The Ridership Forecasts for the Baltimore-Washington SCMAGLEV Are Far Too High

The United States desperately needs better public transit.  While the lockdowns made necessary by the spread of the virus that causes Covid-19 led to sharp declines in transit use in 2020, with (so far) only a partial recovery, there will remain a need for transit to provide decent basic service in our metropolitan regions.  Lower-income workers are especially dependent on public transit, and many of them are, as we now see, the “essential workers” that society needs to function.  The Washington-Baltimore region is no exception.

Yet rather than focus on the basic nuts and bolts of ensuring quality services on our subways, buses, and trains, the State of Maryland is once again enamored with using the scarce resources available for public transit to build rail lines through our public parkland in order to serve a small elite.  The Purple Line light rail line was such a case.  Its dual rail lines will serve a narrow 16-mile corridor, passing through some of the richest zip codes in the nation, but destroying precious urban parkland.  As was discussed in an earlier post on this blog, with what will be spent on the Purple Line one could instead stop charging fares on the county-run bus services in the entirety of the two counties the Purple Line will pass through (Montgomery and Prince George’s), and at the same time double those bus services (i.e. double the lines, or double the service frequency, or some combination).

The administration of Governor Hogan of Maryland nonetheless pushed the Purple Line through, although construction has now been halted for close to a year due to cost overruns leading the primary construction contractor to withdraw.  Hogan’s administration is now promoting the building of a superconducting, magnetically-levitating, train (SCMAGLEV) between downtown Baltimore and downtown Washington, DC, with a stop at BWI Airport.  Over $35 million has already been spent, with a massive Draft Environmental Impact Statement (DEIS) produced.  As required by federal law, the DEIS has been made available for public comment, with comments due by May 24.

It is inevitable that such a project will lead to major, and permanent, environmental damage.  The SCMAGLEV would travel partially in tunnels underground, but also on elevated pylons parallel to the Baltimore-Washington Parkway (administered by the National Park Service).  The photos at the top of this post show what it would look like at one section of the parkway.  The question that needs to be addressed is whether any benefits will outweigh the costs (both environmental and other costs), and ridership is central to this.  If ridership is likely to be well less than that forecast, the whole case for the project collapses.  It will not cover its operating and maintenance costs, much less pay back even a portion of what will be spent to build it (up to $17 billion according to the DEIS, but likely to be far more based on experience with similar projects).  Nor would the purported economic benefits then follow.

I have copied below comments I submitted on the DEIS forecasts.  Readers may find them of interest as this project illustrates once again that despite millions of dollars being spent, the consulting firms producing such analyses can get some very basic things wrong.  The issue I focus on for the proposed SCMAGLEV is the ridership forecasts.  The SCMAGLEV project sponsors forecast that the SCMAGLEV will carry 24.9 million riders (one-way trips) in 2045.  The SCMAGLEV will require just 15 minutes to travel between downtown Baltimore and downtown Washington (with a stop at BWI), and is expected to charge a fare of $120 (roundtrip) on average and up to $160 at peak hours.  As one can already see from the fares, at best it would serve a narrow elite.

But there is already a high-speed train providing premier-level service between Baltimore and Washington – the Acela service of Amtrak.  It takes somewhat longer – 30 minutes currently – but its fare is also somewhat lower at $104 for a roundtrip, plus it operates from more convenient stations in Baltimore and Washington.  Importantly, it operates now, and we thus have a sound basis for forecasts of what its ridership might be in the future.

One can thus compare the forecast ridership on the proposed SCMAGLEV to the forecast for Acela ridership (also in the DEIS) in a scenario of no SCMAGLEV.  One would expect the forecasts to be broadly comparable.  One could allow that perhaps it might be somewhat higher on the SCMAGLEV, but probably less than twice as high and certainly less than three times as high.  But one can calculate from figures in the DEIS that the forecast SCMAGLEV ridership in 2045 would be 133 times higher than what they forecast Acela ridership would be in that year (in a scenario of no SCMAGLEV).  For those going just between downtown Baltimore and downtown Washington (i.e. excluding BWI travelers), the forecast SCMAGLEV ridership would be 154 times higher than what it would be on the comparable Acela.  This is absurd.

