madison-metro/madison-metro.R

library(tidyverse)
library(influxdbclient)
library(glue)
library(ggmap)
library(sf)

# parameters needed to make connection to Database
token <- substr(read_file(file = 'api_keys/influxdb_madison-metro'), 1, 88)
org <- "e2581d54779b077f"
bucket <- "madison-metro"

days <- 1

influx_connection <- InfluxDBClient$new(url = "https://influxdb.dendroalsia.net",
                                token = token,
                                org = org)
#---
# Fields you want to query
fields <- c("des", "spd", "pdist", "lon", "lat", "dly", "origtatripno")

# Creating an empty list to store results for each field
results <- vector("list", length(fields))

# Loop through each field, get data, and coerce types if needed
for (i in seq_along(fields)) {
  field <- fields[i]
  
  query_string <- glue('from(bucket: "{bucket}") ',
                       '|> range(start: -{days}d) ',
                       '|> filter(fn: (r) => r["_measurement"] == "vehicle_data")',
                       '|> filter(fn: (r) => r["_field"] == "{field}")')
  
  data <- influx_connection$query(query_string)
  
  # Ensure the columns are coerced to consistent types 
  # (Optionally add coercion based on your expected types)
  data <- bind_rows(data) %>%
    mutate(value = as.character(`_value`),
           field = `_field`) %>% 
    select(time, rt, pid, vid, value, field)
  
  results[[i]] <- data
}

# Bind all results together
metro_raw <- bind_rows(results)

metro_raw <- pivot_wider(metro_raw, values_from = value, names_from = field) %>%
  distinct(pid, vid, lat, lon, spd, .keep_all = TRUE)

metro_data <- metro_raw %>%
  mutate(time = with_tz(time, "America/Chicago"),
         spd = as.double(spd),
         pdist = as.double(pdist),
         lon = as.double(lon),
         lat = as.double(lat)) %>%
  group_by(pid, vid) %>%
  arrange(time) %>%
  mutate(lag_pdist = lag(pdist),
         lag_time = lag(time)) %>%
  mutate(spd_calc = (pdist - lag_pdist)/as.double(difftime(time, lag_time, units = "hours"))/5280)

routes_categorized <- read_csv(file = "routes_categorized.csv", col_types = "cc")

bucket_feet <- 200

lat_round <- bucket_feet/364481.35
lon_round <- bucket_feet/267203.05

metro_summary <- metro_data %>%
  left_join(routes_categorized, by = "pid") %>%
  mutate(lat_bucket = round(lat / lat_round) * lat_round,
         lon_bucket = round(lon / lon_round) * lon_round) %>%
  group_by(lat_bucket, lon_bucket, rt, des, pid) %>%
  summarise(lat_bucket = median(lat_bucket, na.rm = TRUE),
            lon_bucket = median(lon_bucket, na.rm = TRUE),
            spd = median(spd, na.rm = TRUE),
            spd_calc = median(spd_calc, na.rm = TRUE),
            pdist = median(pdist),
            trip_count = length(unique(origtatripno)))
  
metro_data_sf <- st_as_sf(metro_data %>% filter(!is.na(lon)), coords = c("lon", "lat"), remove = FALSE)
metro_summary_sf <- st_as_sf(metro_summary %>% filter(!is.na(lon_bucket)), coords = c("lon_bucket", "lat_bucket"), remove = FALSE)

segments_sf <- metro_summary_sf %>%
  group_by(rt, pid) %>%
  arrange(pid, pdist) %>%  # Ensure points within each route are sorted if needed
  mutate(
    lead_geom = lead(geometry),
    lead_spd = lead(spd)
  ) %>%
  filter(!is.na(lead_geom)) %>%
  # Create a segment for each pair of points
  rowwise() %>%
  mutate(
    segment = st_cast(st_union(geometry, lead_geom), "LINESTRING")
  ) %>%
  ungroup() %>%
  as.data.frame() %>%
  select(rt, pid, des, lat_bucket, lon_bucket, spd, segment, spd_calc) %>%
  st_as_sf()

# get counts of routes
route_counts <- metro_data %>% group_by(pid, rt, des) %>% summarise(route_count = length(unique(origtatripno)))

# make charts
ggplot(data = metro_summary %>% filter(pid %in% c("421", "422")),
       aes(x = pdist,
           y = spd_calc)) +
  geom_point() +
  geom_smooth() +
  facet_grid(paste0(rt, "-", des) ~ .)

register_stadiamaps(key = substr(read_file(file = "api_keys/stadia_api_key"), 1, 36))

bbox <- c(left = min(metro_data$lon),
          bottom = min(metro_data$lat),
          right = max(metro_data$lon),
          top = max(metro_data$lat))

#get basemap
basemap <- get_stadiamap(bbox = bbox, zoom = 13, maptype = "stamen_toner_lite")

# A West
quantile(segments_sf %>% filter(pid %in% c("469")) %>% pull(spd_calc), c(0,0.25, 0.5, 0.75, 1), na.rm = TRUE)

for (route in unique(routes_categorized$name)){
  route_focus <- routes_categorized %>% filter(name == route) %>% pull(pid)
  ggmap(basemap) +
    labs(title = paste0("Metro Route Speed - ", route),
         subtitle = paste0("averaged between ",
                           sum(route_counts %>% filter(pid %in% route_focus) %>% pull(route_count)),
                           " bus trips - ",
                           min(date(metro_data$time)),
                           " to ",
                           max(date(metro_data$time))),
         x = NULL,
         y = NULL) +
    theme(axis.text=element_blank(),
          axis.ticks=element_blank(),
          plot.caption = element_text(color = "grey")) +
    geom_sf(data = segments_sf %>% filter(pid %in% route_focus),
            inherit.aes = FALSE,
            aes(color = spd_calc),
            linewidth = 1) +
    scale_color_distiller(palette = "RdYlGn", direction = "reverse", limits = c(0,70), name = "Average speed or segment\n(calculated with locations, not reported speed)")
  ggsave(file = paste0("figures/",
                       route,
                       ".pdf"),
         title = paste0("Metro Route Speed - ", route),
         device = pdf,
         height = 8.5,
         width = 11,
         units = "in",
         create.dir = TRUE)
}