library(knitr) library(tidytransit) library(dplyr) library(lubridate) library(ggplot2) knitr::opts_chunk$set(echo = TRUE)
Each trip in a GTFS feed is referenced to a service_id (in trips.txt). The GTFS reference specifies that a "service_id contains an ID that uniquely identifies a set of dates when service is available for one or more routes". A service could run on every weekday or only on Saturdays for example. Other possible services run only on holidays during a year, independent of weekdays. However, feeds are not required to indicate anything with service_ids and some feeds even use a unique service_id for each trip and day. In this vignette, we'll look at a general way to gather information on when trips run by using "service patterns".
Service patterns can be used to find a typical day for further analysis like routing or trip frequencies for different days.
We use a feed from the New York Metropolitan Transportation Authority. It is provided as a sample feed with tidytransit but you can read it directly from the MTA's website.
local_gtfs_path <- system.file("extdata", "google_transit_nyc_subway.zip", package = "tidytransit") gtfs <- read_gtfs(local_gtfs_path) # gtfs <- read_gtfs("http://web.mta.info/developers/data/nyct/subway/google_transit.zip")
Tidytransit provides a dates_services
(stored in the list .
) that indicates which
service_id
runs on which date. This is later useful for linking dates and trips via
service_id
.
head(gtfs$.$dates_services)
To understand service patterns better we need information on weekdays and holidays. With a calendar table we know the weekday and possible holidays for each date. We'll use a minimal example with two holidays.
holidays = tribble(~date, ~holiday, ymd("2018-07-04"), "Independence Day", ymd("2018-09-03"), "Labor Day") calendar = tibble(date = unique(gtfs$.$dates_services$date)) %>% mutate( weekday = (function(date) { c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday")[as.POSIXlt(date)$wday + 1] })(date) ) calendar <- calendar %>% left_join(holidays, by = "date") head(calendar)
To analyse on which dates trips run and to group similar services we use service patterns.
Such a pattern simply lists all dates a trip runs on. For example, a trip with a pattern
like 2019-03-07, 2019-03-14, 2019-03-21, 2019-03-28 runs every Thursday in March 2019.
To handle these patterns, we create a servicepattern_id
using a hash function. Ideally
there are the same number of servicepattern_ids and service_ids. However, in real life feeds
this is rarely the case. In addition, the usability of service patterns depends largely on
the feed and its complexity.
gtfs <- set_servicepattern(gtfs)
Our gtfs feed now contains the data frame servicepatterns
which links each servicepattern_id
to an existing service_id
(and by extension trip_id
).
head(gtfs$.$servicepatterns)
In addition, gtfs$.$dates_servicepatterns
has been created which connects dates and service
patterns (like dates_services
). We can compare the number of service patterns to the number of services.
head(gtfs$.$dates_servicepatterns) # service ids used n_services <- length(unique(gtfs$trips$service_id)) # 70 # unique date patterns n_servicepatterns <- length(unique(gtfs$.$servicepatterns$servicepattern_id)) # 7
The feed uses 70 service_ids but there are actually only 7 different date patterns. Other feeds might not have such low numbers, for example the Swiss GTFS feed uses around 15'600 service_ids which all identify unique date patterns.
We'll now try to figure out usable names for those patterns. A good way to start is visualising the data.
date_servicepattern_table <- gtfs$.$dates_servicepatterns %>% left_join(calendar, by = "date") ggplot(date_servicepattern_table) + theme_bw() + geom_point(aes(x = date, y = servicepattern_id, color = weekday), size = 1) + scale_x_date(breaks = scales::date_breaks("1 month")) + theme(legend.position = "bottom")
The plot shows that pattern s_128de43
runs on every Sunday from July until October without
exceptions. s_a4c6b26
also runs on Sundays but it also covers a Monday (September 3rd).
Similarly, the date pattern s_f3bcc6f
runs every Saturday. s_d7d9701
covers weekdays
(Mondays through Friday), s_e25d6ca
seems to do the same through November with some exceptions.
