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---
title: "Service Patterns and Calendar Schedules"
author: "Flavio Poletti"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{tidytransit-Service Patterns}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r setup, include=FALSE}
library(knitr)
library(tidytransit)
library(dplyr)
library(lubridate)
library(ggplot2)
knitr::opts_chunk$set(echo = TRUE)
```
## Overview
Each trip in a GTFS feed is referenced to a service_id (in trips.txt). The [GTFS reference](https://developers.google.com/transit/gtfs/reference/#calendartxt)
specifies that a "service_id contains an ID that uniquely identifies a set of dates when
service is available for one or more routes". A service could run on every weekday or only
on Saturdays for example. Other possible services run only on holidays during a year,
independent of weekdays. However, feeds are not required to indicate anything with
service_ids and some feeds even use a unique service_id for each trip and day. In
this vignette we'll look at a general way to gather information on when trips run by
using "service patterns".
Service patterns can be used to find a typical day for further analysis like routing or
trip frequencies for different days.
## Prepare data
We use a feed from the New York Metropolitan Transportation Authority. It is provided
as a sample feed with tidytransit but you can read it directly from the MTA's website.
```{r}
local_gtfs_path <- system.file("extdata", "google_transit_nyc_subway.zip", package = "tidytransit")
gtfs <- read_gtfs(local_gtfs_path, local=TRUE)
# gtfs <- read_gtfs("http://web.mta.info/developers/data/nyct/subway/google_transit.zip")
```
With `set_date_service_table()` we add a table to the feed that indicates which `service_id`
runs on which date. This is later useful for linking dates and trips via `service_id`.
```{r}
gtfs <- set_date_service_table(gtfs)
head(gtfs$.$date_service_table)
```
To understand service patterns better we need information on weekdays and holidays. With
a calendar table we know the weekday and possible holidays for each date. We'll use a minimal example
with two holidays.
```{r}
holidays = tribble(~date, ~holiday,
ymd("2018-07-04"), "Independence Day",
ymd("2018-09-03"), "Labor Day")
calendar = tibble(date = unique(gtfs$.$date_service_table$date)) %>%
mutate(
weekday = (function(date) {
c("Sunday", "Monday", "Tuesday",
"Wednesday", "Thursday", "Friday",
"Saturday")[as.POSIXlt(date)$wday + 1]
})(date)
)
calendar <- calendar %>% left_join(holidays, by = "date")
head(calendar)
```
To analyse on which dates trips run and to group similar services we use service patterns.
Such a pattern simply lists all dates a trip runs on. For example, a trip with a pattern
like _2019-03-07, 2019-03-14, 2019-03-21, 2019-03-28_ runs every Thursday in March 2019.
To handle these patterns we create a `servicepattern_id` using a hash function. Ideally there are
the same number of servicepattern_ids and service_ids. However, in real life feeds this is rarely
the case. In addition, the usability of service patterns depends largely on the feed and its
complexity.
```{r}
gtfs <- set_servicepattern(gtfs)
```
Our gtfs feed now contains the data frame `service_pattern` which links each `servicepattern_id`
to an existing `service_id` (and by extension `trip_id`).
```{r}
head(gtfs$.$service_pattern)
```
In addition, `gtfs$.$date_servicepattern_table` has been created which connects dates and service
patterns (like `date_service_table`). We can compare the number of service patterns to the number of services.
```{r}
# service ids used
n_services <- length(unique(gtfs$trips$service_id)) # 70
# unique date patterns
n_servicepatterns <- length(unique(gtfs$.$service_pattern$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](https://opentransportdata.swiss/dataset/timetable-2019-gtfs)
uses around 15'600 service_ids which all identify unique date patterns.
## Analyse Data
### Exploration Plot
We'll now try to figure out usable names for those patterns. A good way to start is visualising the data.
```{r fig.height=4, fig.width=7}
date_servicepattern_table <- gtfs$.$date_servicepattern_table %>% 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.
### Names for service patterns
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.
```{r}
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.
```{r}
servicepattern_names = gtfs$.$date_servicepattern_table %>%
group_by(servicepattern_id) %>% summarise(
servicepattern_name = suggest_servicepattern_name(date, calendar)
)
print(servicepattern_names)
```
## Visualise services
### Plot calendar for each service pattern
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 `date_service_table` and `service_id`.
```{r fig.height=4, fig.width=7}
dates = gtfs$.$date_servicepattern_table
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)
```
### Plot number of trips per day as calendar
We can plot the number of trips for each day as a calendar heat map.
```{r fig.height=4, fig.width=7}
trips_servicepattern = left_join(select(gtfs$trips, trip_id, service_id), gtfs$.$service_pattern, by = "service_id")
trip_dates = left_join(gtfs$.$date_servicepattern_table, trips_servicepattern, by = "servicepattern_id")
trip_dates_count = trip_dates %>% group_by(date) %>% summarise(count = dplyr::n())
trip_dates_count$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 = F, 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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