Category Archives: Economics

The 60-Year Line

Whenever we build a piece of large-scale infrastructure, we should be thinking about the markets it serves today, and the market it serves over its lifetime. We are often building lines that aim to promote development. That is, they are serving non-places in the hope they become places. The evidence on this is mixed. Sometimes lines successfully promote development, sometimes they don’t. If the lines were privately built (as in times of yore), this would be much less of public policy question, as the public is not bearing the monetary risk. That is not to say there are no policy questions, the line-builder wants right-of-way, and that often requires eminent domain powers.

However the lines are now publicly built, so the public is bearing the risk so that the privately owned lands might appreciate in value, and the public might get a small share of that increment. Usually we don’t employ value capture. General tax revenues are not nearly enough to justify the line, since lines are expensive now — all the good lines, the low-hanging fruit, have been built, and most development is a transfer from one place to another.

The risk is the capital outlay will not be recovered from future revenue (from users, or non-users).

In contrast, building lines where people actually are, where demand currently exists, presents much lower risk in revenue projections.

Lines typically last upwards of 60 years with a given technology. We certainly cannot predict 60 years into the future. 60 years ago was before both the Shinkansen and the Interstate Highway System. Predictions from 60 years ago about today were not terribly accurate. Sixty years is longer than a Kondratieff Cycle.

Will today’s places have any activity in 60 years? A good test of that is whether the place had activity 60 years ago. Look at the map of 60 years ago. Where was the activity? Where is it today? The intersection of those two maps show places with proven longevity. There are no guarantees those places will have activity in 60 years of course (“past performance is no guarantee of future results”), but they are more likely to because there is an underlying cause for the stability of the place. That is, there was a cause for that place to develop in the first place (e.g. a useful waterfall, a port, or a junction between intercity rail lines), and the positive feedback structure between transportation, accessibility, and land use actively worked to reinforce the strength of that place.

Value Capture Flowchart
Value Capture Flowchart

Applying that to the Twin Cities, the best prediction you can make is that there will be strong demand between Downtown Minneapolis and Downtown St. Paul. We currently serve that corridor with interstate highway and transit.

Applying that again to the Twin Cities, the newest places (if we can call them that) outside the beltway are making claims for long-term investments of resources fixing them into the urban system without the evidence of long-term stability (See e.g. the SW LRT to a park and ride lot on Mitchell Road, or Highway 212, or the Bottineau Line to a cornfield, or Highway 610). It is certainly possible those destinations will become significant demand generators, but it is far from certain. If a private firm wanted to bear the risk of those prospective developments not working out, more power to them. But the public is asked to do this, while perfectly good markets go unserved or underserved for lack of capital.

Met Council board not big transit riders, survey finds | startribune

Eric Roper at the Strib riffs on my dogfooding article and does a local study: Met Council board not big transit riders, survey finds

“We should ask whether members of the council have sufficient expertise about transit … to be managing a transit system. Do they understand the problems at a deep level?” said University of Minnesota professor David Levinson, who researches transportation systems and has written about the need for transit decisionmakers to commute on their own product.

Levinson, the professor, compared the low transit usage by the Met Council to the board of Apple not using computers. He has frequently criticized the lack of information at most Twin Cities bus stops when compared to other cities, including route numbers, destinations, frequency and maps.

“Having that experience of being lost on the transit system is probably a useful experience for [council members] to have to understand why their system isn’t as attractive as it should be, why it’s not as popular as they hope it would be,” Levinson said.

“It’s a success”

There are no more common words to hear shortly after the opening of a new rail project in the United States than “It’s a success”. The forecast of the declaration of success is far more accurate the forecast of ridership or costs.

For instance, Metrorail (WMATA) claims:

Metro: Silver Line ridership remains strong

Metro today provided updated Silver Line ridership information showing that, less than two months after opening, the new line is already performing at 60 percent of its projected ridership for the end of the first full year of service. As of last week, an average of 15,000 riders are entering the system at the five new Silver Line stations on weekdays for a combined 30,000 trips to or from the new stations.

