The Transportationist is on MPR’s Daily Circuit. Today 10 am Central.

I got invited back.

I will be having a live in-studio conversation with Kerri Miller on MPR’s Daily Circuit Today (Tuesday October 14 at 10 am Central Time). The other guest is  Adie Tomer of the Brookings Institute/Metropolitan Policy.

Topics are likely to include transportation issues in the midterm elections,  big ideas for transportation’s future, how those ideas get paid for, and who the constituents are in that conversation.

 

In the Oct. 9 gubernatorial debate, Gov. Mark Dayton and his two opponents named transportation on their list of priorities for the next four years.

Dayton proposed a gas tax increase to pay for his transportation plan, but how to pay for all that’s needed for roads and bridges is a problem that vexes politicians nationwide.

On The Daily Circuit, we talk with two transportation experts about the economics of transportation planning.

The Evolution of the Green Line: A Retrospective

Metro Transit’s Green Line opened in June, 2014. While ridership almost immediate beat “expectations1, and the line was quickly declared “a success“, at first there were still bugs in the works related to traffic signal timings and thus overall run-time and reliability, and safety.

Though the planners felt this line on the map was permanently drawn, a review of history reveals that first lines often change over time. Investments continued to be made, and technology advanced.0

What’s happened next?  This brief article summarizes the history of how the Green Line evolved from 2014 to the present.

 

1. Transit Priority

History records that St. Paul Public Works worked very hard to ensure a safe and convenient trip for all users of University Avenue. After five years of tweaking traffic signal timings (a set of light-bulbs that instructed human drivers when it was safe to proceed), there was effective transit signal priority on the Green Line, so the line hardly ever needed to stop at traffic signals, and only stops at stations. This shaved a couple of minutes off end-to-end run times.

2. Green waves

Transit priority  added delay to cross traffic, but better informing traffic (trains, cars on University, cars on cross-streets) the speed they needed to travel to achieve the Green Wave improved overall signal efficiency and throughput and minimize stopped delays and lost time. This made travelers happier and once implemented system-wide reduced total delay by 10 percent. (History records every traffic engineering improvement reduced delay by 10 percent. This is puzzling.)

 

3. Shared space

At first, the traffic control infrastructure on the segment of the Green Line on Washington Avenue through the University of Minnesota was incredibly over-built (or counter-productively safe as one article of the era put it). Eventually, after the great traffic signal outage of 2024 (due to a widespread worm infecting centralized traffic control centers) resulted in reduced crashes, engineers had the bright idea to un-build infrastructure in many places. This gave sufficient cover for local officials to operate the Green Line through campus as a streetcar on a pedestrian mall. De-signalizing Washington Avenue turned the corridor into a shared space. Some de-busing also place, as the Campus Connector (a bus) was fully replaced by the Green Line (and the buses were rerouted to other corridors).

In a shared space, engineers finally came to realize that attention is redirected away from the hypnotic light bulbs and towards the actual other travelers. As children we are advised “Look before you leap”.  It took a few months to re-educate adults they no longer need  pay attention to the (now removed) light bulb across the street, and instead use those same eyes looking for approaching bikes, buses, and trains. The general message was  “Look before you cross”, meaning “Look. When there is nothing going to run you over – Cross. When something is going to run you over – Don’t Cross.”

The icon was a pancake on the road, with a sad face, representing the food item a pedestrian would be as flat as, if he didn’t use his eyes and brain. The use of this icon was controversial, as being insensitive, and became a symbol in the culture wars about how we treat death, but the general view was the humor and controversy made people  more careful.

4. Minimizing Conflicts – No left turns

One of the most dangerous places on the LRT line was vehicles making left turns on University Avenue across the LRT path (this was sadly a universal problem with LRT in the median, not just the Green Line). Drivers were paying attention ahead to the signal and opposing-traffic, and to their right for cross-traffic, not to their left for a train emerging from behind. In one sense, this was of course the car driver’s fault, assuming they violated the traffic control devices instructions. In another sense, engineers need to better understand human factors and design for people not machines. As the saying of the time went “Safety is a shared responsibility” – though the intent of that was to guilt travelers into behaving well, rather than the system administrators into designing for humans.)

