r5r- 100 random ways
Jesper
10/7/2021
r5r traveltimes example
100 random locations on Vesterbro to RÃ¥dhusplads. So sample polygon and use r5r to get traveltimes.
library(tmap)## Warning: package 'tmap' was built under R version 4.0.5 library(sf)## Warning: package 'sf' was built under R version 4.0.5## Linking to GEOS 3.9.0, GDAL 3.2.1, PROJ 7.2.1 wd <- "C://Users//np83zg//OneDrive - Aalborg Universitet//Skrivebord//cars-master//rspace_osm//cppRouting//"
file_name <- "LTMzone_niveau2_m_udland.shp"
setwd(wd)
shp_ntm <- st_read(file_name)## Reading layer `LTMzone_niveau2_m_udland' from data source `C:\Users\np83zg\OneDrive - Aalborg Universitet\Skrivebord\cars-master\rspace_osm\cppRouting\LTMzone_niveau2_m_udland.shp' using driver `ESRI Shapefile'
## Simple feature collection with 1278 features and 15 fields (with 7 geometries empty)
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -5086456 ymin: -7164669 xmax: 6029458 ymax: 9490738
## Projected CRS: ETRS89 / UTM zone 32N plot(shp_ntm[,c("zoneid","geometry")])
# We only need Denmark
selector <- shp_ntm$CNTR_CODE=="DK"
colnames <- c("zoneid","geometry")
plot(shp_ntm[selector,colnames])
colnames <- c("GruppeID","geometry")
plot(shp_ntm[selector,colnames])
# Limit the ntm-zones to Denmark ... we ignore transport
# in and out of the country ...
ntm <- shp_ntm[selector,]
ntm <- ntm[!is.na(ntm$komnr),]
length(unique(ntm$komnr))## [1] 98 length(unique(ntm$zoneid))## [1] 907 colnames <- c("zoneid","zonenavn","komnr","Shape_Area","geometry")
ntm <- ntm[,colnames]
tm_shape(ntm[,c("komnr","geometry")]) +
tm_polygons("komnr")
# minor data transformations (crs,area)
ntm <- st_transform(ntm,crs=4326)
# Test the data column Shape_Area
ss <- st_area(ntm)
plot(ss,ntm$Shape_Area)
# Recode to km^2 and lower precision
ntm$area <- round(st_area(ntm),0)
ntm$Shape_Area <- NULL
# Check centroid plot ... where are there most zones
centroids <- st_centroid(ntm$geometry)## Warning in st_centroid.sfc(ntm$geometry): st_centroid does not give correct
## centroids for longitude/latitude data centroids <- st_as_sf(centroids)
centroids$id <- ntm$zoneid
plot(centroids)
#st_write(ntm,dsn="ntm.shp",append=TRUE)
# Make a coordinate matrix ...
xy <- matrix(unlist(unclass(st_centroid(ntm$geometry))),ncol=2,byrow=2)## Warning in st_centroid.sfc(ntm$geometry): st_centroid does not give correct
