edits to analysis
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@ -1,64 +1,3 @@
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labs(title = "Metro Route Speed",
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subtitle = paste0("averaged between ",
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length(unique(metro_data %>% filter(pid %in% c("422")) %>% pull(origtatripno))),
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" bus trips - ",
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min(date(metro_data$time)),
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" to ",
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max(date(metro_data$time))),
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x = NULL,
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y = NULL) +
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theme(axis.text=element_blank(),
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axis.ticks=element_blank(),
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plot.caption = element_text(color = "grey")) +
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geom_sf(data = segments_sf %>% filter(pid %in% c("422")),
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inherit.aes = FALSE,
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aes(color = lag_spd),
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linewidth = 1) +
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scale_color_distiller(palette = "RdYlGn", direction = "reverse", limits = c(0,70), name = "Average speed\n(calculated with consecutive points)") +
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facet_wrap(paste0(rt, "-", des) ~ .)
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View(metro_summary)
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metro_summary <- metro_data %>%
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mutate(pdist_bucket = round(pdist / 500) * 500) %>%
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group_by(pdist_bucket, rt, des, pid) %>%
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summarise(lat = median(lat),
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lon = median(lon),
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spd = median(spd),
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lag_spd = median(lag_spd),
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trip_count = n())
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metro_summary <- metro_data %>%
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mutate(pdist_bucket = round(pdist / 500) * 500) %>%
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group_by(pdist_bucket, rt, des, pid, origtatripno) %>%
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summarise(lat = median(lat),
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lon = median(lon),
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spd = median(spd),
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lag_spd = median(lag_spd),
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trip_count = n())
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trip_count = length(unique(origtatripno))
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metro_summary <- metro_data %>%
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mutate(pdist_bucket = round(pdist / 500) * 500) %>%
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group_by(pdist_bucket, rt, des, pid) %>%
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summarise(lat = median(lat),
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lon = median(lon),
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spd = median(spd),
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lag_spd = median(lag_spd),
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trip_count = length(unique(origtatripno)))
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ggmap(basemap) +
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labs(title = "Metro Route Speed",
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subtitle = paste0("averaged between ",
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segments_sf %>% filter(pid %in% c("422")) %>% pull(trip_count))),
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ggmap(basemap) +
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labs(title = "Metro Route Speed",
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subtitle = paste0("averaged between ",
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segments_sf %>% filter(pid %in% c("422")) %>% pull(trip_count),
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" bus trips - ",
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min(date(metro_data$time)),
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" to ",
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max(date(metro_data$time))),
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x = NULL,
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y = NULL) +
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theme(axis.text=element_blank(),
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axis.ticks=element_blank(),
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plot.caption = element_text(color = "grey")) +
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geom_sf(data = segments_sf %>% filter(pid %in% c("422")),
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inherit.aes = FALSE,
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aes(color = lag_spd),
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@ -510,3 +449,64 @@ aes(color = lag_spd),
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linewidth = 1) +
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scale_color_distiller(palette = "RdYlGn", direction = "reverse", limits = c(0,70), name = "Average speed\n(calculated with consecutive points)") +
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facet_wrap(paste0(rt, "-", des) ~ .)