In the planning process, Silver Line ridership was projected to reach 25,000 boardings at the five new stations after one full year of service.

Metro estimates that the Silver Line is currently adding approximately 6,000 new riders — making roughly 12,000 trips — to the Metrorail system each weekday. The balance, approximately 9,000 riders, are primarily former Orange Line riders who have switched to the Silver Line.

Some outlets have used the word “success” to describe the line, as has Secretary Foxx. Certainly it is still early, and maybe the Silver Line will exceed first year forecasts, or final year forecasts, or even have benefits in excess of costs, or somehow reduce inequity in the Washington region, or lead to economic development, or any number of other objectives hoisted on transit lines. It is arguably successful from a project delivery perspective, in that it was delivered, and opened for service, but that seems a narrow way to think about success.

In contrast, another new start, Metro Transit’s Green Line, has done a bit better, even with all sorts of traffic signal timing issues. It too is heralded as a success, with ridership exceeding forecast year ridership about 3 months in.  While many of its riders were transfers from existing bus services, it clearly is serving more new people for less money than the Silver Line.

Which is more successful? Which is a better investment? Time will tell, and I will leave that to the reader’s judgment.

I have two hypotheses as to why these words are so common.

First, it may be that all projects are successful. For this hypothesis to hold, we would need to see enormous transit market share across the country after several decades of more than 20% of all transportation funding going to transit (figure 2, but also this). Sadly the evidence suggests otherwise.

Alternatively, it may be that the appearance of success is important, independent of the actual facts on the ground. Calling “success” aligns you with “Team Rail” and rewards your supporters. The illusion of success is critical to obtain future funds. No one wants to give money to an agency that actively (if honestly) claims “It’s a failure” or “It’s a disappointment”, or “We’re still perfecting it,” or even “It’s a hobby“.

I hold this latter explanation as more likely. This is not to say there are no successes in urban rail transit. There are many. Starting in 1863 with the  London Underground, rail transit globally had an extraordinarily good run for 60 years. In the US, it sort of petered out after that for the next 50 years or so, though in other countries, rail transit has continued at various levels of strengths.

Some of the lines in the past 40 years have been more successful than others, all depending on your definition of success. (For instance, a list of LRT systems by ridership per mile is here.) The best systems remain the ones built in the early 20th century, with only LA’s Metro Rail breaking the top 5 in riders per mile (and DC’s MetroRail coming 6th).  Yet as far as I can tell, all new systems have been declared successful by somebody (even the relatively low ridership per mile lines like Tampa’s TECO line, or Charlotte’s Lynx). Some are even pre-declared, like The Tide in Hampton Roads.

I find it hard to see billions being spent on the Silver Line so far to add 6000 riders (12000 trips)  as an unqualified success, (I would find it hard to see meeting these low forecasts as a success either). This is more $ per passenger than many commuter rail lines spend, which few outside the agencies themselves are calling successes (the advocates of course do use that exact word).

If spending $2B added zero or negative riders, that would be truly surprising, indicative of active destruction of money. I will just state there were plausible alternative uses of the funds that would have improved society in other ways. Every expenditure has an opportunity cost.

Do not believe or repeat the press releases of agencies and advocates uncritically.

 

 

How to account for higher quality of service in Benefit/Cost Analysis

I recently had an twitter and email conversation with Benjamin Ross about rail vs. bus benefit/cost analysis (BCA).

The problem is that conventional BCA in practice does not consider the quality differences of different modes, focusing primarily on travel time, monetary costs, and monetized externalities. Assuming everything else were analyzed correctly, this leads us to over-invest in low quality modes and under-invest in high quality modes, from a welfare-maximizing perspective.

Let’s start with a few premises

1. The value of time (value of travel time savings) of each user differs because of a variety of factors. Everyone is in a hurry sometimes, and so has a higher value of time (willingness to pay for saving time) when time-strapped than at other times. Some people have more money than others, and so find it easier to pay to save time. The related notion of value of travel time reliability (VTTR) is reviewed here.

