U.S. Transportation
Joe Long
10/06/2020
Moving 12-Month Total Vehicle Miles Traveled (M12MTVUSM227NFWA)
indicator <-fredr_series_observations(series_id = "M12MTVUSM227NFWA",
observation_start = as.Date("2015-01-01"))
colnames(indicator) <- c("date","series","miles")
indicator[,c(1,3)] %>% slice_sample(n = 10)%>% arrange(date)%>% kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
column_spec(2, T, color = "blue" ) | date | miles |
|---|---|
| 2016-01-01 | 3101553 |
| 2016-07-01 | 3141648 |
| 2016-08-01 | 3148652 |
| 2017-04-01 | 3186368 |
| 2017-05-01 | 3189660 |
| 2018-02-01 | 3216597 |
| 2018-12-01 | 3240325 |
| 2019-05-01 | 3252151 |
| 2019-07-01 | 3255029 |
| 2020-07-01 | 2970035 |
indicator %>% ggplot() +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=dmy('01-10-2020'), ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
geom_line(mapping = aes(x=date,y=miles), color = "blue4",size=1) +
labs(title = "Moving 12-Month Total Vehicle Miles Traveled_ Total",
subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
x="Time", y="Millions of Miles",
caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego")+
theme_economist()
Vehicle Miles Traveled (TRFVOLUSM227NFWA)
indicator <-fredr_series_observations(series_id = "TRFVOLUSM227NFWA",
observation_start = as.Date("2019-01-01"))
colnames(indicator) <- c("date","series","miles")
indicator[,c(1,3)] %>% slice_sample(n = 10)%>% arrange(date)%>% kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
column_spec(2, T, color = "blue" ) | date | miles |
|---|---|
| 2019-01-01 | 248210 |
| 2019-03-01 | 271461 |
| 2019-05-01 | 286018 |
| 2019-06-01 | 280938 |
| 2019-08-01 | 286587 |
| 2019-09-01 | 271696 |
| 2020-01-01 | 253621 |
| 2020-02-01 | 231843 |
| 2020-03-01 | 220101 |
| 2020-04-01 | 168499 |
indicator %>% ggplot() +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=dmy('01-10-2020'), ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
geom_line(mapping = aes(x=date,y=miles), color = "blue4",size=1) +
labs(title = "Vehicle Miles Traveled",
subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
x="Time", y="Millions of Miles",
caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego")+
theme_economist()
Load Factor for U.S. Air Carrier Domestic and International, Scheduled Passenger Flights (LOADFACTOR)
indicator <-fredr_series_observations(series_id = "LOADFACTOR",
observation_start = as.Date("2000-01-01"))
colnames(indicator) <- c("date","series","percent")
indicator[,c(1,3)] %>% slice_sample(n = 10)%>% arrange(date)%>% kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
column_spec(2, T, color = "blue" ) | date | percent |
|---|---|
| 2001-11-01 | 66.9 |
| 2002-11-01 | 67.6 |
| 2004-06-01 | 81.5 |
| 2008-01-01 | 74.0 |
| 2011-09-01 | 81.9 |
| 2012-03-01 | 83.0 |
| 2012-04-01 | 82.5 |
| 2016-01-01 | 80.2 |
| 2016-08-01 | 84.6 |
| 2020-06-01 | 54.1 |
indicator %>% ggplot() +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=dmy('01-10-2020'), ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
geom_line(mapping = aes(x=date,y=percent), color = "blue4",size=1) +
labs(title = "U.S. Air Passenger _ Load Factor",
subtitle = str_glue("Domestic & International\nFrom {min(indicator$date)} through {max(indicator$date)}"),
x="Time", y="Percent of load",
caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego")+
theme_economist()
tail(indicator,n=12) %>%
ggplot() +
geom_line(mapping = aes(x=date,y=percent), color = "blue4",size=1) +
labs(title = "U.S. Air Passenger _ Load Factor",
subtitle = "May 2019 to May 2020",
x="Time", y="Percent of load",
caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego")+
theme_economist()
## Public Transit Ridership (TRANSIT)
indicator <-fredr_series_observations(series_id = "TRANSIT",
observation_start = as.Date("2000-01-01"))
colnames(indicator) <- c("date","series","value")
indicator[,c(1,3)] %>% slice_sample(n = 12)%>% arrange(date)%>% kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
column_spec(2, T, color = "blue" ) | date | value |
|---|---|
| 2001-03-01 | 846702 |
| 2001-06-01 | 807301 |
| 2005-05-01 | 806149 |
| 2007-10-01 | 946754 |
| 2011-10-01 | 919419 |
| 2012-08-01 | 893072 |
| 2013-05-01 | 908338 |
| 2013-09-01 | 900750 |
| 2015-04-01 | 901828 |
| 2016-08-01 | 864510 |
| 2017-09-01 | 844867 |
| 2018-12-01 | 763263 |