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library(tidyverse)
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library(influxdbclient)
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library(glue)
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library(ggmap)
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library(sf)
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# parameters needed to make connection to Database
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token <- substr(read_file(file = 'api_keys/influxdb_madison-metro'), 1, 88)
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org <- "e2581d54779b077f"
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bucket <- "madison-metro"
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days <- 1
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influx_connection <- InfluxDBClient$new(url = "https://influxdb.dendroalsia.net",
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token = token,
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org = org)
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#---
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# Fields you want to query
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fields <- c("spd", "pdist", "pid", "lon", "lat", "vid", "dly", "origtatripno")
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# Creating an empty list to store results for each field
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results <- vector("list", length(fields))
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# Loop through each field, get data, and coerce types if needed
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for (i in seq_along(fields)) {
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field <- fields[i]
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query_string <- glue('from(bucket: "{bucket}") ',
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'|> range(start: -{days}d) ',
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'|> filter(fn: (r) => r["_measurement"] == "vehicle_data")',
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'|> filter(fn: (r) => r["_field"] == "{field}")')
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data <- influx_connection$query(query_string)
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# Ensure the columns are coerced to consistent types
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# (Optionally add coercion based on your expected types)
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data <- bind_rows(data) %>%
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mutate(value = as.character(`_value`),
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field = `_field`) %>%
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select(time, rt, des, value, field)
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results[[i]] <- data
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}
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# Bind all results together
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metro_raw <- bind_rows(results)
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metro_raw <- pivot_wider(metro_raw, values_from = value, names_from = field) %>%
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distinct(pid, vid, lat, lon, spd, .keep_all = TRUE)
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metro_data <- metro_raw %>%
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mutate(time = with_tz(time, "America/Chicago"),
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spd = as.double(spd),
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pdist = as.double(pdist),
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lon = as.double(lon),
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lat = as.double(lat)) %>%
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group_by(origtatripno) %>%
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arrange(time) %>%
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mutate(lag_pdist = lag(pdist),
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lag_time = lag(time)) %>%
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mutate(lag_spd = (pdist - lag_pdist)/as.double(difftime(time, lag_time, units = "hours"))/5280)
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routes_categorized <- read_csv(file = "routes_categorized.csv", col_types = "cc")
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bucket_lat <- 364481.35/200
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bucket_lon <- 267203.05/200
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metro_summary <- metro_data %>%
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left_join(routes_categorized, by = "pid") %>%
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mutate(lat_bucket = round(lat / 200) * 100) %>%
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group_by(pdist_bucket, rt, des, pid) %>%
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summarise(lat = median(lat, na.rm = TRUE),
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lon = median(lon, na.rm = TRUE),
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spd = median(spd, na.rm = TRUE),
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lag_spd = median(lag_spd, na.rm = TRUE),
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trip_count = length(unique(origtatripno)))
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@ -16,7 +16,7 @@ influx_connection <- InfluxDBClient$new(url = "https://influxdb.dendroalsia.net"
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org = org)
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#---
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# Fields you want to query
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fields <- c("spd", "pdist", "pid", "lon", "lat", "vid", "dly", "origtatripno")
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fields <- c("des", "spd", "pdist", "lon", "lat", "dly", "origtatripno")
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# Creating an empty list to store results for each field
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results <- vector("list", length(fields))
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@ -37,7 +37,7 @@ for (i in seq_along(fields)) {
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data <- bind_rows(data) %>%
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mutate(value = as.character(`_value`),
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field = `_field`) %>%
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select(time, rt, des, value, field)
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select(time, rt, pid, vid, value, field)
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results[[i]] <- data
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}
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@ -54,33 +54,37 @@ metro_data <- metro_raw %>%
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pdist = as.double(pdist),
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lon = as.double(lon),
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lat = as.double(lat)) %>%