2. We don’t actually know user value of time. (An alternative approach evaluates just based on travel time, and assumes everyone is equal, since time is just as fast for rich and poor people.  For instance Carlos Daganzo and his students (e.g. Gonzales) optimize in terms of time, and convert monetary and other costs into time, referring to value of time as a politically determined variable. E.g. section 2.3.2 here. developing a temporal value of money rather than a monetary value of time. This is not standard in transportation economics.)

3. We  assume the value of time of all users is the same in a Benefit/Cost Analysis because the alternative would bias investment toward users with a high value of time. E.g. wealthy people in the western suburbs would get more investment than poor people in the city because they have a higher value of time, which is politically unacceptable to admit, as they did not pay proportionate to their value of time (since transportation funding on major roads comes predominantly from gas taxes. In contrast for local roads it comes predominantly from property taxes, which of course are paid for more by the wealthy).  For a market good this is not a problem (rich people pay for and get better goods and services all the time, otherwise why be rich). We do BCA because transportation is a publicly provided good.

4. We have models which purport to know people’s value of time and do use that in forecasting travel demand. The ratio of coefficients to time costs and money costs is implicit in the mode choice model. The value of time is usually in practice estimated from revealed preference data, but values have a wide range depending on location and methodology.

5. Travel demand models are highly inaccurate, etc., for a variety of reasons.

6. If these models were correct, the log-sum of the denominator of the mode choice model multiplied by the value of time (determined by the coefficients on time and cost in the model), with a little math, gives you an estimate of Consumers Surplus. This estimate is not usually used in practice, as no one outside of economics and travel demand modeling believes in utility theory.

7. Benefit/Cost Analysis is much simpler (and more simplistic) than travel demand modeling, and uses travel time savings and monetary cost in estimating Consumers Surplus.

8. BCA doesn’t actually estimate CS, just change in CS, since we don’t know the shape of the demand curve, but can estimate small changes to the demand curve and assume the curve is linear. Those doing BCA often use the rule of 1/2 to find the area of the benefit trapezoid)

Area=benefit=(Tb-Ta)*(1/2)*(Qb+Qa).

Multiply the area by the Value of Time to monetize. This is shown in Figure 1.

BenRoss.001

9. This assumes the value of time experienced is the same independent of how it is experienced. Yet people clearly would pay more for a better experience. That doesn’t show up unless you have multiple demand curves (see below), and that is never done except by academics.

10. The travel demand model gives you an alternative specific constant (ASC), which says all else equal, mode X is preferred to mode Y, and will tell you how much additional demand there will be for X than Y under otherwise identical circumstances (namely price and time).

11. Empirical evidence suggests the ASC is positive for transit compared to car (all else equal, people like transit over car. Car mode shares are higher in most US markets because all else is not equal).

Usually the ASC is higher for new rail than new bus, since trains are a nicer experience. This is sometimes called the rail bias factor.

For instance Table 3 below reproduces values the FTA accepts for rail bias factors according to the linked report. The implication is that people would be willing to spend 15-20 minutes longer on a commuter rail than a local bus serving the same OD pair and otherwise with the same characteristics (except for the quality of the mode).

Much of this is just a question of modeling specification though, so e.g. the rationale includes things that (a) can be modeled and specified (but aren’t typically), and (b) may be improved for bus routes. Recent research says this number can be brought down a lot by better specification.

Mode

Constant Range (relative to Local Bus)

Rationale

Commuter Rail

15 – 20 minutes

Reliable (fixed‐guideway), vehicle and passenger amenities, visibility, station amenities, etc.

Urban Rail

10 – 15 minutes

Reliable due to dedicated, fixed‐guideway, well‐identified, stations and routes, etc.