## centroids for longitude/latitude data id <- ntm$zoneid
# Create the coordinate dataset ...
coord_df <- data.frame(ID=id,X=xy[,1],Y=xy[,2])
coord_df## ID X Y
## 1 530030 8.871359 55.75216
## 2 480050 10.146158 55.46160
## 3 810120 10.268959 57.18203
## 4 563000 8.411613 55.40972
## 5 813130 10.326862 57.32651
## 6 510020 9.406825 55.30736
## 7 657220 8.944937 56.29289
## 8 860120 10.262853 57.48261
## 9 479250 10.596682 54.99552
## 10 561120 8.500821 55.48454
## 11 766050 9.701972 55.76091
## 12 766010 10.001091 55.80396
## 13 621250 9.337882 55.50978
## 14 482010 10.868764 55.03598
## 15 665020 8.388776 56.49694
## 16 825000 11.019810 57.27361
## 17 846130 9.911194 56.73223
## 18 773030 8.687401 56.75273
## 19 707060 10.765538 56.46499
## 20 849220 9.373810 57.06340
## 21 840040 9.726481 56.87394
## 22 740270 9.341706 56.28654
## 23 779210 8.910908 56.59143
## 24 575010 9.176948 55.53722
## 25 153020 12.399966 55.64595
## 26 155010 12.645998 55.59264
## 27 550030 8.988779 55.04865
## 28 820060 9.282866 56.89556
## 29 791330 9.669624 56.48331
## 30 860320 9.815786 57.38353
## 31 851410 10.181520 56.92016
## 32 851520 10.197877 57.07338
## 33 561340 8.760347 55.40819
## 34 561360 8.600332 55.50572
## 35 561310 8.782114 55.27618
## 36 740160 9.525393 56.09859
## 37 540240 9.669036 54.95880
## 38 573210 8.341550 55.73775
## 39 751110 10.211748 56.14704
## 40 751720 10.005741 56.14471
## 41 573120 8.606518 55.71976
## 42 615240 9.744738 55.94118
## 43 849130 9.659166 57.20580
## 44 813320 10.401792 57.62779
## 45 621230 9.431291 55.36093
## 46 210050 12.428980 55.92125
## 47 190040 12.384472 55.78659
## 48 615250 9.605890 55.98578
## 49 615210 9.949584 55.93497
## 50 740220 9.734835 56.20338
## 51 846300 10.006689 56.63579
## 52 846110 10.259664 56.77743
## 53 706260 10.482496 56.16998
## 54 706250 10.554949 56.23673
## 55 706240 10.686179 56.20228
## 56 706230 10.704505 56.17923
## 57 706210 10.735553 56.28334
## 58 791250 9.351620 56.44825
## 59 791240 9.397207 56.41978
## 60 791220 9.395458 56.47806
## 61 791230 9.398308 56.44658
## 62 165020 12.357438 55.66314
## 63 730320 10.033075 56.46303
## 64 730330 10.037180 56.48409
## 65 730220 9.856265 56.48993
## 66 165010 12.354529 55.68677
## 67 169030 12.267121 55.68302
## 68 223010 12.525771 55.87857
## 69 175010 12.463392 55.66847
## 70 175020 12.447439 55.67566
## 71 175030 12.449520 55.68601
## 72 163030 12.419033 55.74584
## 73 163020 12.430591 55.73113
## 74 175040 12.453191 55.69822
## 75 175050 12.438445 55.70382
## 76 190020 12.364249 55.83163
## 77 190010 12.403058 55.81451
## 78 190030 12.356390 55.81249
## 79 190060 12.335879 55.77454
## 80 190050 12.400881 55.76382
## 81 250040 12.086226 55.86383
## 82 250030 12.078520 55.83066
## 83 250020 12.102134 55.80236
## 84 102770 12.487041 55.67000
## 85 102760 12.487435 55.65882
## 86 102750 12.505925 55.64686
## 87 102810 12.553252 55.66811
## 88 102820 12.544023 55.66769
## 89 102650 12.473906 55.69998
## 90 102130 12.572096 55.67580
## 91 102830 12.552438 55.65938
## 92 102840 12.532940 55.65857
## 93 102850 12.545402 55.65191
## 94 751310 10.218153 56.04079
## 95 751120 10.207024 56.16455
## 96 751450 10.108479 56.19286
## 97 751420 10.163270 56.15237
## 98 751430 10.096037 56.15406
## 99 751710 10.039882 56.20154
## 100 751440 10.147936 56.18063
## 101 751410 10.169514 56.16659
## 102 751350 10.077686 