CODEWITHSUNDEEP
rggplot2gtfstransitr5r
Audit_power28
Audit_power28

Reputation: 11

Using r5r in RStudio to Plot Transit : Help Troubleshooting Transit times & Plotting Appropriate Map

I'm working on plotting access to a single point in Montreal-- the central station-- and the process seems to go sideways at the point where I create the travel time matrix (ttm). My hex_grid_points has 1123 and then gets reduced to 934 for a reason I cannot explain. When I try to graph the result over a map of Montreal (the last block of ggplot code below), the travel times do not seem to correspond with the distance from the central station (which also does not graph as a red point as expected with that code). I've tried to figure out what is going wrong, the only thing I found was increasing max_trip_duration to something ridiculous like 300 resulted in something like 1050 points in the ttm (vs the 934 before, and the 1123 I would expect). Still, this does not account for why the travel time vary vastly from cell to cell--a 100min transit time hexagon will be right next to a 10min transit time hexagon, even though they're sized to be 1km, and that wouldn't take an individual more than 10-15 minutes to walk to that other hexagon.

I tried following the tutorial here:https://citygeographics.org/r5r-workshop/accessibility-example-travel-time-matrix/ and have compared my own produced map as a check on consistency, but the travel times are vastly different than my own map. Yes, the dates of investigation are different, but not wildly so, and I'd expect something close to the same. I have included links to data I used in the analysis, I didn't manage to pull in the data with URLs, but the links point directly to the data.

library(pacman)
pacman::p_load(dplyr, ggplot2, sf, terra, here, readr, stargazer, giscoR, tidyverse,r5r,osmextract,osmdata,skimr,data.table,geosphere, geojsonsf, hexbin, viridis)

options(java.parameters = "-Xmx5G")

#Creating Single Polygon to represent bounds of sample area
montreal_divisions <-st_read(file.path(".","Montreal Data_transitfeeds","Montreal-limites-administratives-agglomeration-WGS84.geojson"))
montreal_boundary <- st_union(montreal_divisions)
montrealbbox <- st_bbox(montreal_boundary)

#Creation of Hexgrid
#CRS = 4326 , converting degrees to meters in order to set size 
desired_distance <- 1000 #meter To be changed subject to resolution desires
meters_to_degrees <- 1/111000 #1 degree to 111km or 111,000 meters
hex_size <- desired_distance*meters_to_degrees

hex_points <- st_make_grid(
  montreal_boundary, 
  cellsize = c(hex_size, hex_size),
  square = FALSE
  )

sf_use_s2(FALSE)
hex_grid <- st_intersection(st_as_sf(hex_points), montreal_boundary)
#Add ID column
hex_grid$id <-seq_len(nrow(hex_grid))
#Set ID column as first column
hex_grid <- hex_grid[, c("id", setdiff(names(hex_grid), "id"))]

write.csv(hex_grid, "montreal_hexgrid_og.csv", row.names = FALSE)

#Convert hex_grid to dataframe of centroids
hex_grid_centroids <- st_centroid(hex_grid)
hex_grid_df <- st_coordinates(hex_grid_centroids) %>%
  as.data.frame() %>%
  rename(lon=X, lat=Y)
write.csv(hex_grid_df, "montreal_hexgrid_df.csv", row.names = FALSE)

##Hard coding coordinates of destination = Central Station Montreal (source = google)
point_data <-data.frame(lon = -73.5665, lat = 45.5001 )
station_central_sf <- st_as_sf(point_data,coords=c("lon", "lat"))
station_central_sf <- st_set_crs(station_central_sf,"+proj=longlat +datum=WGS84")

station_central_sf$lat <-st_coordinates(station_central_sf)[,"Y"]
station_central_sf$lon <-st_coordinates(station_central_sf)[,"X"]
station_central_sf$id <- seq_len(nrow(station_central_sf))

#Build transport network
data_path <- file.path("C:")  #Containing file with GTFS.zip and osm.pbf files
r5r_core <- setup_r5(data_path = data_path)


#Travel Time Matrix

points <- st_read(file.path(data_path,"montreal_hexgrid_df.csv")) 
points$id <- seq_len(nrow(points))
#convert chr to int
points$lon <- as.numeric(points$lon)
points$lat <- as.numeric(points$lat)


mode<- c("WALK", "TRANSIT") 
max_walk_time <- 30 #in minutes
max_trip_duration <- 120 #in minutes
departure_datetime <- as.POSIXct("28-11-2023 08:00:00", 
                                 format = "%d-%m-%Y %H:%M:%S")

#Calculating travel time matrix
ttm <- travel_time_matrix(r5r_core = r5r_core,
                          origins = points,
                          destinations = station_central_sf,
                          mode = mode,
                          departure_datetime = departure_datetime,
                          max_walk_time = max_walk_time,
                          max_trip_duration = max_trip_duration,
                          progress = TRUE,
                          time_window = 60)

write.csv(ttm,file.path(data_path,"CentralStationDestTTM.csv"),row.names=FALSE)


#Producing a couple of graphs for troubleshooting
ggplot(ttm, aes(x = from_id, y = to_id, fill = travel_time_p50)) +
  geom_tile() +
  scale_fill_gradient(low = "lightblue", high = "darkblue") +
  labs(x = "Origin", y = "Destination", title = "Travel Time Matrix") +
  theme_minimal()

##Convert ttm to a data frame
ttm_df <- as.data.frame(ttm)

##Plot travel times as a bar graph
ggplot(ttm_df, aes(x = from_id, y = travel_time_p50)) +
  geom_bar(stat = "identity", fill = "skyblue") +
  labs(x = "Origin", y = "Travel Time (minutes)", title = "Travel Time to Central Station") +
  theme_minimal()

#Finally attempting to Graph the matrix values

points <- points[, c("id", setdiff(names(points), "id"))] ##Reorder the columns with 'id' as the first column
ttm <- ttm %>% mutate(from_id=row_number())
points <- points %>% mutate (id=row_number())
ttm_joined <- left_join(ttm,points,by=c("from_id" = "id"))

joined_ttm_sf <- st_as_sf(ttm_joined,coords=c("lon","lat"),crs=4326) #Convert to sf for mapping

#Join data with hex_grid 
joined_hex_grid <- left_join(hex_grid,ttm_joined,by = c("id" = "from_id"))
                    

breaks <- seq(0, max(joined_hex_grid$travel_time_p50, na.rm = TRUE), by = 10) #Define breaks for the color scale

ggplot() +
  geom_sf(data = station_central_sf, color = "red", size = 3) +    # Central station
  geom_sf(data = joined_hex_grid, aes(fill = travel_time_p50)) +     # Travel time matrix
  scale_fill_gradient(low = "green", high = "red",breaks=breaks) +     # Color scale
  labs(title = "Travel Time Matrix to Central Station", fill = "Travel Time (minutes)") +
  theme_minimal()

I used the following sources of GTFS data:

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