BRT

5 – 10 minutes

Reliable when running on semi‐dedicated lanes, often times uses low access and especially branded vehicles

Express Bus

‐10 to 10 minutes

Non‐stop, single‐seat ride, comfort, reliable when running on semi‐dedicated lanes

Infrequent off‐peak service, unreliable when subject to road congestion

 

12. The Consumers Surplus from a mode choice model would reflect this with higher utility when rail is available than if bus were available.

13. The Consumers Surplus from BCA, using the rule of 1/2,  would be higher for a rail line (Figure 2) than a bus line (Figure 1) because the demand is higher.

BenRoss.002

14. The CS from BCA would not reflect fully the quality difference. It should be shown as moving the demand curve outward. The benefit from the red area (Figure 3) is missing.

 

BenRoss.003

 

 

15. The red area is impossible to estimate with any confidence, since the shape of the curves outside the known area (before and after) is unknown. I drew the total consumers surplus as a triangle (and the change in CS as a trapezoid) (Figure 3), but this is misleading. Certainly it is positive.

16. If it were a triangle, and the Demand curves were parallel, some geometry might reveal the shape, but we also don’t know the lines are parallel. In reality they surely aren’t. The high value of time folks (on the left) might be willing to pay a lot more for the improved quality than the low value of time folks on the right.

Ben Ross proposes to improve BCA and develop an adjustment factor to account for the differences in quality  between modes. He suggests we look at the number of minutes it takes to get a number of riders for each mode.

I have mathematized this. So Rq=Crail,q – Cbus,q, where R is the travel time difference at some number of riders q, and Cm,q is the travel time (cost) at which you would get q riders on mode m. 

To illustrate:

If 1,000 people ride the bus at 10 minutes and 1,000 people ride the train at 12 minutes, Ben proposes the extra pleasure (or lessened pain) of taking rail is equal in value to a time savings of two minutes.

At a given margin, this is probably approximately correct. That is, the  marginal (the 1,000th) train rider is willing to take (pay) 12 minutes 12 minutes while the 1,000th bus rider insists on 10 minutes.

The problem we are trying to construct an area (the benefit). There is no guarantee that R is constant.

  • The 2,000th rail rider might insist on 11 minutes, while the 2,000th bus rider requires 8 minutes. R2000= 11-8 =3 ≠ 12-10.
  • The 10,000th rail rider might be willing to pay 3 minutes, while the 10,000th bus rider requires -3 minutes (you have to pay them 3 minutes to ride the bus). R10000=3–3 = 6.

Now we could try to find the “average” value of R, or the value of R for the average rider.  So let’s say you have forecast 30,000 riders for a line, then you try to find R for the 15,000th rider, and apply it over the whole range.

(What travel time do you need to get only 15,000 bus riders and 15,000 rail riders, this will be much different than the actual travel time you are modeling, and it will be a higher travel time, so the model will require some adjustment to obtain this number).

This again assumes distance between the curves is fixed. Unlike the rule of 1/2, which is meant to be applied over a small area, so the curvature doesn’t really matter, the assumption here is this applies over the whole demand curve, where differences in curvature might be quite significant.

If we used the model to trace out the demand curves, we could then integrate (find the red area), but this is data that is not generally obtained or reported to the economist doing the BCA. The modeler could compute this of course if they wanted to, with a bunch of model runs, but the modeler could just use the log sum, and no one believes the model or in utility or understands log sums. So the economists takes the forecast in its reduced form, and treats the method for getting it as a black box (or magic).

So is the approximation R reasonable? Is using this value better than using the implied R of 0 which is currently done?

As Ben notes,

All we really have is our one Alternative Specific Constant. It’s tough enough to draw a single value of that constant out of the available data, we surely can’t measure its dependence on income, walkability, etc.  What we actually know is the size of the rail preference under the conditions where the data was collected that the constant was calibrated against, not under the conditions that the model is simulating.
The hard part is scaling from measurement conditions to project conditions, but there are only a few simple alternatives (per trip, per mile, per minute) so if you don’t know which is right you could show results for all of them (and accept that reality may be in between).

