The goal of dftTrafficCounts is to provide easy access to datasets provided by UK Department for Transport (DfT) on their roadtraffic.dft.gov.uk website. See the Metadata document for details.
# install.packages("devtools")
devtools::install_github("itsleeds/dftTrafficCounts")Load the package (and packages we’ll use below) as follows:
library(dplyr)
library(ggplot2)
library(dftTrafficCounts)You can read-in the data for a single counter point as follows:
d_counter = dtc_import()
#> Rows: 20
#> Columns: 31
#> Delimiter: ","
#> chr [ 8]: region_name, local_authority_name, road_name, road_type, start_junction_road_nam...
#> dbl [23]: count_point_id, year, region_id, local_authority_id, easting, northing, latitude...
#>
#> Use `spec()` to retrieve the guessed column specification
#> Pass a specification to the `col_types` argument to quiet this message
names(d_counter)
#> [1] "count_point_id" "year"
#> [3] "region_id" "region_name"
#> [5] "local_authority_id" "local_authority_name"
#> [7] "road_name" "road_type"
#> [9] "start_junction_road_name" "end_junction_road_name"
#> [11] "easting" "northing"
#> [13] "latitude" "longitude"
#> [15] "link_length_km" "link_length_miles"
#> [17] "estimation_method" "estimation_method_detailed"
#> [19] "pedal_cycles" "two_wheeled_motor_vehicles"
#> [21] "cars_and_taxis" "buses_and_coaches"
#> [23] "lgvs" "hgvs_2_rigid_axle"
#> [25] "hgvs_3_rigid_axle" "hgvs_3_or_4_articulated_axle"
#> [27] "hgvs_4_or_more_rigid_axle" "hgvs_5_articulated_axle"
#> [29] "hgvs_6_articulated_axle" "all_hgvs"
#> [31] "all_motor_vehicles"
head(d_counter)
#> # A tibble: 6 x 31
#> count_point_id year region_id region_name local_authority… local_authority…
#> <dbl> <dbl> <dbl> <chr> <dbl> <chr>
#> 1 74816 2019 8 Yorkshire … 63 Leeds
#> 2 74816 2018 8 Yorkshire … 63 Leeds
#> 3 74816 2017 8 Yorkshire … 63 Leeds
#> 4 74816 2016 8 Yorkshire … 63 Leeds
#> 5 74816 2015 8 Yorkshire … 63 Leeds
#> 6 74816 2014 8 Yorkshire … 63 Leeds
#> # … with 25 more variables: road_name <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>, estimation_method <chr>,
#> # estimation_method_detailed <chr>, pedal_cycles <dbl>,
#> # two_wheeled_motor_vehicles <dbl>, cars_and_taxis <dbl>,
#> # buses_and_coaches <dbl>, lgvs <dbl>, hgvs_2_rigid_axle <dbl>,
#> # hgvs_3_rigid_axle <dbl>, hgvs_3_or_4_articulated_axle <dbl>,
#> # hgvs_4_or_more_rigid_axle <dbl>, hgvs_5_articulated_axle <dbl>,
#> # hgvs_6_articulated_axle <dbl>, all_hgvs <dbl>, all_motor_vehicles <dbl>Read-in the data for major roads as follows:
d_roads = dtc_import_roads()
names(d_roads)Read-in data for all local authorities as follows:
d_las = dtc_import_la()
#> Rows: 5,529
#> Columns: 6
#> Delimiter: ","
#> dbl [6]: local_authority_id, year, link_length_km, link_length_miles, cars_and_taxis, al...
#>
#> Use `spec()` to retrieve the guessed column specification
#> Pass a specification to the `col_types` argument to quiet this message
head(d_las)
#> # A tibble: 6 x 7
#> local_authority… year link_length_km link_length_mil… cars_and_taxis
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 132 1993 385. 239. 287107347.
#> 2 170 1993 493. 306. 604140896.
#> 3 171 1993 675. 420. 446328949.
#> 4 163 1993 846. 526. 668148078.
#> 5 164 1993 524. 326. 343540976.