It's generally difficult to automatically generate readable names for service patterns. Below you see a semi automated approach with some heuristics. However, the workflow depends largely on the feed and its structure. You might also consider setting names completely manually.
suggest_servicepattern_name = function(dates, calendar) { servicepattern_calendar = tibble(date = dates) %>% left_join(calendar, by = "date") # all normal dates without holidays calendar_normal = servicepattern_calendar %>% filter(is.na(holiday)) # create a frequency table for all calendar dates without holidays weekday_freq = sort(table(calendar_normal$weekday), decreasing = T) n_weekdays = length(weekday_freq) # all holidays that are not covered by normal weekdays anyways calendar_holidays <- servicepattern_calendar %>% filter(!is.na(holiday)) %>% filter(!(weekday %in% names(weekday_freq))) if(n_weekdays == 7) { pattern_name = "Every day" } # Single day service else if(n_weekdays == 1) { wd = names(weekday_freq)[1] # while paste0(weekday, "s") is easier, this solution can be used for other languages pattern_name = c("Sunday" = "Sundays", "Monday" = "Mondays", "Tuesday" = "Tuesdays", "Wednesday" = "Wednesdays", "Thursday" = "Thursdays", "Friday" = "Fridays", "Saturday" = "Saturdays")[wd] } # Weekday Service else if(n_weekdays == 5 && length(intersect(names(weekday_freq), c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday"))) == 5) { pattern_name = "Weekdays" } # Weekend else if(n_weekdays == 2 && length(intersect(names(weekday_freq), c("Saturday", "Sunday"))) == 2) { pattern_name = "Weekends" } # Multiple weekdays that appear regularly else if(n_weekdays >= 2 && (max(weekday_freq) - min(weekday_freq)) <= 1) { wd = names(weekday_freq) ordered_wd = wd[order(match(wd, c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday")))] pattern_name = paste(ordered_wd, collapse = ", ") } # default else { pattern_name = paste(weekday_freq, names(weekday_freq), sep = "x ", collapse = ", ") } # add holidays if(nrow(calendar_holidays) > 0) { pattern_name <- paste0(pattern_name, " and ", paste(calendar_holidays$holiday, collapse = ", ")) } pattern_name <- paste0(pattern_name, " (", min(dates), " - ", max(dates), ")") return(pattern_name) }
We'll apply this function to our service patterns and create a table with ids and names.
servicepattern_names = gtfs$.$dates_servicepatterns %>% group_by(servicepattern_id) %>% summarise( servicepattern_name = suggest_servicepattern_name(date, calendar) ) print(servicepattern_names)
We can plot the service pattern like a calendar to visualise the different patterns. The original
services can be plotted similarly (given it's not too many) by using dates_services
and service_id
.
dates = gtfs$.$dates_servicepatterns dates$wday <- lubridate::wday(dates$date, label = T, abbr = T, week_start = 7) dates$week_nr <- lubridate::week(dates$date) dates <- dates %>% group_by(week_nr) %>% summarise(week_first_date = min(date)) %>% right_join(dates, by = "week_nr") week_labels = dates %>% select(week_nr, week_first_date) %>% unique() ggplot(dates) + theme_bw() + geom_tile(aes(x = wday, y = week_nr), color = "#747474") + scale_x_discrete(drop = F) + scale_y_continuous(trans = "reverse", labels = week_labels$week_first_date, breaks = week_labels$week_nr) + theme(legend.position = "bottom", axis.text.x = element_text(angle = 90, hjust = 1)) + labs(x = NULL, y = "Date of Sundays") + facet_wrap(~servicepattern_id, nrow = 1)
We can plot the number of trips for each day as a calendar heat map.
trips_servicepattern = left_join(select(gtfs$trips, trip_id, service_id), gtfs$.$servicepatterns, by = "service_id") trip_dates = left_join(gtfs$.$dates_servicepatterns, trips_servicepattern, by = "servicepattern_id") trip_dates_count = trip_dates %>% group_by(date) %>% summarise(count = dplyr::n()) trip_dates_count$weekday <- lubridate::wday(trip_dates_count$date, label = T, abbr = T, week_start = 7) trip_dates_count$day_of_month <- lubridate::day(trip_dates_count$date) trip_dates_count$first_day_of_month <- lubridate::wday(trip_dates_count$date - trip_dates_count$day_of_month, week_start = 7) trip_dates_count$week_of_month <- ceiling((trip_dates_count$day_of_month - as.numeric(trip_dates_count$weekday) - trip_dates_count$first_day_of_month) / 7) trip_dates_count$month <- lubridate::month(trip_dates_count$date, label = T, abbr = F) ggplot(trip_dates_count, aes(x = weekday, y = -week_of_month)) + theme_bw() + geom_tile(aes(fill = count, colour = "grey50")) + geom_text(aes(label = day_of_month), size = 3, colour = "grey20") + facet_wrap(~month, ncol = 3) + scale_fill_gradient(low = "cornsilk1", high = "DarkOrange", na.value="white")+ scale_color_manual(guide = "none", values = "grey50") + theme(axis.text.y = element_blank(), axis.ticks.y = element_blank()) + theme(panel.grid = element_blank()) + labs(x = NULL, y = NULL, fill = "# trips") + coord_fixed()
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