I don’t see how this is different from the money value of time.  Doesn’t it involve the same kind of approximation?  And an assumed method of scaling?  Measured under one set of conditions, used under different conditions.

 

I don’t think I would trust using the model to trace out the demand curves.  The delta we’re looking at is ultimately derived from that Alternative Specific Constant.
When you only have one measured data point, drawing curves inevitably pulls in assumptions that tend to get insufficient examination and can easily introduce subtle (or not-so-subtle) errors.  The only robust conclusions are the ones that you can connect directly to your measured data point.  In my opinion (derived mostly from other kinds of models, but very strongly held) the best way to proceed is to treat your measured data point as a constant, multiply it by the relevant parameters, and go straight to an answer.  Then adjust it for whatever important factors that you can point to and explain in words why your measurement didn’t account for them and why your correction is appropriate.
You can certainly compare the calculation to a black-box model that solves partial differential equations (or in the transportation case a giant matrix), but you shouldn’t believe any model results whose cause you can’t explain convincingly after you get it.  (yes, the model sometimes detects your erroneous intuition, but most of the time it’s the model that is wrong).

One Way to Deal With a Desire Line | streets.mn

Soon enough it will be Winter. Again a landscape covered with white powdery snow will reveal where travelers want to go. The first figure is an aerial shot of the former environment around the McNamara Alumni Center on the University of Minnesota campus. The second figure is in front of (behind) McNamara . Though there is a sidewalk just on the right of this image, pedestrians prefer the straight line path between the Scholars Walk and the diagonal path across Walnut from Beacon Street to the intersection of Oak Street and Washington Avenue. And why shouldn’t they? It’s cold outside. The extra few feet (extra few seconds) are not worth it, even for a cleared path.

In this Aerial photo via Google Maps you can see what the scene looked like before the recent "improvements". Pedestrians could walk diagonally across Walnut to the Scholars' Walk
In this Aerial photo via Google Maps you can see what the scene looked like before the recent “improvements”. Pedestrians could walk diagonally across Walnut to the Scholars Walk

The 2009 Campus Master Plan for the University of Minnesota is a very clear document regarding transportation. It prioritizes pedestrians, as is completely appropriate for a campus. There is nothing about “modal balance” or other nonsense. [I was involved with the development of transportation elements of the plan. I am also an employee of the University.]

Guideline 35 says:

Develop pedestrian connections that will:

  • Continue to share corridors with other modes of movement along streets or paths;
  • Enable pedestrians to take the most direct route between major destinations;
  • Prioritize pedestrian movement over other modes of travel whenever possible.

Guideline 57 says:

Design signature streets to accommodate all modes of
travel, with walking as the highest priority followed by bicycling, transit, and private vehicles.

So you would think when a desire line emerges, it would be considered for improvement since it is evidence of a direct route. Certainly you would think direct paths would be preserved rather than removed.

Desire line at McNamara Alumni Center
Desire line at McNamara Alumni Center

Sadly, this desire line used to be the regular sidewalk path until recent landscaping work done at the McNamara Alumni Center. But the people (well about 20% of the people based on my springtime count) could not be kept down by a mere four inches of concrete, they rebelled, in the typically passive-aggressive Minnesota way, by walking across the desire line rather than the rat run of the planner, especially in Winter when the curb is so conveniently hidden under snow, but even in summer, when there were dying plantings showing the ineffectiveness of the curb.

Still, I complained to campus facilities staff about the remodeling (1) making it a worse pedestrian condition, and (2) flying in the face of the campus master plan.

I am told this change was to slow down bicyclists coming from Washington Avenue to the Scholars Walk. I personally never noticed much of a bicyclist problem on the Scholars Walk, and there is Beacon Street right next door (and now Washington Avenue Mall a block away) so I doubt this will continue to be a significant problem. But perhaps a regent encountered a bicyclist.

I am also told that this was not a University of Minnesota, but a University of Minnesota Foundation decision. See the distinction? Me neither, and I work there. They share the umn.edu domain and the Foundation Board is in part appointed by the Regents. I am sure this is important for tax purposes or some such.

