If you downloaded the summary file for the 2015 National Transit Database that the Antiplanner posted December 30, please do so again (link fixed). I discovered I made an error transferring the operating cost data from the raw files to this summary sheet, and the revised version corrects this error.
The revised version, which is about 1.6 megabytes, also has a lot more calculations in it. These include vehicle occupancy (passenger miles divided by vehicle revenue miles), average number of seats per vehicle, and average standing room per vehicle. Some columns calculate operating and maintenance costs per passenger mile or vehicle mile, but these should be used with care as maintenance costs can vary tremendously from year to year.
As I promised yesterday, I’ve compiled what I consider to be the most important data in the 2015 National Transit Database into one spreadsheet. These data include trips, passenger miles, vehicle revenue miles & hours, weekday trips, fares, operating costs, maintenance costs, capital costs, BTUs of energy consumption, and grams of carbon dioxide emissions.
The 2015 database is expanded from previous years, which just included data from transit systems in major urban areas over 50,000 people. The 2015 data also include transit systems in minor urban areas of under 50,000 people, rural areas, and Indian reservations. The major urban area data fill the first 2,066 lines of the spreadsheet, while the rest fill the next 1,549 lines. The major urban areas accounted for 10.377 billion transit trips in 2015, while the smaller areas accounted for a mere 128 million trips.
The Federal Transit Administration has posted 2015 transit data as part of the National Transit Database. However, the Department of Transportation’s new web sites have made downloading data fairly tedious.
To save you time, I’ve downloaded the data and then uploaded them in two zip files. First is the Historic Time Series showing data from 1991 through 2015. Second is a more detailed 2015 database, providing safety, energy, and other detailed data not found in the historic time series. Each of these files is between 10 and 11 megabytes in size.
For simple things such as capital costs, operating costs, fares, trips, and passenger miles, the historic time series is an excellent source of information. The most useful files are table 2.1, “operating expenses and services,” which has separate sheets for operating costs, fares, vehicle revenue miles and hours, trips, and passenger miles, and table 3.1, “uses of capital costs,” which has capital expenses. All of the sheets in these two tables break down data by transit agency and mode. Unfortunately, the capital expense sheet does not break down the difference between new projects and maintenance of existing projects.
Because the past few years have seen the slowest recovery from a recession on record, the Federal Reserve Bank has been keeping interest rates low and in fact cutting them to almost zero. But this has raised concerns among leading bankers that the low rates are producing another asset bubble, including another housing bubble.
The above graph shows the home price index for several metropolitan areas calculated by the Federal Housing Finance Agency using the Case-Shiller method. (The official Case-Shiller Index published by Standard & Poors doesn’t include as many metropolitan areas as the FHFA index.) It shows that, not only are housing prices rising again, in some urban areas–on the chart, Honolulu, San Francisco-Oakland, San Jose, and Seattle–already have prices much greater than they were at the peak of the 2006 bubble. It seems likely that these prices are going to crash again soon.
The energy efficiency of the average car on the road increased slightly in 2014 as did air travel, but the average light truck and Amtrak used slightly more BTUs per passenger mile in 2014 than in 2013. That’s the finding from the latest edition of the Transportation Energy Data Book (6.5-MB PDF), which was posted on line on Monday. Specifically, these numbers are from tables 2-15, highway modes, and 2-16, non-highway modes.
The book is published each year by the Department of Energy’s Oak Ridge National Laboratory. In addition to the book in PDF format and the individual spreadsheets for each of the 250 tables in the book, they usually have a link to all the spreadsheets in ZIP format, but the isn’t available yet.
According to the spreadsheets, most forms of urban transit became a little more energy efficient in 2014. I suspect declining fuel prices will produce some different results for 2015. Transit ridership is falling, so transit’s energy efficiency per passenger mile is likely to decline, as is Amtrak’s. If falling fuel prices allowed airlines to keep fares lower and fill more seats, airline fuel efficiencies may increase.
The share of American workers who live in households with no vehicles yet nonetheless drive alone to work grew from 20.4 percent in 2014 to 20.9 percent in 2015, according to the latest American Community Survey. This growth came at the expense of slight declines in carpooling, transit, work-at-homes, and “other” (taxi, bicycle, motorcycle), while walking to work increased slightly. No one knows for certain how people with no cars drive alone to work, but most probably use employer-supplied vehicles.