56.10715
## 103 751340 10.118802 56.08695
## 104 751330 10.103734 56.03165
## 105 751320 10.188693 56.07574
## 106 820010 9.456396 56.69795
## 107 751540 10.186269 56.19669
## 108 751530 10.200054 56.17691
## 109 751520 10.226498 56.20705
## 110 751510 10.242933 56.19344
## 111 820040 9.509200 56.79991
## 112 846210 9.802007 56.63749
## 113 846230 9.795316 56.58655
## 114 846220 9.688601 56.65432
## 115 791210 9.447578 56.45447
## 116 820070 9.304281 56.97643
## 117 820050 9.434145 56.86245
## 118 621220 9.556091 55.38633
## 119 621210 9.595463 55.45855
## 120 621240 9.331509 55.42007
## 121 740240 9.551406 56.29024
## 122 740260 9.429084 56.25757
## 123 740250 9.466740 56.21951
## 124 740210 9.681362 56.14840
## 125 740230 9.673873 56.25754
## 126 740140 9.494361 56.18662
## 127 740170 9.454051 56.04296
## 128 740120 9.558853 56.17950
## 129 740110 9.601764 56.19436
## 130 621120 9.471906 55.52057
## 131 621130 9.481521 55.50522
## 132 621170 9.441595 55.48021
## 133 621180 9.441090 55.50217
## 134 621140 9.478512 55.49132
## 135 607010 9.774976 55.60769
## 136 561220 8.417836 55.53356
## 137 573150 8.474482 55.62936
## 138 661260 8.422697 56.36791
## 139 657310 8.865377 56.04602
## 140 756230 9.066558 55.91478
## 141 756220 9.241434 56.00162
## 142 661210 8.626220 56.34530
## 143 787240 8.322162 56.81321
## 144 440030 10.558656 55.45926
## 145 440010 10.660435 55.52590
## 146 440020 10.662151 55.44289
## 147 479210 10.685249 55.04282
## 148 410070 9.706033 55.49808
## 149 410060 9.766749 55.49029
## 150 410050 9.815055 55.52288
## 151 173050 12.513737 55.77616
## 152 173040 12.521064 55.78557
## 153 410010 9.971608 55.47484
## 154 410040 9.844302 55.46100
## 155 530020 9.069821 55.73702
## 156 530010 9.029779 55.66068
## 157 530040 8.937649 55.83448
## 158 450040 10.716383 55.37537
## 159 420080 9.866582 55.28616
## 160 420070 9.963192 55.28679
## 161 420060 10.042873 55.37378
## 162 420050 10.133918 55.39303
## 163 420040 10.185800 55.35029
## 164 440050 10.577839 55.40717
## 165 440040 10.589907 55.35722
## 166 665050 8.230881 56.57390
## 167 665010 8.261851 56.40977
## 168 480020 10.363396 55.56007
## 169 480030 10.221570 55.50826
## 170 846120 10.145489 56.74124
## 171 480040 10.138621 55.55701
## 172 482030 10.714357 54.81776
## 173 482020 10.742612 54.91925
## 174 707090 10.316326 56.52613
## 175 540320 9.773168 55.03295
## 176 561130 8.436849 55.48387
## 177 561210 8.483360 55.54155
## 178 479220 10.646032 55.06792
## 179 450030 10.788340 55.33790
## 180 450020 10.802832 55.31074
## 181 450010 10.763479 55.30951
## 182 450070 10.684551 55.24005
## 183 706130 10.473929 56.34887
## 184 706120 10.585606 56.31124
## 185 706110 10.548113 56.39891
## 186 851620 9.963050 57.18002
## 187 561140 8.447194 55.46943
## 188 657120 8.948458 56.16793
## 189 671120 8.584323 56.48887
## 190 671110 8.557232 56.43828
## 191 671130 8.550719 56.52602
## 192 615140 9.765083 55.85424
## 193 615110 10.294479 55.75984
## 194 615170 9.874848 55.86726
## 195 615160 9.825397 55.87435
## 196 615150 9.828700 55.85893
## 197 746040 9.697410 56.08123
## 198 746020 9.865133 56.10301
## 199 746010 9.921992 56.17814
## 200 746090 9.906697 56.00809
## 201 813310 10.571551 57.72893
## 202 813330 10.427957 57.51139
## 203 671200 8.533818 56.64293
## 204 851120 9.674601 56.96082
## 205 851810 9.955345 57.08464
## 206 860350 9.944438 57.53285
