Can we get any country emission from this package

[Invincibles-group]

How will I get the emission data if I pass the gtfs data for Toronto City and fleet information?

Parameters for the emission model() function include ef usa moves, ef usa emfac, ef europe emep, ef brazil cetesb, and ef brazil scaled euro (scale ef brazil cetesb() based on ef scaled euro()).

I get the following error when I use any of the options: Error: The assertion'veh type' failed: 'Vehicle','Model','in.service','Garage','Built','Seats','Length','Propulsion','Manufacturer', but not'veh type'.

By this error message I have understood like if I pass the ef_usa_moves to the ef_model parameter to the toronto fleet data it is throwing error as it expect the fleet data for the Toronto should be same as ef_usa_moves. But in my case the Toronto fleet data is different when compared USA fleet data.

**```{r}
emi_list <- emission_model(tp_model = tp_model
, ef_model = "ef_usa_moves"
, fleet_data = fleet_df
, pollutant = c("NOx","PM10")
)

[Joaobazzo]

Hi @Invincibles-group
The data on vehicle category is organized differently according to each emission factor. The first thing you need to do is to adjust the data of your local fleet. For instance, if you want to estimate emissions using MOVES emission factor method, you fleet data should match the MOVES emission factor by fuel type and model year.

# for checking how gtfs2emis storage data on MOVES
> library(gtfs2emis)
> data("ef_usa_moves_db")
> head(ef_usa_moves_db)
   fuel_type reference_year model_year pollutant lower_speed_interval upper_speed_interval
1:       CNG           2015       1989       CH4           0.0 [km/h]           4.0 [km/h]
2:       CNG           2015       1989       CH4           4.0 [km/h]          12.1 [km/h]
3:       CNG           2015       1989       CH4          12.1 [km/h]          20.1 [km/h]
4:       CNG           2015       1989       CH4          20.1 [km/h]          28.2 [km/h]
5:       CNG           2015       1989       CH4          28.2 [km/h]          36.2 [km/h]
6:       CNG           2015       1989       CH4          36.2 [km/h]          44.3 [km/h]
                ef source_type id_speed
1: 69.12351 [g/km] Transit Bus        1
2: 41.04421 [g/km] Transit Bus        2
3: 28.99163 [g/km] Transit Bus        3
4: 24.88722 [g/km] Transit Bus        4
5: 21.28728 [g/km] Transit Bus        5
6: 18.18489 [g/km] Transit Bus        6

# Running the transport model
# read GTFS
gtfs_file <- system.file("extdata/bra_cur_gtfs.zip", package = "gtfs2emis")
gtfs <- gtfstools::read_gtfs(gtfs_file) 

# keep a single trip_id to speed up this example
gtfs_small <- gtfstools::filter_by_trip_id(gtfs, trip_id ="4451136")
  
# run transport model
tp_model <- transport_model(gtfs_data = gtfs_small,
                            min_speed = 2,
                            max_speed = 80,
                            new_speed = 20,
                            spatial_resolution = 100,
                            parallel = FALSE)

# Fleet
# Example using US MOVES emission model and fleet
fleet_data_ef_moves <- data.frame( model_year = 2010:2019
                                  , fuel = "D"
                                  , reference_year = 2020
                                  , fleet_composition = rep(0.1,10))
# Example using US MOVES emission model and fleet
emi_moves <- emission_model(tp_model = tp_model
                          , ef_model = "ef_usa_moves"
                          , fleet_data = fleet_data_ef_moves
                          , pollutant = c("CO","PM10","CO2","CH4","NOx")
                          , reference_year = 2020)

If you still have issues, you can upload here few lines of you Toronto fleet data in .txt or .csv format, so I can reproduce it.

