gtfs2emis is an R package to estimate the emission levels of public transport vehicles based on General Transit Feed Specification (GTFS) data. The package requires two main inputs: i) public transport data in GTFS standard format; and ii) some basic information on fleet characteristics such as vehicle age, technology, fuel, and Euro stage. As it stands, 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 of how emission levels vary across space, time, and by fleet characteristics. A full description of the methods used in the gtfs2emis model is presented in Vieira, Pereira and Andrade (2022).

You can install gtfs2emis:

# From CRAN
install.packages("gtfs2emis")
library(gtfs2emis)

# 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.

1. 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 a GTFS.zip feed.

2. emission_model() estimates hot-exhaust emissions based on four inputs:

• 1. the result from the transport_model();
• 2. a data.frame with info on fleet characteristics;
• 3. a string indicating which emission factor model should be considered;
• 4. a string indicating which pollutants should be estimated.

To help users analyze the output from emission_model(), the gtfs2emis package has few functions:

3. emis_to_dt() to convert the output of emission_model() from list to data.table.
4. emis_summary() to aggregate emission estimates by the time of the day, vehicle type, or road segment.
5. emis_grid() to spatially aggregate emission estimates using any custom spatial grid or polygons.

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 in 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_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 gtfs2emis will assume that fleet is homogeneously distributed across all routes; OR - A detailed table that (1) brings info on the characteristics of each vehicle and, (2) tells the probability with which each vehicle type is allocated to each transport route.

Here is what a simple fleet table to be used with the EMEP-EEA emission factor model looks like:

fleet_file <- system.file("extdata/irl_dub_fleet.txt", package = "gtfs2emis")

fleet_df <- read.csv(fleet_file)
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+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("NOx","PM10")
)

names(emi_list)
#>  [1] "pollutant"         "veh_type"          "euro"             
#>  [4] "fuel"              "tech"              "slope"            
#>  [7] "load"              "speed"             "EF"               
#> [10] "emi"               "fleet_composition" "tp_model"

Currently, the gtfs2emis package provides a computational method to estimate running exhaust emissions factors based on the following emission factor models:

• Brazil
  • CETESB: 2019 model from the Environmental Company of Sao 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).

gtfs2emis also provides emissions estimates from tire, brake and surface wear using the EMEP/EEA model. The function estimates emissions of particulate matter (PM), encompassing black carbon (BC), which arises from distinct sources (tire, brake, and road surface wear). The focus is on primary particles, which refer to those that are directly emitted, rather than those generated from the re-suspension of previously deposited material.

Check out the guides for learning everything there is to know about all the different features:

• Getting started
• Defining Fleet data
• Exploring Emission Factors
• Exploring Non Exhaust Emission Factors

There are 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, resuspension, and evaporative emissions factors
• Add railway emission factors

citation("gtfs2emis")
#> To cite gtfs2emis in publications use:
#> 
#>   Vieira, J. P. B., Pereira, R. H. M., & Andrade, P. R. (2023). Estimating 
#>      Public Transport Emissions from General Transit Feed Specification Data. 
#>      Transportation Research Part D: Transport and Environment. Volume 119, 
#>      103757. https://doi.org/10.1016/j.trd.2023.103757
#> 
#> A BibTeX entry for LaTeX users is
#>   
#>   @article{vieira2023estimating,
#>     title = {Estimating Public Transport Emissions from {{General Transit Feed Specification}} Data},
#>     author = {Vieira, Jo{\~a}o Pedro Bazzo and Pereira, Rafael H. M. and Andrade, Pedro R.},
#>     year = {2023},
#>     month = jun,
#>     journal = {Transportation Research Part D: Transport and Environment},
#>     volume = {119},
#>     pages = {103757},
#>     issn = {1361-9209},
#>     doi = {10.1016/j.trd.2023.103757},
#>     urldate = {2023-05-06},
#>     langid = {english},
#>     keywords = {Emission factors,Emission models,GTFS,Gtfs2emis,Public transport emissions,Urban bus}
#>   }

The gtfs2emis package is developed by a team at the Institute for Applied Economic Research (IPEA) in collaboration from the National Institute for Space Research (INPE), both from Brazil.