gtfs2emis is an R package to estimate the emission levels of public transport networks based on GTFS data. The package requires two main inputs: i) public transport data in the GTFS standard format; and ii) some basic information on fleet characteristics such as fleet age, technology, fuel and Euro stage. As it stands, the the package estimates several pollutants (see table below) at high spatial and temporal resolutions. Pollution levels can be calculated for specific transport routes, trips, time of the day or for the transport system as a whole. The output with emission estimates can be extracted in different formats, supporting analysis on how emission levels vary across space, time and by fleet characteristics.
gtfs2emis will soon be on CRAN. In the meantime, you can install the dev version from Github:
# or use the development version with latest features
utils::remove.packages('gtfs2emis')
devtools::install_github("ipeaGIT/gtfs2emis")
library(gtfs2emis)
The gtfs2emis package has two core functions.
-
transport_model()converts GTFS data into a GPS-like table with the space-time positions and speeds of public transport vehicles. The only input required is aGTFS.zipfeed. -
emission_model()estimates hot-exhaust emissions based on four inputs:- a) the result from the
transport_model(); - b) a
data.framewith info on fleet characteristics; - c) a
stringindicating which emission factor model should be considered; - d) a
stringindicating which pollutants should be estimated.
- a) the result from the
To help users analyze the output from emission_model(), the gtfs2emis package has few functions:
emis_summary()to aggregate emission estimates by time of the day, vehicle type or road segment.emis_grid()to spatially aggregate emission estimates using any custom spatial grid or polygons.emis_to_dt()to convert the output ofemission_model()fromlisttodata.table.
See a detailed demonstration of gtfs2emis in this intro Vignette. To illustrate functionality, the package includes small sample data sets of the public transport and fleet of Curitiba (Brazil), Detroit (USA) and Dublin (Ireland). Estimating the emissions of a given public transport system using gtfs2emis can be done three simple steps, as follows.
The first step is to use the transport_model() function to convert GTFS data into a GPS-like table, so that we can get the space-time position and speed of each vehicle of the public transport system at high spatial and temporal resolutions.
# read GTFS.zip
gtfs_file <- system.file("extdata/irl_dub/irl_dub_gtfs.zip", package = "gtfs2emis")
gtfs <- gtfstools::read_gtfs(gtfs_file)
# generate transport model
tp_model <- transport_model(gtfs_data = gtfs,
spatial_resolution = 100,
parallel = TRUE)
The second step is to prepare a data.frame with some characteristics of the public transport fleet. Note that different emission factor models may require information on different fleet characteristics, such as vehicle age, type, Euro standard, technology and fuel. This can be either:
- A simple table with the overall composition of the fleet. In this case, the
gtfs2emiswill assume that fleet is homogeneously distributed across all routes; OR - A detailed table that (1) brings info on the characteristics of each individual vehicle and, (2) tells the probability with which each vehicle type is allocated to each transport route.
Here is how a simple fleet table to be used with the Emep-EEA emission factor model looks like:
fleet_file <- system.file("extdata/irl_dub/irl_dub_fleet.txt", package = "gtfs2emis")
fleet_df <- read.csv(fleet_file)
head(fleet_df)
> veh_type euro fuel N fleet_composition tech
> Ubus Std 15 - 18 t III D 10 0.009 -
> Ubus Std 15 - 18 t IV D 296 0.295 SCR
> Ubus Std 15 - 18 t V D 148 0.147 SCR
> Ubus Std 15 - 18 t VI D 548 0.546 DPF+SCR
In the final step, the emission_model() function to estimate hot exhaust emissions of our public transport system. Here, the user needs to pass the results from transport_model(), some fleet data as described above, and select which emission factor model and pollutants should be considered (see the options available below). The output from emission_model() is a list with several vectors and data.frames with emission estimates and related information such as vehicle variables (fuel, age, tech, euro, fleet_composition), travel variables (slope, load, gps) or pollution (EF, emi).
emi_list <- emission_model(tp_model = tp_model
, ef_model = "ef_europe_emep"
, fleet_data = fleet_df
, pollutant = c("CO2","PM10")
)
names(emi_list)Currently the gtfs2emis package provides a computational method to estimate running exhaust emissions factors based on the following emission factor models:
- Brazil
- CETESB: 2017 model from the Environmental Company of São Paulo (CETESB)
- Europe
- EMEP/EEA: European Monitoring and Evaluation Programme, developed by the European Environment Agency (EEA).
- United States
- EMFAC2017/CARB: California Emission Factor model, developed by the California Air Resources Board (CARB).
- MOVES3/EPA: Vehicle Emission Simulator, developed by the Environmental Protection Agency (EPA).
| Source | Pollutants |
|---|---|
| CETESB | CH4, CO2, PM10, N2O, KML, FC (Fuel Consumption), gD/KWH, gCO2/KWH, CO, HC, NMHC, NOx, NO2, NO, RCHO, ETOH, FS(Fuel Sales) and NH3 |
| EMFAC2017/CARB | CH4, CO, CO2, N2O, NOx, PM10, PM2.5, SOX, TOG (Total Organic Gases), ROG (Reactive Organic Gases) |
| EMEP/EEA | FC, CO2, CO, NOx, VOC, PM10, EC, CH4, NH3, N2O, and FC |
| MOVES3/EPA | CH4, CO, CO2, EC, HONO, N2O, NH3, NH4, NO, NO2, NO3, NOx, PM10, PM25, SO2, THC, TOG and VOC |
| Source | Buses | Characteristics |
|---|---|---|
| CETESB | Micro, Standard, Articulated | Age, Fuel, EURO stantard |
| EMEP/EAA | Micro, Standard, Articulated | Fuel, EURO stantard, technology, load, slope |
| EMFAC2017/CARB | Urban Buses | Age, Fuel |
| MOVES3/EPA | Urban Buses | Age, Fuel |
There several others transport emissions models available for different purposes (see below). As of today, gtfs2emis is the only method with the capability to estimate emissions of public transport systems using GTFS data.
- R: vein Bottom-up and top-down inventory using GPS data.
- R: EmissV Top-down inventory.
- Python: PythonEmissData Jupyter notebook to estimate simple top-down emissions.
- Python: YETI YETI - Yet Another Emissions From Traffic Inventory
- Python: mobair bottom-up model using GPS data.
- Include cold-start, and evaporative emissions factors
- Add railway emission factors
The gtfs2emis package is developed by a team at the Institute for Applied Economic Research (Ipea) with collaboration from the National Institute for Space Research (INPE), both from Brazil. You can cite this package as:
- Bazzo, J.P.; Pereira, R.H.M.; Andrade, P.R.; (2020) gtfs2emis: Generating estimates of public transport emissions from GTFS data