gtfs2emis: Estimating public transport emissions from GTFS data

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).

Installation

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)

Usage and Data requirements

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.

Demonstration on sample data

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.

1. Run transport model

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) 

2. Prepare fleet data

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

3. Run emission model

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"

Emission factor models and pollutants available

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).

List of pollutants available by emission factor models

Fleet characteristics required by each emission factor model

Emissions from road vehicle tire, brake, and surface wear

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.

Learn more

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

Related packages

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.

Future enhancements

• Include cold-start, resuspension, and evaporative emissions factors
• Add railway emission factors

Citation

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}
#>   }

Credits

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.