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group_by(origtatripno) %>%
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group_by(pid, vid) %>%
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arrange(time) %>%
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mutate(lag_pdist = lag(pdist),
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lag_time = lag(time)) %>%
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mutate(lag_spd = (pdist - lag_pdist)/as.double(difftime(time, lag_time, units = "hours"))/5280)
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mutate(spd_calc = (pdist - lag_pdist)/as.double(difftime(time, lag_time, units = "hours"))/5280)
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routes_categorized <- read_csv(file = "routes_categorized.csv", col_types = "cc")
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bucket_lat <- 364481.35/200
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bucket_lon <- 267203.05/200
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bucket_feet <- 200
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lat_round <- bucket_feet/364481.35
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lon_round <- bucket_feet/267203.05
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metro_summary <- metro_data %>%
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left_join(routes_categorized, by = "pid") %>%
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mutate(lat_bucket = round(lat / 200) * 100) %>%
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group_by(pdist_bucket, rt, des, pid) %>%
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summarise(lat = median(lat, na.rm = TRUE),
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lon = median(lon, na.rm = TRUE),
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mutate(lat_bucket = round(lat / lat_round) * lat_round,
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lon_bucket = round(lon / lon_round) * lon_round) %>%
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group_by(lat_bucket, lon_bucket, rt, des, pid) %>%
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summarise(lat_bucket = median(lat_bucket, na.rm = TRUE),
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lon_bucket = median(lon_bucket, na.rm = TRUE),
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spd = median(spd, na.rm = TRUE),
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lag_spd = median(lag_spd, na.rm = TRUE),
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spd_calc = median(spd_calc, na.rm = TRUE),
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pdist = median(pdist),
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trip_count = length(unique(origtatripno)))
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metro_data_sf <- st_as_sf(metro_data %>% filter(!is.na(lon)), coords = c("lon", "lat"), remove = FALSE)
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metro_summary_sf <- st_as_sf(metro_summary %>% filter(!is.na(lon)), coords = c("lon", "lat"), remove = FALSE)
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metro_summary_sf <- st_as_sf(metro_summary %>% filter(!is.na(lon_bucket)), coords = c("lon_bucket", "lat_bucket"), remove = FALSE)
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segments_sf <- metro_summary_sf %>%
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group_by(rt, pid, des) %>%
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arrange(pid, pdist_bucket) %>% # Ensure points within each route are sorted if needed
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group_by(rt, pid) %>%
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arrange(pid, pdist) %>% # Ensure points within each route are sorted if needed
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mutate(
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lead_geom = lead(geometry),
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lead_spd = lead(spd)
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@ -93,7 +97,7 @@ segments_sf <- metro_summary_sf %>%
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) %>%
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ungroup() %>%
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as.data.frame() %>%
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select(rt, pid, des, pdist_bucket, spd, segment, lag_spd) %>%
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select(rt, pid, des, lat_bucket, lon_bucket, spd, segment, spd_calc) %>%
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st_as_sf()
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# get counts of routes
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@ -101,8 +105,8 @@ route_counts <- metro_data %>% group_by(pid, rt, des) %>% summarise(route_count
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# make charts
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ggplot(data = metro_summary %>% filter(pid %in% c("421", "422")),
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aes(x = pdist_bucket,
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y = lag_spd)) +
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aes(x = pdist,
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y = spd_calc)) +
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geom_point() +
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geom_smooth() +
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facet_grid(paste0(rt, "-", des) ~ .)
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@ -118,7 +122,7 @@ bbox <- c(left = min(metro_data$lon),
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basemap <- get_stadiamap(bbox = bbox, zoom = 13, maptype = "stamen_toner_lite")
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# A West
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quantile(segments_sf %>% filter(pid %in% c("469")) %>% pull(lag_spd), c(0,0.25, 0.5, 0.75, 1))
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quantile(segments_sf %>% filter(pid %in% c("469")) %>% pull(spd_calc), c(0,0.25, 0.5, 0.75, 1), na.rm = TRUE)
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for (route in unique(routes_categorized$name)){
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route_focus <- routes_categorized %>% filter(name == route) %>% pull(pid)
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@ -137,7 +141,7 @@ for (route in unique(routes_categorized$name)){
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plot.caption = element_text(color = "grey")) +
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geom_sf(data = segments_sf %>% filter(pid %in% route_focus),
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inherit.aes = FALSE,
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aes(color = lag_spd),
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aes(color = spd_calc),
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linewidth = 1) +
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scale_color_distiller(palette = "RdYlGn", direction = "reverse", limits = c(0,70), name = "Average speed or segment\n(calculated with locations, not reported speed)")
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ggsave(file = paste0("figures/",
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