## 207 860360 9.966895 57.57897
## 208 320050 11.945955 55.30941
## 209 350030 11.880925 55.59094
## 210 306140 11.447174 55.76364
## 211 265230 12.095575 55.63370
## 212 265220 12.101343 55.65593
## 213 330240 11.370430 55.32370
## 214 265110 12.156180 55.69881
## 215 336040 12.211917 55.36590
## 216 269030 12.160629 55.53865
## 217 860340 9.870714 57.47298
## 218 860330 9.720586 57.37225
## 219 860310 9.960222 57.37524
## 220 860130 10.227012 57.56491
## 221 860140 10.064738 57.54285
## 222 860110 10.149594 57.39133
## 223 810210 10.074490 57.27297
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## 225 810110 10.374249 57.15370
## 226 813120 10.495560 57.33996
## 227 813110 10.465003 57.25210
## 228 849120 9.696895 57.11927
## 229 849140 9.588633 57.23267
## 230 849210 9.532617 57.12476
## 231 849230 9.304522 57.11887
## 232 580160 9.362873 54.83445
## 233 580220 9.319764 55.06361
## 234 580110 9.422542 55.10691
## 235 480010 10.403906 55.49914
## 236 665070 8.194110 56.66772
## 237 665060 8.176022 56.60439
## 238 410020 9.881664 55.39708
## 239 779150 9.007459 56.57638
## 240 727030 10.111575 55.98177
## 241 727010 10.237449 55.96587
## 242 851610 10.070003 57.11416
## 243 540140 9.815235 54.93102
## 244 540310 9.904547 54.98548
## 245 550080 8.521870 55.13599
## 246 540230 9.604132 54.91191
## 247 540220 9.693425 54.87561
## 248 173030 12.513350 55.79320
## 249 173020 12.536293 55.78502
## 250 173010 12.568065 55.78997
## 251 173090 12.442104 55.79375
## 252 540210 9.742109 54.91681
## 253 580130 9.385596 55.03016
## 254 580120 9.397530 55.06143
## 255 849110 9.799218 57.17648
## 256 580140 9.502175 54.99360
## 257 580150 9.376880 54.89186
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## 259 550050 8.861791 54.93925
## 260 550060 8.747161 55.00518
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## 262 550020 9.019164 55.18871
## 263 550040 8.923534 54.95960
## 264 550010 9.107683 55.12797
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## 266 510050 9.485332 55.18777
## 267 510080 9.050857 55.27191
## 268 316270 11.502367 55.72025
## 269 316210 11.730478 55.56762
## 270 316250 11.487083 55.63773
## 271 340010 11.603814 55.42125
## 272 340020 11.533824 55.46265
## 273 330160 11.240841 55.43160
## 274 330120 11.325076 55.42114
## 275 330320 11.113965 55.34953
## 276 330230 11.282856 55.25500
## 277 330310 11.231525 55.35301
## 278 390240 12.040997 54.92742
## 279 390230 12.203652 54.93357
## 280 390220 12.275591 54.98408
## 281 390210 12.402729 54.99615
## 282 360120 11.517764 54.77573
## 283 326210 11.136119 55.66907
## 284 400330 14.700035 55.10134
## 285 400130 15.040123 55.03724
## 286 400110 15.115223 55.10883
## 287 400140 14.900999 55.07661
## 288 330220 11.379277 55.22939
## 289 316150 11.664540 55.70896
## 290 390140 11.878918 55.04368
## 291 390150 11.975302 54.99988
## 292 390160 11.901934 55.00511
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## 305 740130 9.570676 56.16131
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## 309 102740 12.502237 55.66127
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## 319 849240 9.140835 57.09045
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## 463 173080 12.478046 55.79545
## 464 630330 9.426438 55.70351
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## 907 851250 9.900322 56.93386 head(coord_df)## ID X Y