#> 6 156 1993 542. 337. 409189896
#> # … with 2 more variables: all_motor_vehicles <dbl>, local_authority_name <chr>
nrow(d_las)
#> [1] 5529
length(unique(d_las$local_authority_id))
#> [1] 210We can plot the results as follows:
las_of_interest = c("Leeds", "Derby", "Southampton",
"Nottingham", "Birmingham")
d_las
#> # A tibble: 5,529 x 7
#> local_authority… year link_length_km link_length_mil… cars_and_taxis
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 132 1993 385. 239. 287107347.
#> 2 170 1993 493. 306. 604140896.
#> 3 171 1993 675. 420. 446328949.
#> 4 163 1993 846. 526. 668148078.
#> 5 164 1993 524. 326. 343540976.
#> 6 156 1993 542. 337. 409189896
#> 7 74 1993 933. 580. 1024182940
#> 8 198 1993 550. 342. 319820060.
#> 9 193 1993 456. 283. 291962403.
#> 10 169 1993 714. 443. 561704370.
#> # … with 5,519 more rows, and 2 more variables: all_motor_vehicles <dbl>,
#> # local_authority_name <chr>
d_las_of_interest = d_las %>%
filter(local_authority_name %in% las_of_interest)
d_las %>%
ggplot() +
geom_line(aes(year, all_motor_vehicles, group = local_authority_name), colour = "grey") +
geom_line(aes(year, all_motor_vehicles, colour = local_authority_name), data = d_las_of_interest, size = 1.2)
u = "http://data.dft.gov.uk/road-traffic/dft_traffic_counts_raw_counts.zip"
d = dtc_import(u = u)This is a large dataset covering all local authorities in Great Britain:
nrow(d)
#> [1] 4337901
ncol(d)
#> [1] 34
names(d)
#> [1] "count_point_id" "direction_of_travel"
#> [3] "year" "count_date"
#> [5] "hour" "region_id"
#> [7] "local_authority_id" "road_name"
#> [9] "road_category" "road_type"
#> [11] "start_junction_road_name" "end_junction_road_name"
#> [13] "easting" "northing"
#> [15] "latitude" "longitude"
#> [17] "link_length_km" "link_length_miles"
#> [19] "sequence" "ramp"
#> [21] "pedal_cycles" "two_wheeled_motor_vehicles"
#> [23] "cars_and_taxis" "buses_and_coaches"
#> [25] "lgvs" "hgvs_2_rigid_axle"
#> [27] "hgvs_3_rigid_axle" "hgvs_4_or_more_rigid_axle"
#> [29] "hgvs_3_or_4_articulated_axle" "hgvs_5_articulated_axle"
#> [31] "hgvs_6_articulated_axle" "all_hgvs"
#> [33] "all_motor_vehicles" "local_authority_name"
head(d$local_authority_name)
#> [1] "Lancashire" "Lancashire" "Lancashire" "Lancashire" "Lancashire"
#> [6] "Lancashire"We can look at how patterns change over time for the whole dataset.
Lets look at the number of count entries for each year.
summary(d$count_date)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> "2000-03-17" "2005-05-13" "2009-06-26" "2010-01-28" "2015-04-24" "2019-10-18"
d_year_count = d %>% count(year)
d_day_count = d %>% count(count_date)
plot(d_day_count)
# could become package function
tar_read(summary_mode) %>%
ggplot() +
geom_line(aes(year, value, colour = name)) +
scale_y_log10()
And by local authorities:
d_sample = d %>% filter(local_authority_name %in% las_of_interest)
d_summary_la = d_sample %>%
select(pedal_cycles:buses_and_coaches | matches("year|local_authority_name")) %>%
group_by(year, local_authority_name) %>%
summarise_all(sum) %>%
tidyr::pivot_longer(cols = pedal_cycles:buses_and_coaches)
ggplot(d_summary_la) +
geom_line(aes(year, value, colour = name)) +
facet_wrap(~local_authority_name) +
scale_y_log10()
You can reproduce the work presented here using the targets package:
targets::tar_make()Parts of the project are updated as follows:
