Continuing BB’s intermittent meander on bus ridership and transit in Burque, let’s get back to “farebox recovery” and last year’s ordinance that ABQ Ride achieve a 25% farebox recovery ratio (or .25, if you prefer) by June 30, 2022. Farebox recovery is basically how much a bus system takes in via fares versus its operating expenses.

As mentioned a few posts back, the 2015 ratio was 12.6% in a down year of lower gasoline prices from the 2014 ratio of 14.1%. The highest it’s been in recent years is only 16%. So how is ABQ Ride going to meet the ordinance goal of 25%?

To give some context of the challenge, let’s plug in the claimed 50% increase in Rapid Ride ridership that will come with Albuquerque Rapid Transit. With the current fare structure of $1 single ride and plugging that fare into a best case scenario of highest Rapid Ride ridership (3.114M in 2012), and the highest recent prior ratio of 16% we get:

• Additional ridership: 3.114M x 1.5 = 4.671M or 1.557M new riders
• 1.557M x a buck = (obviously) $1.557M
• Adding that figure to the highest prior overall (all routes) revenue of $4.625M we get: $6.182M coming in
• And even assuming ABQ Ride operating expenses don’t rise a penny and stay at 2015’s $33.405M that’s
• $6.182M coming in and $33.405M going out or
• A farebox recovery ratio of 18.5%

In other words, a 50% rise in Rapid Ride ridership in the next five years only gets us about halfway to the increase needed to meet the ordinance goal. Something else is gonna have to happen. What are our choices? Well there’s:

1. Raise the fares;
2. Raise the ridership on non Rapid Ride routes with the current fare structure;
3. Dramatically raise bus ridership via lower fares that attract significantly more “discretionary riders” (more about that below);
4. Lower the operating costs;
5. Some combination of the above.

So what’s it gonna be? I’m gonna take a wild speculation and guess that #1 above will be the necessary action taken. And that has some interesting impacts both economic and socio-economic.

A recent paper by Todd Litman of the Victoria (Canada) Transport Policy Institute explores those impacts, while also illustrating that my list above isn’t complete and transit pricing structures are more complicated than one (e.g., me) might initially think. Litman reviews a number of studies on the concept of transit price elasticity. “Elasticity” here refers to…well let’s just have Litman explain it:

Price sensitivity is measured using elasticities, defined as the percentage change in consumption resulting from a one-percent change in price, all else held constant. A high elasticity value indicates that a good is price-sensitive, that is, a relatively small change in price causes a relatively large change in consumption. A low elasticity value means that prices have relatively little effect on consumption. The degree of price sensitivity refers to the absolute elasticity value, that is, regardless of whether it is positive or negative.

For example, if the elasticity of transit ridership with respect to transit fares is –0.5, this means that each 1.0% increase in transit fares causes a 0.5% reduction in ridership, so a 10% fare increase will cause ridership to decline by about 5%. Similarly, if the elasticity of transit ridership with respect to transit service hours is 1.5, a 10% increase in service hours would cause a 15% increase in ridership.

Ah, so our list above is a bit too limited. ABQ Ride could increase its service hours, which would theoretically bump its ridership by some degree of elasticity. Of course, that increase would also cause operating costs to rise. Or, we could reduce service hours (although some bus users might snort that this would be almost impossible, given how little service there is in some parts of town already) to reduce operating expenses.

It’s a complicated interplay, one Litman notes has become more so with researchers including more and more intervening variables, such as price of gasoline, size of city and ratio of riders who are choosing to ride when other means, like a car, are available, versus those who do not literally (auto) or logically (bike/walk) have the same option. Here’s Litman on “discretionary riders” and price sensitivity:

Transit dependent riders are generally less price sensitive than choice or discretionary riders (people who have the option of using an automobile for that trip). Certain demographic groups, including people with low incomes, non-drivers, people with disabilities, high school and college students, and elderly people tend to be more transit dependent. In most communities transit dependent people are a relatively small portion of the total population but a large portion of transit users, while discretionary riders are a potentially large but more price elastic transit market segment.

The same complexity courses through the rest of Litman’s analysis, including observation that earlier studies, and subsequent transit planning, were inadequately complex. For example, transit planners long followed the “Simpson-Curtin Rule,” which stated that a 1% decrease in ridership could be expected with a 3% rise in fares. Subsequent studies, better including factors such as those mentioned above, show that Simpson-Curtin understates the effect.

Concluding his review, Litman asserts the following, caveat included:

An important conclusion of this research is that no single transit elasticity value applies in all situations: various factors affect price sensitivities including type of user and trip, geographic conditions and time period. Available evidence suggests that the elasticity of transit ridership with respect to fares is usually in the –0.2 to –0.5 range in the short run (first year), and increases to –0.6 to – 0.9 over the long run (five to ten years). These are affected by the following factors:

• Transit price elasticities are lower for transit dependent riders than for discretionary (“choice”) riders.

• Elasticities are about twice as high for off-peak and leisure travel as for peak and commute travel.

• Cross-elasticities between transit and automobile travel are relatively low in the short run (0.05), but increase over the long run (probably to 0.3 and perhaps as high as 0.4).

• A relatively large fare reduction is generally needed to attract motorists to transit, since they are discretionary riders. Such travelers may be more responsive to service quality (speed, frequency and comfort), and higher automobile operating costs through road or parking pricing.

• Due to variability and uncertainty it is preferable to use ranges rather than point values for elasticity analysis.

Much can be drawn from Litman’s review and conclusions, including the simple observation that it would be ever-so-refreshing to have a community conversation about transit in Albuquerque, Albuquerque Rapid Transit included, as it pertains to Litman’s work, instead of the fallacy-filled screaming match we are having instead.  Oh well.

That said, boiling Litman’s conclusions down into how they fit the choices before ABQ Ride in achieving the 25% ordinance goal by 2022, it’s quite arguable whether a rise in fares would get us significantly closer to achieving the recovery goal, not to mention the potential economic inequity such a price increase would have on those who aren’t discretionary riders.  More on that and a possible, if controversial, remedy next time.

Let’s close, for now, with one additional wrinkle to all of this. Today, Albuquerque Rapid Transit tweeted the following:

Hungry for depth in better understanding the aforementioned claim of a 50% increase in ridership, is the tweet above making the point that such an increase can only be expected when we have BRT not only along Central Avenue, but also University Boulevard? Because on one level that makes sense (How is the Central line going to see 1.3M new riders on its own?), and another level makes such a claim dependent on what seems a rather tenuous supposition…that we’re gonna get a second BRT line. Ever.

Upon reflection; however, you’ve gotta like ABQ Ride’s moxie. Now if we could just get a bit more information, logic, analysis and “plan” to go along with it.