## 1 530030 8.871359 55.75216
## 2 480050 10.146158 55.46160
## 3 810120 10.268959 57.18203
## 4 563000 8.411613 55.40972
## 5 813130 10.326862 57.32651
## 6 510020 9.406825 55.30736 # Draw random points in polygons ..
library(sp)## Warning: package 'sp' was built under R version 4.0.5 out <- spsample(as_Spatial(ntm[1,]),3,type="random")## Warning in proj4string(obj): CRS object has comment, which is lost in output st_as_sf(out)## Simple feature collection with 3 features and 0 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: 8.808126 ymin: 55.6804 xmax: 8.890199 ymax: 55.79864
## Geodetic CRS: WGS 84 (with axis order normalized for visualization)
## geometry
## 1 POINT (8.810561 55.6804)
## 2 POINT (8.890199 55.68546)
## 3 POINT (8.808126 55.79864) st_sample(ntm[1:2,],10)## although coordinates are longitude/latitude, st_intersects assumes that they are planar## although coordinates are longitude/latitude, st_intersects assumes that they are planar
## although coordinates are longitude/latitude, st_intersects assumes that they are planar
## although coordinates are longitude/latitude, st_intersects assumes that they are planar## Geometry set for 10 features
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: 8.838631 ymin: 55.4735 xmax: 10.0957 ymax: 55.80943
## Geodetic CRS: WGS 84
## First 5 geometries:## POINT (8.878177 55.80344)## POINT (10.0496 55.4735)## POINT (8.942653 55.78745)## POINT (10.0957 55.50255)## POINT (8.864981 55.74528) # Sample 100 points in a zone and calculate transport time to a fixed zone
from_zone <- 102830
to_zone <- 102130 # RÃ¥dhuspladsen
z_sample <- st_sample(ntm[ntm$zoneid==from_zone,],100)## although coordinates are longitude/latitude, st_intersects assumes that they are planar## although coordinates are longitude/latitude, st_intersects assumes that they are planar
## although coordinates are longitude/latitude, st_intersects assumes that they are planar
## although coordinates are longitude/latitude, st_intersects assumes that they are planar
## although coordinates are longitude/latitude, st_intersects assumes that they are planar
## although coordinates are longitude/latitude, st_intersects assumes that they are planar origin <- st_coordinates(z_sample)
destination <- st_coordinates(centroids[centroids$id==102130,])
# Given origin-destination data how can I compute travel time?
library(r5r)## Warning: package 'r5r' was built under R version 4.0.5## Loading required namespace: rJava## Please make sure you have already allocated some memory to Java by running:
## options(java.parameters = '-Xmx2G').
## Currently, Java memory is set to -Xmx512m wd <- getwd()
data_path <- paste(wd,"//cph//",sep="")
r5r_core <- setup_r5(data_path = data_path, verbose = FALSE)## Using cached version from C:/Users/np83zg/OneDrive - Aalborg Universitet/Dokumenter/R/win-library/4.0/r5r/jar/r5-v6.2-all_20210408.jar##
## Using cached network.dat from C:/Users/np83zg/OneDrive - Aalborg Universitet/Skrivebord/cars-master/rspace_osm/cppRouting//cph///network.dat setwd(wd)
max_walk_dist <- 5000
max_trip_duration <- 120
departure_datetime <- as.POSIXct("13-05-2019 12:00:00",
format = "%d-%m-%Y %H:%M:%S")
origin_df <- data.frame(id=paste(102830,1:100,sep="_"),lat=origin[,2],lon=origin[,1])
destination_df <- data.frame(id=102130,lat=destination[,2],lon=destination[,1])
# calculate a travel time matrix
# calculate a travel time matrix
dit <- detailed_itineraries(r5r_core = r5r_core,
origins = origin_df,
destinations = destination_df,
mode = "CAR",
departure_datetime = departure_datetime,
max_walk_dist = max_walk_dist,
shortest_path = FALSE,
verbose = FALSE)## Warning in assert_points_input(destinations, "destinations"): 'destinations$id'
## forcefully cast to character.## Destinations dataframe expanded to match the number of origins. # extract OSM network
street_net <- street_network_to_sf(r5r_core)
# plot
library(ggplot2)## Warning: package 'ggplot2' was built under R version 4.0.5 ggplot() +
geom_sf(data = street_net$edges, color='gray85') +
geom_sf(data = dit, aes(color=mode)) 