reading GTFS data

[majki00]

Hi, I am working on creating accessibility index for Warsaw. I have successfully dowloaded the package and it works with the data provided in your example. However, when I am using GTFS data for Warsaw (https://transitfeeds.com/p/ztm-warszawa/720/20200204) travel_time_matrix returns only WALK time as if GTFS data was not read properly (the time is identical no matter whether mode is WALK or WALK and TRANSIT). I wonder whether there is anything missing in the provided GTFS data. The data is saved as warsaw.gtfs.zip. I would be grateful for your response.

Network data: http://download.geofabrik.de/europe/poland/mazowieckie.html POIS: http://slipo.eu/?p=1551

Code:

#path to dir with all the files
path<-("/Users/michal/Desktop/rstudio_diss/data_acc")
list.files(path)
#sample of points
cin.p <- cinema[ c(sample(1:nrow(cinema), 10, replace=TRUE)), ]
hos.p<-hospital[ c(sample(1:nrow(hospital), 10, replace=TRUE)), ]

r5r_core <- setup_r5(data_path = path, verbose = FALSE)
mode<-c("WALK", "TRANSIT")
max_walk_dist<-3000
max_trip_duration<-60
departure_datetime<-as.POSIXct("04-02-2020 07:30:00", format="%d-%m-%Y %H:%M:%S")
h <- travel_time_matrix(r5r_core = r5r_core,
                          origins = cin.p,
                          destinations = hos.p,
                          mode = mode,
                          departure_datetime = departure_datetime,
                          max_walk_dist = max_walk_dist,
                          max_trip_duration = max_trip_duration,
                          verbose = FALSE)
head(h)

dit<-detailed_itineraries(r5r_core = r5r_core,
                          origins = cin.p,
                          destinations = hos.p,
                          mode = mode,
                          departure_datetime = departure_datetime,
                          max_walk_dist = max_walk_dist,
                          max_trip_duration = max_trip_duration,
                          verbose = FALSE)

[mvpsaraiva]

Hi @majki00. The problem you mentioned usually happens when the date informed in departure_datetime is outside the GTFS range, but that doesn't seem to be the case here.

I've tested your code and got results that seem to be ok.

Results with WALK and TRANSIT:

fromId	toId	travel_time
node/7061367621	node/6627386045	56
way/243923693	way/29024766	30
way/243923693	node/6627386045	34
way/243923693	node/6685009464	31
node/678963604	way/29024766	28
node/678963604	node/1724515583	48
node/678963604	node/6627386045	41
node/678963604	node/6685009464	40
node/3740488836	way/29024766	30
node/3740488836	node/6627386045	55
node/5965555600	way/29024766	56
node/5965555600	node/6627386045	53
node/5965555600	node/6685009464	51
node/6835804279	way/29024766	29
node/6835804279	node/6685009464	47
node/5106026256	way/29024766	49
node/5106026256	node/6627386045	43
node/5106026256	node/6685009464	44

Results with WALK only:

fromId	toId	travel_time
way/243923693	node/6685009464	46
node/678963604	way/29024766	53

Maybe the problem is in the code that's missing from your example, like the part you load and treat the POIs.

[mvpsaraiva]

Just for reference, this is the full code I've used. I've saved all files you provided links in the data folder inside an RStudio project

# allocate RAM memory to Java
options(java.parameters = "-Xmx16G")

library(r5r)
library(data.table)

#path to dir with all the files
path<-("data")
list.files(path)

poi <- fread("data/mazowieckie-pois.osm.csv")
cinema <- poi[SUBCATEGORY == "CINEMA"]
hospital <- poi[SUBCATEGORY == "HOSPITAL"]

#sample of points
cin.p <- cinema[ c(sample(1:nrow(cinema), 10, replace=TRUE)), ]
hos.p<-hospital[ c(sample(1:nrow(hospital), 10, replace=TRUE)), ]

cin.p <- janitor::clean_names(cin.p)
hos.p <- janitor::clean_names(hos.p)

r5r_core <- setup_r5(data_path = path, verbose = FALSE)
mode<-c("WALK", "TRANSIT")
# mode<-c("WALK")
max_walk_dist<-3000
max_trip_duration<-60
departure_datetime<-as.POSIXct("04-02-2020 07:30:00", format="%d-%m-%Y %H:%M:%S")
h <- travel_time_matrix(r5r_core = r5r_core,
                        origins = cin.p,
                        destinations = hos.p,
                        mode = mode,
                        departure_datetime = departure_datetime,
                        max_walk_dist = max_walk_dist,
                        max_trip_duration = max_trip_duration,
                        verbose = FALSE)
head(h)

[majki00]

Thank you for quick response @mvpsaraiva and apologies for bothering you once again, but I still am not able to resolve the issue. I used exact same code as you and tried various options of treating the POIs but the results are still not correct. In my directory 'data_acc' I have gtfs.zip, mazowieckie_osm.pbf and mazowsze_osm_clean.csv (same as mazowieckie-pois.osm.csv but clipped for Warsaw). Furthermore, I noticed that WALK and WALK or TRANSIT results are actually not identical and there are more results for WALK only, which is even stranger (shown below). I feel stuck and do not really know where should I search for the issue. I would be grateful for your suggestions.