Further, left-turns, when protected, added an additional phase to the traffic cycle, with the concomitant lost time (all-red plus extra green start-up time), wasting capacity. There were many relatively low-volume left-turns on University that could be eliminated2. This would of course have increased travel times for those who wanted to make a left (or U-) turn. Alternative solutions of the era included making 3 right turns, or a right and 2 lefts, and the space-wasting jughandle. One cannot imagine the residents on Aurora or Sherburne (the streets immediately south and north of University in the residential areas of St. Paul) would have been pleased with this option, though for streets in industrial areas in the western part of St. Paul, such Myrtle and Charles, implementation would have  been easier.

The streets below crossed University between Prospect Park and Rice Street stations. The + indicates a station, the * indicates an off-90 degree angle. The (F) indicates associated freeway entrance/exit ramps to I-94.

  • 29th* + [Prospect Park]
  • Malcolm
  • Bedford
  • Berry + [Westgate]
  • Eustis (F)
  • Cromwell (F)
  • Franklin *
  • Raymond +
  • Hampden
  • Vandalia / Cretin (F)
  • Transfer / Cleveland
  • Prior
  • Fairview +
  • Aldine
  • Fry
  • Snelling + (F)
  • Pascal
  • Hamline +
  • Griggs
  • Lexington + (F)
  • Chatsworth
  • Victoria +
  • Grotto
  • Dale + (F)
  • Mackubin
  • Western +
  • Marion
  • Rice + (F)

Traffic engineers looked at the post-Green Line traffic counts and identified Left Turns that could be eliminated. The challenge was that eliminating cross streets and left-turns diminishes access, and created more of a wall-effect than the Green Line already produces. It is unlikely this could have been done everywhere, but that doesn’t mean it couldn’t have been done anywhere. In the event, only Berry Street saw Left Turns and Through Movements eliminated in 2028. Soon after, the widespread deployment of automated vehicles mooted most of the vehicle-train interaction safety issues.

Other controls upon drivers, such as rail gates, were also proposed in the late 2010s and early 2020s as the number of vehicle-train crashes and fatalities remained persistently high, but they were not implemented before drivers were made obsolete.

 

5. Grade separations/transfer stations with arterial BRT and/or streetcars

 

As longer distance through roads with freeway interchanges, Rice, Dale, Lexington, and Snelling all had very significant levels of cross-traffic for several decades. This cross-traffic was delayed by the Green Line. The Green Line was delayed by the additional green time given to the cross-traffic (or slowed in the case of Always Green Traffic Control).

Urban diamond interchanges, with the cross streets running under University and the Green Line, allowed the following connections University WB with Cross Street NB, Cross Street SB with University WB, University EB with Cross Street SB and Cross Street NB with University EB. Upstream or downstream U-turns / roundabouts (such as at Snelling at Spruce Tree or Snelling and Sherburne) would replace the left turns. The 1 block distance was sufficient for an acceptable grade for an underpass as described.

On the outside lanes of the underpasses officials constructed  transit stations, where there would be staircase and elevator connections to the median of University Avenue and the associated Green Line Station, so transfer passengers from the Snelling Avenue A-Line BRT,  Lexington Avenue P-Line, Dale Street Zed-Line, and Rice Street Streetcar wouldn’t need to cross streets. These were planned starting in 2017 after the unexpected success of BRT, but not completed until 2034.

 

 

6. Higher frequencies

While the trains were not usually full for the first several decades (At 6 trains an hour, each with a capacity of 600 persons, in each direction, the capacity was some 3600 persons per hour in each direction, the demand was typically half that in the daily peak hour, though reached that levels for particular trains, especially if the headways got long do to bunching, and also  around events).

The constraint on higher frequencies was limited capacity in downtown Minneapolis where the Green and Blue lines share track. At 10 minute headways on each line, there was a train crossing in one direction or the other every 2.5 minutes. At 5 minutes, there would have been a train every 1.25 minutes, which would not allow much time (if any) for north-south cross traffic. The two solutions proposed for this were splitting the Green and Blue lines in downtown, so they follow different streets, or  grade separation. This was expensive (which is why it took so long to build ), but once Minneapolis decided to become an actual big city, it was constructed in 2039, the year the Minnesota Multi-Purpose Stadium was demolished (about five years after the National Football League went bankrupt from lawsuits and cancelled television contracts). A set of public playing fields replaced the site for another 30 years. Most recently, a Cyborgian Battle-Bots arena is being constructed on the site for the Minnesota Bot-Kings. Critics suggest that it is inhumane to breed and build cyborgs simply as fighting machines, and that they should be given full liberties. That has yet to happen, though a referendum is on the weekly demo-ballot later in the year.

