The goal of trafficsim is to simulate traffic levels on the network starting with OD data.
To set-up the project with {targets} we ran the following commands:
library(targets)use_targets()To visualise the project data processing stages run the following:
tar_visnetwork()To re-run the code in this project, use the following command:
tar_make()For debugging, it’s useful to be able to load an object from the
pipeline. Do this with tar_load().
tar_load(clean_traffic_data)
clean_traffic_data
#> # A tibble: 5 × 35
#> Count_point_id Direc…¹ Year Count_date hour Regio…² Regio…³ Regio…⁴
#> <dbl> <chr> <dbl> <dttm> <dbl> <dbl> <chr> <chr>
#> 1 37778 S 2011 2011-06-07 00:00:00 11 10 West M… E12000…
#> 2 37778 S 2011 2011-06-07 00:00:00 12 10 West M… E12000…
#> 3 37778 S 2011 2011-06-07 00:00:00 13 10 West M… E12000…
#> 4 37778 S 2011 2011-06-07 00:00:00 14 10 West M… E12000…
#> 5 37778 S 2011 2011-06-07 00:00:00 15 10 West M… E12000…
#> # … with 27 more variables: Local_authority_id <dbl>,
#> # Local_authority_name <chr>, Local_authority_code <chr>, Road_name <chr>,
#> # Road_category <chr>, Road_type <chr>, Start_junction_road_name <chr>,
#> # End_junction_road_name <chr>, Easting <dbl>, Northing <dbl>,
#> # Latitude <dbl>, Longitude <dbl>, Link_length_km <dbl>,
#> # Link_length_miles <dbl>, Pedal_cycles <dbl>,
#> # Two_wheeled_motor_vehicles <dbl>, Cars_and_taxis <dbl>, …