And it gets worse.  For reasons that are not clear, the base year figures for Acela ridership in the Baltimore-Washington market are more than eight times higher in the DEIS than figures that Amtrak itself has produced.  It is possible that the SCMAGLEV analysts included Acela riders who have boarded north of Baltimore (such as in Philadelphia or New York) and then traveled through to DC (or from DC would pass through Baltimore to ultimate destinations further north).  But such travelers should not be included, as the relevant travelers who might take the SCMAGLEV would only be those whose trips begin in either Baltimore or in Washington and end in the other metropolitan area.  The project sponsors have made no secret that they hope eventually to build a SCMAGLEV line the full distance between Washington and New York, but that would at a minimum be in the distant future.  It is not a source of riders included in their forecasts for a Baltimore to Washington SCMAGLEV.

The Amtrak forecasts of what it expects its Acela ridership would be, by market (including between Baltimore and Washington) and under various investment scenarios, come from its recent NEC FUTURE (for Northeast Corridor Future) study, for which it produced a Final Environmental Impact Statement.  Using Amtrak’s forecasts of what its Acela ridership would be in a scenario where major investments allowed the Acela to take just 20 minutes to go between Baltimore and Washington, the SCMAGLEV ridership forecasts were 727 times as high (in 2040).  That is complete nonsense.

My comment submitted on the DEIS, copied below, goes further into these results and discusses as well how the SCMAGLEV sponsors could have gotten their forecasts so absurdly wrong.  But the lesson here is that the consultants producing such forecasts are paid by project sponsors who wish to see the project built.  Thus they have little interest in even asking the question of why they have come up with an estimate that 24.9 million would take a SCMAGLEV in 2045 (requiring 15 minutes on the train itself to go between Baltimore and DC) while ridership on the Acela in that year (in a scenario where the Acela would require 5 minutes more, i.e. 20 minutes, and there is no SCMAGLEV) would be about just 34,000.

One saw similar issues with the Purple Line.  An examination of the ridership forecasts made for it found that in about half of the transit analysis zone pairs, the predicted ridership on all forms of public transit (buses, trains, and the Purple Line as well) was less than what they forecast it would be on the Purple Line only.  This is mathematically impossible.  And the fact that half were higher and half were lower suggests that the results they obtained were basically just random.  They also forecast that close to 20,000 would travel by the Purple Line into Bethesda each day but only about 10,000 would leave (which would lead to Bethesda’s population exploding, if true).  The source of this error was clear (they mixed up two formats for the trips – what is called the production/attraction format with origin/destination), but it mattered.  They concluded that the Purple Line had to be a rail line rather than a bus service in order to handle their predicted 20,000 riders each day on the segment to Bethesda.

It may not be surprising that private promoters of such projects would overlook such issues.  They may stand to gain (i.e. from the construction contracts, or from an increase in land values next to station sites), even though society as a whole loses.  Someone else (government) is paying.  But public officials in agencies such as the Maryland Department of Transportation should be looking at what is the best way to ensure quality and affordable transit services for the general public.  Problems develop once the officials see their role as promoters of some specific project.  They then seek to come up with a rationale to justify the project, and see their role as surmounting all the hurdles encountered along the way.  They are not asking whether this is the best use of scarce public resources to address our very real transit needs.

A high-speed magnetically-levitating train (with superconducting magnets, no less), may look attractive.  But officials should not assume such a shiny new toy will address our transit issues.