[Invincibles-group]

https://transittoronto.ca/bus/8500.shtml

This is the toronto fleet data can you share the step by step from the beginning to get the emission

https://transittoronto.ca/bus/8500.shtml

[Invincibles-group]

can_toronto_fleet.txt

[Joaobazzo]

library(data.table)
library(gtfs2emis)
library(janitor) # clean names

# 1) read fleet-----

# Adjust fleet data
tor_fleet <- data.table::fread("tor_fleet.csv",sep = ";")
names(tor_fleet) <- janitor::make_clean_names(names(tor_fleet))

# 2) Adjust fleet data-----
# manually adjust the model_year data
unique(tor_fleet$built)
tor_fleet[,model_year := built]
tor_fleet[built == "2007-8",model_year := "2007"]
tor_fleet[built == "2007-9",model_year := "2007"]
tor_fleet[built == "2018-9",model_year := "2018"]
tor_fleet[built == "2009-12",model_year := "2009"]
tor_fleet[built == "2011-12",model_year := "2011"]
tor_fleet[built == "2015-16",model_year := "2015"]
tor_fleet[built == "2016-17",model_year := "2016"]
tor_fleet[built == "2017-20",model_year := "2017"]
tor_fleet[built == "2013-14",model_year := "2013"]
tor_fleet[,model_year := as.numeric(model_year)]

# checking `data(ef_usa_moves_db)` there
# is not emission factor for pollutants in the
# year of 1982. For this exercise, I'm removing this 
# vehicle
tor_fleet <- tor_fleet[model_year != 1982,]

# manually adjust the fuel type
unique(tor_fleet$propulsion)
tor_fleet[,fuel := propulsion]
tor_fleet[propulsion == "Diesel",fuel := "D"]
tor_fleet[propulsion == "Gasoline",fuel := "G"]

# depending on the emission process you are 
# looking to estimate, there is no available emission factor
# for Hybrid or Electric vehicle. I am removing
# these vehicles to simplify the analysis.

tor_fleet <- tor_fleet[fuel %in% c("D","G"),]



# In addition, depending on the emission factor method
# you are interested, there are not available data
# for small urban buses (e.g. vehicles of lengths of 6 - 8 m)
# So, for this example, I'm considering that all vehicles 
# has standard sizes.
tor_fleet[,veh_type := "BUS_URBAN_D"]

# Now, we sum the total vehicle by model_year, vehicle type and fuel

tor_fleet <- tor_fleet[,list("number_in_service" = sum(number_in_service))
                       , by = .(model_year,veh_type,fuel)]

# we estimate the fleet composition (proportion by year)
tor_fleet[,fleet_composition := number_in_service/sum(number_in_service)]

# it should sum 1.0 for most of the cases
sum(tor_fleet$fleet_composition) #  1

# 3) read GTFS -----

# read GTFS
gtfs_file <- system.file("extdata/bra_cur_gtfs.zip", package = "gtfs2emis")
gtfs <- gtfstools::read_gtfs(gtfs_file) 

# keep a single trip_id to speed up this example
gtfs_small <- gtfstools::filter_by_trip_id(gtfs, trip_id ="4451136")


# run transport model
tp_model <- transport_model(gtfs_data = gtfs_small,
                            min_speed = 2,
                            max_speed = 80,
                            new_speed = 20,
                            spatial_resolution = 100,
                            parallel = FALSE)
fleet_data_ef_moves <- data.frame(  veh_type = "BUS_URBAN_D"
                                    , model_year = 2010:2019
                                    , fuel = "D"
                                    , reference_year = 2020
                                    , fleet_composition = rep(0.1,10))
# Example using US MOVES emission model and fleet
emi_moves <- emission_model(tp_model = tp_model
                            , ef_model = "ef_usa_moves"
                            , fleet_data = tor_fleet
                            , reference_year = 2020
                            , pollutant = c("CO2","PM10","VOC","CO","NOx"))

# Aggregate total emissions by 'pollutant'
emis_summary(emi_moves,veh_vars = "model_year") 

# by vehicle type
emis_summary(emi_moves,veh_vars = "model_year", by = "vehicle")
emis_summary(emi_moves,veh_vars = c("model_year","fuel"), by = "vehicle")
emis_summary(emi_moves,veh_vars = "model_year", by = "time") 

[tor_fleet.csv](https://github.com/ipeaGIT/gtfs2emis/files/10874929/tor_fleet.csv)

tor_fleet.csv

[Joaobazzo]

Hi @Invincibles-group. I'm closing the discussion here, but if there's an issue reproducing the results you can reopen it.