 

7. A/B Service

In 2044 A/B service was established in peak hours. In A/B service,  trains skipped every other station (skip-stop service), so the A train would stop at Prospect Park, Raymond, Snelling, Lexington, Dale, and Rice, and the B-train would stop at Westgate, Fairview, Hamline, Victoria, and Western.  (While at first people were skeptical because of load imbalances, the additional capacities helped increase development on the “B-train” stops.

This could not have been implemented until higher frequencies are achieved (thanks to the improvements in downtown Minneapolis) without defeating the entire purpose of the added infill stations in St. Paul. With higher frequencies, A/B service ensured 10 minute headways on all stations, and 5 minutes at selected stations. Express trains, which were also discussed on the Green Line could not work without passing tracks, or the trains would bunch up as express trains approached locals. Once trains were automated (in 2064, long after cars were automated, because, well because this is transit), then the two tracks could be more efficiently utilized to allow passing.

 

8. Service Extensions 

The Southwest Green Line extension was opened in 2023, several years behind schedule due to lawsuits and tunnel cave-ins. Demand was less than expected at most suburban stations for many years. The 2047 rezoning in Minneapolis  allowed the area around the Chain of Lakes to explode with new high rise development, and the Kenilworth station became the busiest on the Line.

The Blue Line extension to the Northwest suburbs opened in 2022. The nature of the routes allowed interlining, so eventually half the Green Line trains went to Edina Prairie (the merged cities of Eden Prairie and Edina) and half to Brooklyn (the merged cities of Brooklyn Center and Brooklyn Park). City mergers were encouraged by state government as part of the municipal consolidation movement to reduce the number of mayors the Governor had to meet with every year. Similarly every other Blue Line train went to each city. The routes were informally dubbed Green Prairie and Green Lyn, and Blue Prairie and Blue Lyn.

A service extension to the East, across the Kellogg Bridge (rebuilt in 2031 for a second time), extended the Green Line to the Metropolitan State University campus, and then down East 7th street for a few miles.

In 2030, a spur, from Stadium Village stations, along the Campus Transitway, connected the Falcon Heights campus of the University of Minnesota (UM-FH) and the State Fair Grounds through Energy Park, to the Green Line, and on to Downtown Minneapolis, running every 10 minutes. This was dubbed the Maroon Line (the name Gold Line having already been used up on a Freeway BRT in the east Metro.) The University secretly acquired the St. Paul Ports Authority (which had been privatized in 2023), and redeveloped many sites, especially at the Raymond Avenue station of the Maroon Line.

9. Express trains

In 2049, the new Minnesota Magnetic Levitation Commission began running fast, high-frequency express trains between downtown Minneapolis and downtown St. Paul, on a right-of-way other than the Green Line. This created point-to-point travel times on the order of 15 minutes between the downtowns, instead of 40-50 minutes that the Green Line afforded.

This intersected the Green Line at the railroad tracks between Transfer and Prior (the once (and future) Amtrak station), behind a the ruins of a chain store called Menard’s. This site created  a natural transfer station, especially as that area was redeveloped from industrial to the new development dubbed “Transfer Town” was built simultaneously (in fact, the value capture from Transfer Town helped fund the short MagLev line. (There were other stations at the University of Minnesota-Dinkytown Campus (UM-DT) and at Snelling Avenue, as well as termini adjacent to existing stations  in downtown Minneapolis (The Grand Central Interchange by Target) and St. Paul (The Union Depot by Dunkin’).

While there was probably little  significant time advantage of the MagLev over high-frequency Bus-on-Shoulder or Bus on MnPass lanes on the I-94 corridor, there was so much extra rail right-of-way capacity in the region that this was proposed along with radial lines, as part of a Minnesota MagLev passenger network. MagLev was selected as the need for new right-of-way and infrastructure was made apparent after the rail passenger hostage crisis of 2029, when Burlington Northern Railway held up 200 passengers on three different trains of the Northstar line so a line of high-fructose corn syrup tank cars could pass, which sadly led to a derailment and toxic waste emergency, also known as the Great Corn Sugar Flood.    Ultimately it made sense to build the line with the highest demand (between Minneapolis and St. Paul) first, before the spokes.