A tree! That's how we solve a desire line.
A tree! That’s how we solve a desire line.

Staff said they would try to get this fixed. In spring I even met onsite with a campus planner, who agreed there were better solutions. This summer there was to be work here (to fix some poor construction in the remodel I am told), so there was an opportunity to rectify the situation.

Thus I am surprised to see at the end of this past summer a tree planted where once there was a path, and later a desire line despite curbs aimed nominally at slowing bicyclists and actually just extending the trip of pedestrians (if not increasing the likelihood of their tripping). Now I like trees, but I don’t see them being planted in the middle of streets. So why is it planted where once there was a sidewalk?

Sidewalk at McNamara
Sidewalk at McNamara 1
Sidewalk at McNamara 2
Sidewalk at McNamara 2

Here we have a tree giving the figurative finger to pedestrians who want to take the most direct route between major destinations (like the Stadium Village Campus Connector Bus Stop on Oak Street and the East Bank of Campus, for instance) in direct contravention of the guidelines of the University’s officially adopted plans.

 

Footnotes:

1. If 1200 people  are each delayed three seconds, that is 1 person hour per day that is lost. I don’t know the pedestrian count, but that seems the right order of magnitude. (I know, this is America, and we don’t value the pedestrian’s time).

Desire Line at Nano Building
Just for Future Reference: Another Desire Line at Nano Building leading from the Rec Center

Bus stop amenities shorten wait | MnDaily

The article Bus stop amenities shorten wait by Jessie Baker appeared in the Minnesota Daily.

While some students say they’re content with the Twin Cities bus system, many commuters associate waiting time at stops with unhappiness and unpredictability, according to a recent University study. The research, which examined perceived waiting time at bus stations, will inform local transit authorities as they create and redesign bus stops.

Based on early statistical models, it appears that bus shelters help determine commuters’ perceived wait time when they’re actually waiting for five minutes or fewer, said Yingling Fan, a Humphrey School of Public Affairs associate professor and principal investigator for the study.

But for longer wait times, a bus schedule and a bench can help travelers feel like they aren’t waiting as long.

“The hope is that we can show that there is real value to amenities at stops and stations,” said David Levinson, a civil engineering associate [sic] professor and a co-investigator on the project.

Researchers expect to publish the paper in five months, after they collaborate with city, county and state sponsors about the results, Fan said.

Metro Transit has supported and discussed the project since it started in 2012, said John Levin, director of strategic
initiatives for the transportation authority.

“We are definitely very interested in the results of the study,” he said, adding that he feels those results will help Metro Transit design stops that lower perceived wait time in the future.

Metro Transit doesn’t yet know which amenities will stay and which will go, Levin said, but officials will pay attention to customer information, maps and other amenities highlighted in the study.

“If we understand what those factors are, that helps us understand where we should be paying attention in terms of designing those facilities,” he said.

To conduct the study, researchers surveyed people at Twin Cities bus stops to determine their perceived wait times while simultaneously recording their actual wait times using video cameras, Fan said.

The survey asked commuters questions like whether they used smartphones while waiting and how often they used transit, she said. Research assistants then collected the surveys and also photographed each participant holding the anonymous survey so they could match the replies to the video recording.

Levinson said he thought the study was designed well.

“Generally, people overestimate how much time they spend traveling on the trip, and this study corroborates that for transit,” he said.

Researchers also considered potential ties between perceived wait time and environmental factors like safety, an area’s walkability and aesthetics, Fan said.

For example, Fan said the study found that women tend to overestimate how long they wait at locations research assistants rated as unsafe.

“In the winter, it doesn’t seem like you’re waiting that long because you plan your trips around bus times,” she said.

Levinson said the study’s results will prove useful for Minneapolis public transit in the future.

“This is the beginning of a way to come up with a systematic way to evaluate these amenities,” he said.

Aside from demoting me, the article accurately represents the study, which will be available soon.