tar_visnetwork()
A summary of the raw DfT data is shown below:
print(tar_read(summary_dft))
#> count_point_id direction_of_travel year count_date
#> Min. : 51 Length:4337901 Min. :2000 Min. :2000-03-17
#> 1st Qu.: 46110 Class :character 1st Qu.:2005 1st Qu.:2005-05-13
#> Median :810147 Mode :character Median :2009 Median :2009-06-26
#> Mean :529590 Mean :2010 Mean :2010-01-28
#> 3rd Qu.:945786 3rd Qu.:2015 3rd Qu.:2015-04-24
#> Max. :999999 Max. :2019 Max. :2019-10-18
#> hour region_id local_authority_id road_name
#> Min. : 0.0 Min. : 1.000 Min. : 1.0 Length:4337901
#> 1st Qu.: 9.0 1st Qu.: 4.000 1st Qu.: 67.0 Class :character
#> Median :12.0 Median : 7.000 Median : 97.0 Mode :character
#> Mean :12.5 Mean : 6.157 Mean :102.5
#> 3rd Qu.:15.0 3rd Qu.: 9.000 3rd Qu.:141.0
#> Max. :18.0 Max. :11.000 Max. :210.0
#> road_category road_type start_junction_road_name
#> Length:4337901 Length:4337901 Length:4337901
#> Class :character Class :character Class :character
#> Mode :character Mode :character Mode :character
#>
#>
#>
#> end_junction_road_name easting northing latitude
#> Length:4337901 Min. : 70406 Min. : 10240 Min. :49.91
#> Class :character 1st Qu.:367870 1st Qu.: 178500 1st Qu.:51.49
#> Mode :character Median :432710 Median : 275950 Median :52.36
#> Mean :432488 Mean : 302533 Mean :52.61
#> 3rd Qu.:510541 3rd Qu.: 396995 3rd Qu.:53.47
#> Max. :655040 Max. :1209448 Max. :60.76
#> longitude link_length_km link_length_miles sequence
#> Min. :-7.4431 Length:4337901 Length:4337901 Length:4337901
#> 1st Qu.:-2.4816 Class :character Class :character Class :character
#> Median :-1.5119 Mode :character Mode :character Mode :character
#> Mean :-1.5373
#> 3rd Qu.:-0.3847
#> Max. : 2.0000
#> ramp pedal_cycles two_wheeled_motor_vehicles
#> Length:4337901 Min. : 0.000 Min. : 0.000
#> Class :character 1st Qu.: 0.000 1st Qu.: 0.000
#> Mode :character Median : 0.000 Median : 1.000
#> Mean : 2.873 Mean : 4.982
#> 3rd Qu.: 2.000 3rd Qu.: 5.000
#> Max. :2207.000 Max. :768.000
#> cars_and_taxis buses_and_coaches lgvs hgvs_2_rigid_axle
#> Min. : 0.0 Min. : 0.000 Min. : 0.00 Min. : 0.00
#> 1st Qu.: 44.0 1st Qu.: 0.000 1st Qu.: 7.00 1st Qu.: 0.00
#> Median : 195.0 Median : 2.000 Median : 29.00 Median : 3.00
#> Mean : 417.2 Mean : 5.662 Mean : 71.33 Mean : 14.43
#> 3rd Qu.: 517.0 3rd Qu.: 7.000 3rd Qu.: 82.00 3rd Qu.: 14.00
#> Max. :9709.0 Max. :1425.000 Max. :5811.00 Max. :2327.00
#> hgvs_3_rigid_axle hgvs_4_or_more_rigid_axle hgvs_3_or_4_articulated_axle
#> Min. : -3.000 Min. : 0.000 Min. : 0.00
#> 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.00
#> Median : 0.000 Median : 0.000 Median : 0.00
#> Mean : 2.368 Mean : 2.597 Mean : 2.14
#> 3rd Qu.: 2.000 3rd Qu.: 2.000 3rd Qu.: 1.00
#> Max. :290.000 Max. :796.000 Max. :927.00
#> hgvs_5_articulated_axle hgvs_6_articulated_axle all_hgvs
#> Min. : 0.000 Min. : 0.000 Min. : 0.00
#> 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 1.00
#> Median : 0.000 Median : 0.000 Median : 5.00
#> Mean : 7.449 Mean : 7.459 Mean : 36.45
#> 3rd Qu.: 2.000 3rd Qu.: 2.000 3rd Qu.: 26.00
#> Max. :1107.000 Max. :1430.000 Max. :2691.00
#> all_motor_vehicles local_authority_name
#> Min. : 0.0 Length:4337901
#> 1st Qu.: 54.0 Class :character
#> Median : 245.0 Mode :character
#> Mean : 535.6
#> 3rd Qu.: 650.0
#> Max. :10905.0