When using the gtfs2emis package to estimate the
emission levels of a given public transport system, users are required
to input data.frame with a few characteristics of the
public transport fleet, such as age or vehicle type. This vignette
explains how users can build this input by showing practical examples
for fleet data in Brazilian, European, and North American cities.
The first thing to have in mind is that the fleet
data.frame can be either:
gtfs2emis will assume that fleet
is homogeneously distributed across all routes; ORHere is an example of a simple fleet table that tells us the
characteristics of the urban buses of Dublin, Ireland. The
N and fleet_composition columns tell us,
respectively, the absolute number and the proportion of buses with each
combination of the following characteristics: vehicle type, Euro
standard, technology, and fuel. Note that
sum(fleet_df$fleet_composition) has to be equal to 1.
simple_fleet_file <- system.file("extdata/irl_dub_fleet.txt", package = "gtfs2emis")
simple_fleet_df <- read.csv(simple_fleet_file)
head(simple_fleet_df)
#> veh_type euro fuel N fleet_composition tech
#> 1 Ubus Std 15 - 18 t III D 10 0.00998004 -
#> 2 Ubus Std 15 - 18 t IV D 296 0.29540918 SCR
#> 3 Ubus Std 15 - 18 t V D 148 0.14770459 SCR
#> 4 Ubus Std 15 - 18 t VI D 548 0.54690619 DPF+SCRThis other table illustrates what a detailed fleet data table looks
like, using the example of the city of Curitiba, Brazil. Here, the
N column also tells us the absolute number of buses with
each combination of vehicle characteristics. However, note that this
table brings a shape_id column. These columns indicate
which specific vehicles should be allocated to run on predefined
shape_ids of the GTFS data. For example, it allows users to
assign articulated buses to specific routes in the transport
network.
detailed_fleet_file <- system.file("extdata/bra_cur_fleet.txt", package = "gtfs2emis")
detailed_fleet_df <- read.csv(detailed_fleet_file)
head(detailed_fleet_df)
#> year euro shape_id type_name_br veh_type total fuel
#> 1 2006 III 1849 BUS_URBAN_D Ubus Std 15 - 18 t 2 D
#> 2 2011 III 1849 BUS_URBAN_D Ubus Std 15 - 18 t 4 D
#> 3 2018 V 1733 BUS_MICRO_D Ubus Midi <=15 t 1 D
#> 4 2011 III 1735 BUS_URBAN_D Ubus Std 15 - 18 t 3 D
#> 5 2018 V 1735 BUS_MICRO_D Ubus Midi <=15 t 2 D
#> 6 2008 III 1735 BUS_MICRO_D Ubus Midi <=15 t 2 DPlease note that the columns in your fleet data table should differ depending on the data requirements of the emission factor model the user wants to consider. For example, the emission factor models for US cities (EMFAC2017 and MOVES3), developed by CARB and EPA, only require information on the type of bus, the fuel used, and age of the vehicle. Meanwhile, the EMEP model developed by the European Environment Agency requires much more info, including vehicle type, Euro standard, technology, and fuel. It also allows users to consider the passenger load and slope of streets.
To check which columns and sets of vehicle characteristics are required by each emission factor model, the user can read the documentation of the emission factor functions listed in the table below:
| Emission factor function | Region | Source | Type of buses | Other required characteristics |
|---|---|---|---|---|
ef_brazil_cetesb() |
Brazil | CETESB | Micro, Standard, Articulated | Age, Fuel, EURO stage |
ef_europe_emep() |
Europe | EMEP/EEA | Micro, Standard, Articulated | Fuel, EURO stage, technology, load, slope |
ef_usa_moves() |
US | EMFAC2017/CARB | Urban Buses | Age, Fuel |
ef_usa_emfac() |
US | MOVES3/EPA | Urban Buses | Age, Fuel |
Now here are a few examples of data.frames with the
fleet characteristics required by different emission factor models. Note
that these examples are built as a simple fleet table that includes the
fleet_composition, indicating what proportion of the fleet
is represented by vehicles with each characteristic.
Based on the 2019 data from the emission factor model of CETESB.
fleet_data_ef_cetesb <- data.frame( veh_type = c("BUS_MICRO_D", "BUS_URBAN_D", "BUS_ARTIC_D")
, model_year = c(2010, 2012, 2018)
, fuel = rep("D", 3)
, fleet_composition = c(0.4, 0.4, 0.2))
fleet_data_ef_cetesb
#> veh_type model_year fuel fleet_composition
#> 1 BUS_MICRO_D 2010 D 0.4
#> 2 BUS_URBAN_D 2012 D 0.4
#> 3 BUS_ARTIC_D 2018 D 0.2Based on the European Monitoring and Evaluation Programme (EMEP), developed by EEA.
fleet_data_ef_europe <- data.frame( veh_type = c("Ubus Midi <=15 t"
,"Ubus Std 15 - 18 t"
,"Ubus Artic >18 t")
, euro = c("III","IV","V")
, fuel = rep("D",3)
, tech = c("-","SCR","SCR")
, fleet_composition = c(0.4,0.5,0.1)) #
fleet_data_ef_europe
#> veh_type euro fuel tech fleet_composition
#> 1 Ubus Midi <=15 t III D - 0.4
#> 2 Ubus Std 15 - 18 t IV D SCR 0.5
#> 3 Ubus Artic >18 t V D SCR 0.1Based on the California Emission Factor model (EMFAC2017), developed by CARB.
fleet_data_ef_emfac <- data.frame( veh_type = "BUS_URBAN_D"
, model_year = 2011:2015
, fuel = "D"
, calendar_year = 2019
, fleet_composition = rep(0.2,5))
fleet_data_ef_emfac
#> veh_type model_year fuel calendar_year fleet_composition
#> 1 BUS_URBAN_D 2011 D 2019 0.2
#> 2 BUS_URBAN_D 2012 D 2019 0.2
#> 3 BUS_URBAN_D 2013 D 2019 0.2
#> 4 BUS_URBAN_D 2014 D 2019 0.2
#> 5 BUS_URBAN_D 2015 D 2019 0.2Based on the Motor Vehicle Emission Simulator (MOVES3 Model), developed by EPA.
fleet_data_ef_moves <- data.frame( veh_type = "BUS_URBAN_D"
, model_year = 2011:2015
, fuel = "D"
, calendar_year = 2016
, fleet_composition = rep(0.2,5))
fleet_data_ef_moves
#> veh_type model_year fuel calendar_year fleet_composition
#> 1 BUS_URBAN_D 2011 D 2016 0.2
#> 2 BUS_URBAN_D 2012 D 2016 0.2
#> 3 BUS_URBAN_D 2013 D 2016 0.2
#> 4 BUS_URBAN_D 2014 D 2016 0.2
#> 5 BUS_URBAN_D 2015 D 2016 0.2Check out our extra guide:
If you have any suggestions or want to report an error, please visit the package GitHub page.