 

10. The Green Line is disbanded.

In 2074, sixty years after it first opened, the tracks of the Green Line were ripped up, and the last train cars sent to the Hennepin County Nano-bot Materials Recovery Center for smelting and recycling into a nutritional supplement for Cyborgian Battle-Bots dubbed Soylent Green. Remember Soylent Green is trains.

Plans now call for the abandoned right of way itself to be turned into a high-speed bike way (the University Greenway and downtown VeloTunnel). Bicycling has soared in popularity since climate control was implemented globally a few years back, ensuring urban winter days would be above freezing, and summer days well below human body temperature.

The parallel road, University Avenue, has been replaced with grass, and is used for low elevation robotic hover-jitneys (Ro-Ho-Ji), which have proved much more popular than the Green Line in recent years. These can be summoned simply by yelling the phrase (Ro-Ho-Ji) into the air, the network of microphones deployed across the metropolitan area will quickly identify your location via triangulation and recognize your voice, allowing for quick dispatch of the Ro-Ho-Ji and automatic billing. Drunkards continually yelling Ro-Ho-Ji late on Thursday nights to celebrate the weekend remains a problem, but voice recognition identifies them and special vehicles (Ro-Ho-Ji-Al) for the inebriated scoop them up for insta-sobering.

The “Please Check Schedules” electronic message signs at stations, that never worked properly, and became iconic on tee-shirts and coffee mugs, just like the London slogan “Mind the Gap”, finally showed the correct schedule on the final day of operation. Internal documents posted on WikiLeaks after the closure, revealed that Metro Transit, and later the Green Line Corporation (which operated the line its last 30 years), were able to make the signs work after the first year, but kept them broken to appeal to tourists.

At the closing of the Green Line,  a ribbon was symbolically sown back together.

 

Footnotes:


0.
Nothing was ever finished except to the politicians. Politicians were elected officials in the representative governments of the era who ran for re-election every 4 years. Recall these were eliminated with the Sester-centennial Direct Democracy Constitution Convention of 2026. Politicians interacted with public works when they had their photo (a static two-dimensional image, often black and white) snapped at the ribbon cutting for a new line  every 10 years. The photo was important  the “above the fold” headline in the newspaper (a pile of printed paper left outside the home of older residents, mostly filled with advertisements for stores (places where people would purchase goods) as well as a record of the events of the previous day).

1.
“Expectations” are technically “calculations”. I hesitate to say “forecasts”, since people in the field didn’t actually believe them. These calculations for the Green Line complied with then relatively conservative FTA standards, which tightened in response to studies in the 1990s and that found forecasts were severely optimistic. Compare 1980s forecasts for Hiawatha LRT with later forecasts.  The Alternatives Analysis predicted ridership of 37,000, the Final EIS lowered this number to 24,800. See Table 7 http://www.fta.dot.gov/documents/NSPA2007_Final(1).pdf . These new methods led to somewhat more accurate forecasts … the incentive then became to have the lowest forecast that would still have gotten your project funded. Since the controls on forecasting methods reduced the incentive to lie in order to get funding, if you could beat forecasts after opening, and you could immediately declare success, that helped with the next project. Higher forecasts arewerestill useful for justifying additional spending of course, but spending limits were constrained politically.

 

2.
Berry Street, I am looking at you.

Cross-posted at streets.mn.

Transit 2014: Accessibility to Jobs in Atlanta

Access Across America: Transit 2014 Coverage

Regularly updated

As keen readers of this blog or my twitter feed know, the Accessibility Observatory released Access Across America: Transit 2014  this week, with an official University of Minnesota Press Release and  Maps. This post links to third party coverage and interpretation of the report.

National

Local

Austin

Cincinnati

Denver

Houston

Los Angeles

Louisville

Minneapolis – St. Paul

Phoenix

Portland

San Antonio

Seattle

Tampa

Washington DC

Transit Accessibility in Minneapolis

Access Across America: Transit 2014 … #accessjobstransit

Our Access Across America: Transit 2014 report is now out.