Results for WALK

fromId	toId	travel_time
node/2096125794	19509	52
node/248212391	17922	58
node/248212391	15330	46
node/588807906	19509	44
node/588807906	15330	58
node/2934124990	17922	58
node/2934124990	15330	56
node/2943366220	21768	31
node/2943366220	15330	44

[rafapereirabr]

Hi @majki00 , could you please run sessionInfo() on Rstudio and share the output in a comment below?

[majki00]

Hi @rafapereirabr , thank you for the response. Here is my sessionInfo:

sessionInfo()
R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.3

Matrix products: default
BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] tmap_3.3-1        forcats_0.5.0     stringr_1.4.0     dplyr_1.0.4       purrr_0.3.4       readr_1.4.0      
 [7] tidyr_1.1.2       tibble_3.1.0      tidyverse_1.3.0   mapview_2.10.0    ggplot2_3.3.2     data.table_1.14.0
[13] r5r_0.6.0         nngeo_0.4.4       sf_0.9-8          leaflet_2.0.4.1   raster_3.4-5      rgdal_1.5-23     
[19] h3_3.7.1          rJava_1.0-4       sp_1.4-5         

loaded via a namespace (and not attached):
 [1] fs_1.5.0                satellite_1.0.2         lubridate_1.7.9.2       webshot_0.5.2           RColorBrewer_1.1-2     
 [6] httr_1.4.2              tools_4.0.3             backports_1.2.0         utf8_1.1.4              R6_2.5.0               
[11] KernSmooth_2.23-17      DBI_1.1.1               colorspace_1.4-1        withr_2.3.0             tidyselect_1.1.0       
[16] curl_4.3                compiler_4.0.3          leafem_0.1.3            cli_2.1.0               rvest_0.3.6            
[21] xml2_1.3.2              checkmate_2.0.0         scales_1.1.1            classInt_0.4-3          proxy_0.4-25           
[26] nabor_0.5.0             digest_0.6.27           dichromat_2.0-0         base64enc_0.1-3         pkgconfig_2.0.3        
[31] htmltools_0.5.1.1       dbplyr_2.1.0            htmlwidgets_1.5.2       rlang_0.4.10            readxl_1.3.1           
[36] rstudioapi_0.11         generics_0.1.0          jsonlite_1.7.1          crosstalk_1.1.0.1       magrittr_2.0.1         
[41] jdx_0.1.4               Rcpp_1.0.6              munsell_0.5.0           fansi_0.4.1             abind_1.4-5            
[46] lifecycle_0.2.0         stringi_1.5.3           leafsync_0.1.0          yaml_2.2.1              snakecase_0.11.0       
[51] tmaptools_3.1           grid_4.0.3              parallel_4.0.3          crayon_1.3.4            lattice_0.20-41        
[56] haven_2.3.1             stars_0.5-2             hms_0.5.3               pillar_1.5.1            codetools_0.2-16       
[61] stats4_4.0.3            XML_3.99-0.6            reprex_1.0.0            glue_1.4.2              leaflet.providers_1.9.0
[66] modelr_0.1.8            png_0.1-7               vctrs_0.3.6             cellranger_1.1.0        gtable_0.3.0           
[71] assertthat_0.2.1        janitor_2.1.0           lwgeom_0.2-5            broom_0.7.4             e1071_1.7-6            
[76] viridisLite_0.3.0       class_7.3-17            sfheaders_0.4.0         units_0.7-1             ellipsis_0.3.1   

[mvpsaraiva]

Hi @majki00.

Perhaps your issue is the same as #200. Can you try plotting your origin/destination points over your transit network to see if they fit?

Also, what are your destination points? I can't find those id's in the toId column of the results you posted in the POIs csv. Maybe there's a problem with them?

[majki00]

Hi @mvpsaraiva @rafapereirabr,

it seems that it finally works!! thank you very much for your help. It turned out the the issue was OpenMP support for data.table package (it was operating in a single-threaded mode). After following the instructions from this GitHub thread and installing data.table once again, r5r seems to work correctly. Thank you for your help once again.

[rafapereirabr]

We're glad to hear it's working . closing the issue for now.