You can download 2015 commuting data by numbers of vehicles in the household for the nation, states, and counties, cities and other places, and urbanized areas. For comparison, 2014 data for the nation, states, and counties, cities, and urbanized areas are also available.
Only 4.5 percent of American workers live in households with no vehicles, a share that remained stable from 2014 to 2015. Nearly a third of them are in the New York urban area. Outside of the New York area, the only places with double-digit vehicle-less households tend to be in the Boston, San Francisco-Oakland, and Washington, DC urban areas.
For the United States as a whole, the value of a median-priced owner-occupied home increased from 2.7 times median family incomes in 2013 to 2.8 times in 2014. The 2014 numbers are from the 2015 American Community Survey, which estimates both home values and family incomes for the year before the survey. In the survey, median family incomes are found in table B19101 while median home values are in table B25077.
You can download my spreadsheets combining data from these two tables from the 2015 survey (which, remember, are for 2014) for the nation, states, and counties, urbanized areas, and cities and other places. For comparison, data for 2013 (from the 2014 survey) can be downloaded for nation, states, and counties, urbanized areas, and cities and other places.
In places where land for new housing is abundant, value-to-income ratios tend to hover around 2. Value-to-income ratios above 3 suggest real or artificial limits on the ability of homebuilders to meet the demand for new housing. While the national ratio of 2.8 is worrisome, many states are well under this ratio.
The share of commuters driving alone to work grew from 80.0 percent in 2014 to 80.3 percent in 2015, according to the Census Bureau’s American Community Survey. This increase came at the expense of carpoolers; the share of people taking transit, walking, and cycling remained the same.
The Census Bureau posted 2015 data early this month, giving data junkies lots of information to play with. The bureau has conducted the American Community Survey every year since 2005 based on surveys sent out to about 3.5 million households each year. This makes it far more reliable than a typical poll, which usually surveys only a few hundred people. However, the data should still be used with caution for small categories, such as the number of Latinos living in households with no cars who walk to work in Buffalo, New York.
To save you time, the Antiplanner has downloaded journey-to-work data, table B08301, for the nation, states, and counties, urbanized areas, and cities and other places. For comparison, I’ve also posted the same raw data for 2014: nation, states, and counties, urbanized areas, and cities and other places.
A few weeks ago, the Antiplanner reported on a questionable change in transportation data published by the Bureau of Transportation Statistics. An even more questionable change can be found in table VM-1 of Highway Statistics, an annual report published by the Federal Highway Administration.
Before 2009, Highway Statistics regularly appeared months before the Federal Transit Administration published its annual National Transit Database for the same year. There may have been good reasons for that: the highway data depended on reports from the 50 states and District of Columbia, while the transit data depended on data from nearly 700 transit agencies (as of 2008; more than 850 today). Collecting, reviewing, and collating all that data no doubt took a lot of time.
After Obama took office, a funny thing happened: the highway data started coming out after the transit data. In some cases, not just months, but years after. For example, the on-line 2009 Highway Statistics is still missing some minor tables, and one of the most important tables about highway finance, HF-10, is still missing from the 2011 edition.
According to page 57 the European Union publication, Panorama of Transport, 43 percent of American freight is shipped by rail and 30 percent goes by truck. This makes the American rail freight system the envy of the world, as just 10 percent of European freight goes by rail, with 46 percent going by truck, and just 4 percent of Japanese freight goes by rail, with 60 percent going by truck.
The EU got its U.S. data from the Bureau of Transportation Statistics’ annual National Transportation Statistics report. However, if you look at the most recent edition of table 1-50: U.S. ton-miles of freight in that report, you won’t find those numbers at all. Instead (using 2006 data), something like 42 percent of freight goes by truck while only 32 percent goes by rail. That’s still a greater share of freight going by rail than in Europe or Japan, but a reversal in dominance between rail and trucks.
Yet the Panorama of Transport didn’t get it wrong. I have the corresponding table from the 2008 edition of National Transportation Statistics, and the numbers for 2006 in that table agree with those used by the EU. So what happened to change the numbers?