Access Across America : Transit 2014
Access Across America : Transit 2014

The report (CTS 14-11) and methodology  (CTS 14-12) can be downloaded from the Accessibility Observatory.

Report

Accessibility is the ease of reaching valued destinations. It can be measured for various transportation modes, to different types of destinations, and at different times of day. There are a variety of ways to define accessibility, but the number of destinations reachable within a given travel time is the most comprehensible and transparent, as well as the most directly comparable across cities.

This report examines accessibility to jobs by transit in 46 of the 50 largest (by population) metropolitan areas in the United States. Transit is used for an estimated 5 percent of commuting trips in the United States, making it the second most widely used commute mode after driving. This report complements Access Across America: Auto 2013, a report of job accessibility by auto in 51 metropolitan areas. …

Rankings are determined by a weighted average of accessibility, giving a higher weight to closer jobs. Jobs reachable within ten minutes are weighted most heavily, and jobs are given decreasing weight as travel time increases up to 60 minutes.

Methodology:

This report describes the data and methodology used in the separate publication, Access Across America: Transit 2014. That report examines accessibility to jobs by transit in 46 of the 50 largest (by population) metropolitan areas in the United States. Transit is used for an estimated 5 percent of commuting trips in the United States, making it the second most widely used commute mode after driving. Rankings are determined by a weighted average of accessibility, giving a higher weight to closer jobs. Jobs reachable within ten minutes are weighted most heavily, and jobs are given decreasing weight as travel time increases up to 60 minutes.

The research was sponsored by the Center for Transportation Studies at the University of Minnesota. Accessibility Observatory reports, including the analysis of job accessibility by auto published last year Access Across America: Auto 2013, and interactive maps are available for download at: access.umn.edu/research/america.

Visit the site to see the reports, rankings, data, and maps.

Transit Accessibility in Minneapolis
Transit Accessibility in Minneapolis region. Ranked #13 as of January 2014.

Then and Now: University of Minnesota | streets.mn

 

Streetcar on Washington Avenue in front of Coffman Union c. 1953
Streetcar on Washington Avenue in front of Coffman Union c. 1953
A photo taken from the second floor of Coffman Union on September 26, 2014.
A photo taken from the second floor of Coffman Union on September 26, 2014.

These images show very little change on the main Mall of the University of Minnesota campus. Coffman still looks down at Northrup in the distance. There is a tree in the middle, so the landscape is slightly less formal. Some new buildings have been built at the edge of the Mall and Washington. Washington is deeper entrenched.

But lots of more important changes have occurred. After about 60 years without rail transit on Washington Avenue, it came back in 2014. Private cars no longer drive on Washington – which is now a mall. The wooden bridges across Washington are now metallic. Sheltered bus stops grace Washington.

But the unseen scene shows lots of changes.

Students dress more casually. The ethnic composition of students is very different. I am taking a picture with a camera in a mobile telephone, which every person in this scene probably has. The truck in the foreground is due to the bookstore sidewalk sale. The transit is publicly owned. There are “Health Partners” which manage your health instead of private doctors.  The world is now in color.

Cross-posted at streets.mn.

The Road to Vision Zero Has Some Bumps In It | NY City Lens

Elena Boffetta writes in NY City Lens The Road to Vision Zero Has Some Bumps In It

Speed humps are proposed in Sunnyside. I note there are alternatives.

David Levinson, a professor in the Department of Civil Engineering at the University of Minnesota, said speed humps are not the most efficient way to slow down traffic, as drivers get used to them and tend to speed after passing one, or just avoid them by using alternate routes.

Levinson said speed humps are only one part of a measure called traffic calming, which is a change in the infrastructure and environment of the roads to slow down traffic and make the streets safer for bikers and pedestrians. He said there are other more effective forms of traffic calming.

“Other solutions would be putting trees on the side of the road, changing the pavement material, putting on-street parking,” Levinson said. “A very good one is to narrow the streets intersections. If the intersection is narrow the sidewalk is extended and there is a change in the environment, so cars need to go slower because they are driving through a narrower region.”

He said speed humps also create difficulties for fire trucks, garbage removal vehicles, and snowplows. He said one solution to lower speeds and fewer accidents in residential areas would be to follow the woonerf movement in use in the Netherlands, a system of “living streets” where pedestrians and cyclists have legal priority over motorists.

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).

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