken in 2006 / 2007.
The TRANS committee participated in that survey, which included O-D surveys of trucks using highways around the Ottawa-Gatineau region, and some of the major river crossings between Québec and Ontario.
In the absence of detailed commercial vehicle Origin-Destination flows, general assumptions with respect to commercial vehicle percentages by roadway type may need to be developed to estimate the emissions related to truck traffic. These can be link specific, to reflect observed truck shares from traffic count data.
The inherent drawback of this approach is that the commercial vehicle demands are assumed to increase at the same rate as auto traffic on the link, irrespective of network improvements or increased levels of congestion.
Treatment of Intra-Zonal Trips and Centroid Connectors
Centroid connectors are intended to represent the travel undertaken on the local road network between the centroid of a traffic zone and the major road network represented in the model. For emissions modelling, the travel that is undertaken on the local road network is important to recognize as part of forecasting overall emissions in the NCR.
Often where the model is being used to compare alternative transportation improvements or evaluating policy options, the determination of emission levels due to traffic volumes on local roadways may be ignored, i.e. assumed to remain constant between the scenarios being evaluated.
However, in estimating travel on the local road network, two key factors need to be considered:
VKT on Centroid Connectors - Auto and truck traffic using the centroid connectors represents traffic that is occurring on the local road network within the underling traffic zone system. An estimate of the veh-km traveled on these local roads therefore, needs to developed along with the average speed of this traffic. The most common approach to estimating VKT on centroid connectors in the US, utilizes a VKT adjustment factor. The adjustment factor is typically based on local estimates of VKT within a zone (sum of segment volumes x segment lengths for all local roads in zone) compared to the VMT represented on the centroid connectors in the model. This is a factor calibrated for the base year, which is generally applied for all forecast years, assuming the ratio of local travel to centroid travel remains constant over time. Average speeds for the portion of VKT using centroid connectors are typically assumed to be the same as the average posted speed limit on the local network. This approach of course assumes that the local road network is not affected by congestion. VKT for Intra-zonal Trips – Auto and truck demands that start and end within the same zone are not assigned by the model to the road network, although this component of local travel still occurs on the local roads (for auto and truck trips). Estimating the VKT for this component of the travel demand can be completed using an approach similar to the “nearest neighbour” technique used to estimate intra-zonal travel times. Intra-intra-zonal travel time is typically calculated as one-half the average travel time to the adjacent zones. By using distance in this application, the intra-zonal travel distance can be calculated as one-half of the average travel distance to the adjacent zones. Multiplying this average distance by the intra-zonal demand can provide an estimate of the VKT for intra-zonal trips. The average speed for these trips can be calculated based on the average intra-zonal distance divided by the average intra-zonal travel time.Treatment of Transit Trips
Most travel demand models forecast transit person trips or ridership as opposed to transit vehicle demands.
For the purpose of emissions modelling, the VKT generated by transit vehicles needs to be estimated rather than the Passenger-Kilometres of Travel (PKT). The UTEC, developed by Transport Canada, is one
exception, where the input for rail travel use is based on PKT. The documentation for the program does not indicate how the passenger demand is converted to rail based emissions.
Transit vehicle usage can be estimated outside of the modelling process by reviewing transit route and schedule information. In many cases, the route and schedule would not change in response to ridership demand, except on the busiest routes or express routes. Therefore the estimate of transit vehicle travel should also include a check of demands by route to determine in higher frequencies or additional vehicles would be required to serve projected demands.