# 1. Introduction

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.

# 2. Simple or detailed fleet data table

The first thing to have in mind is that the fleet data.frame 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 brings info on the proportion with which each vehicle type is allocated to each transport route.

### Example of simple fleet table

Here 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")
#>             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

### Example of detailed fleet table

This 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")
#>   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    D

# 3. Fleet characteristics vary by emission factor model

Please 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

# 4. Examples of fleet data tables

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.

## 4.1 Brazil: Environmental Company of Sao Paulo (CETESB):

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               0.2

## 4.2 Europe: EMEP - European Environment Agency (EEA)

Based 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               0.1

## 4.3 United States: EMFAC2017 - California Air Resources Board (CARB)

Based 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               0.2

## 4.4 United States: MOVES3 - Environmental Protection Agency (EPA)

Based 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               0.2