indicator %>% ggplot() +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=dmy('01-10-2020'), ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
geom_line(mapping = aes(x=date,y=value), color = "blue4",size=1) +
labs(title = "Public Transit Ridership",
subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
x="Time", y="Thousands of trip tickets",
caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego")+
theme_economist()
tail(indicator,n=12) %>%
ggplot() +
geom_line(mapping = aes(x=date,y=value), color = "blue4",size=1) +
labs(title = "Public Transit Ridership",
subtitle = "Most recent 12 months",
x="Time", y="Thousands of trip tickets",
caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego")+
theme_economist()
Rail Passenger Miles (RAILPM)
indicator <-fredr_series_observations(series_id = "RAILPM",
observation_start = as.Date("2000-01-01"))
colnames(indicator) <- c("date","series","value")
indicator[,c(1,3)] %>% slice_sample(n = 12)%>% arrange(date)%>% kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
column_spec(2, T, color = "blue" ) | date | value |
|---|---|
| 2001-08-01 | 562266005 |
| 2009-04-01 | 469783200 |
| 2010-03-01 | 538675366 |
| 2013-06-01 | 638602721 |
| 2013-09-01 | 517765302 |
| 2014-03-01 | 542911331 |
| 2014-04-01 | 545773080 |
| 2014-08-01 | 649665562 |
| 2015-01-01 | 458200128 |
| 2017-07-01 | 667515087 |
| 2018-01-01 | 438710211 |
| 2018-07-01 | 655798387 |
indicator %>% ggplot() +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=dmy('01-10-2020'), ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
geom_line(mapping = aes(x=date,y=value/1000000), color = "blue4",size=1) +
labs(title = "Rail Passenger Miles",
subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
x="Time", y="Millions of Miles",
caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego")+
theme_economist()
tail(indicator,n=12) %>%
ggplot() +
geom_line(mapping = aes(x=date,y=value/1000000), color = "blue4",size=1) +
labs(title = "Rail Passenger Miles",
subtitle = "Most recent 12 months",
x="Time", y="Millions of Miles",
caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego")+
theme_economist()
Air Revenue Passenger Miles (AIRRPMTSID11)
A revenue passenger mile (RPM) is a transportation industry metric that shows the number of miles traveled by paying passengers and is typically an airline traffic statistic.
Revenue passenger miles are calculated by multiplying the number of paying passengers by the distance traveled. For example, an airplane with 100 passengers that flies 250 miles has generated 25,000 RPM.
Revenue passenger mile (RPM) is a transportation industry metric primarily used by the airline industry to show the number of miles traveled by paying passengers.
indicator <-fredr_series_observations(series_id = "AIRRPMTSID11",
observation_start = as.Date("2000-01-01"))
colnames(indicator) <- c("date","series","value")
indicator[,c(1,3)] %>% slice_sample(n = 12)%>% arrange(date)%>% kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
column_spec(2, T, color = "blue" ) | date | value |
|---|---|
| 2000-08-01 | 58870677 |
| 2000-09-01 | 61815896 |
| 2001-03-01 | 59252860 |
| 2001-10-01 | 45715767 |
| 2006-04-01 | 67955569 |
| 2012-01-01 | 68847324 |
| 2012-06-01 | 69241615 |
| 2012-11-01 | 69557561 |
| 2013-03-01 | 70132032 |
| 2014-05-01 | 72509487 |
| 2015-01-01 | 73804165 |
| 2017-04-01 | 80742832 |
indicator %>% ggplot() +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=dmy('01-10-2020'), ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
geom_line(mapping = aes(x=date,y=value/1000), color = "blue4",size=1) +
labs(title = "Air Revenue Passenger Miles (RPM) _ AIRRPMTSID11",
subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
x="Time", y="Millions",
caption = "Data source: FRED St. Louis Federal Reserve\nIllustration by @JoeLongSanDiego")+
theme_economist()
tail(indicator,n=12) %>%
ggplot() +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=dmy('01-10-2020'), ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
geom_line(mapping = aes(x=date,y=value/1000), color = "blue4",size=1) +
labs(title = "Air Revenue Passenger Miles (RMP) _ AIRRPMTSID11",
subtitle = "Most recent 12 months period",
x="Time", y="Millions",
caption = "Data source: FRED St. Louis Federal Reserve\nIllustration by @JoeLongSanDiego")+
theme_economist()