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May 22, 2021

Comment Submitted on the DEIS for SCMAGLEV

The Ridership Forecasts Are Far Too High

A.  Introduction

I am opposed to the construction of the proposed SCMAGLEV project between Baltimore and Washington, DC.  A key issue for any such system is whether ridership will be high enough to compensate for the environmental damage that is inevitable with such a project.  But the ridership forecasts presented in the DEIS are hugely flawed.  They are far too high and simply do not meet basic conditions of plausibility.  At more plausible ridership levels, the case for such a project collapses.  It will not cover its operating costs, much less pay back any of the investment (of up to $17 billion according to the DEIS, but based on experience likely to be far higher).  Nor will the purported positive economic benefits then follow.  But the damage to the environment will be permanent.

Specifically, there is rail service now between Baltimore and Washington, at three levels of service (the high-speed Acela service of Amtrak, the regular Amtrak Regional service, and MARC).  Ridership on the Acela service, as it is now and with what is expected with upgrades in future years, provides a benchmark that can be used.  While it could be argued that ridership on the proposed SCMAGLEV would be higher than ridership on the Acela trains, the question is how much higher.  I will discuss below in more detail the factors to take into account in making such a comparison, but briefly, the Acela service takes 30 minutes today to go between Baltimore and Washington, while the SCMAGLEV would take 15 minutes.  But given that it also takes time to get to the station and on the train, and then to the ultimate destination at the other end, the time savings would be well less than 50%.  The fare would also be higher on the SCMAGLEV (at an average, according to the DEIS, of $120 for a round-trip ticket but up to $160 at peak hours, versus an average of $104 on the Acela).  In addition, the stations the SCMAGLEV would use for travel between downtown Baltimore and downtown Washington are less conveniently located (with poorer connections to local transit) than the Acela uses.

Thus while it could be argued that the SCMAGLEV would attract more riders than the Acela, even this is not clear.  But being generous, one could allow that it might attract somewhat more riders.  The question is how many.  And this is where it becomes completely implausible.  Based on the ridership forecasts in the DEIS, for both the SCMAGLEV and for the Acela (in a scenario where the SCMAGLEV is not built), the SCMAGLEV in 2045 would carry 133 times what ridership would be on the Acela.  Excluding the BWI ridership on both, it would be 154 times higher.  There is no way to describe this other than that it is just nonsense.  And with other, likely more accurate, forecasts of what Acela ridership would be in the future (discussed below) the ratios become higher still.

Similarly, if the SCMAGLEV will be as attractive to MARC riders as the project sponsors forecast it will be, then most of those MARC riders would now be on the modestly less attractive Acela.  But they aren’t.  The Acela is 30 minutes faster than MARC (the SCMAGLEV would be 45 minutes faster), yet 28 times as many riders choose MARC over Acela between Baltimore and Washington.  I suspect the fare difference ($16 per day on MARC, vs. $104 on the Acela) plays an important role.  The model used could have been tested by calculating a forecast with their model of what Acela ridership would be under current conditions, with this then compared this to what the actual figures are.  Evidently this was not done.  Had they, their predicted Acela ridership would likely have been a high multiple of the actual and it would have been clear that their modeling framework has problems.

Why are the forecasts off by orders of magnitude?  Unfortunately, given what has been made available in the DEIS and with the accompanying papers on ridership, one cannot say for sure.  But from what has been made available, there are indications of where the modeling approach taken had issues.  I will discuss these below.

In the rest of this comment I will first discuss the use of Acela service and its ridership (both the actual now and as projected) as a basis for comparison to the ridership forecasts made for the SCMAGLEV.  They would be basically similar services, where a modest time saving on the SCMAGLEV (15 minutes now, but only 5 minutes in the future if further investments are made in the Acela service that would cut its Baltimore to DC time to just 20 minutes) is offset by a higher fare and less convenient station locations.  I will then discuss some reasons that might explain why the SCMAGLEV ridership forecasts are so hugely out-of-line with what plausible numbers might be.

B.  A Comparison of SCMAGLEV Ridership Forecasts to Those for Acela  

The DEIS provides ridership forecasts for the SCMAGLEV for both 2030 (several years after the DEIS says it would be opened, so ridership would then be stable after an initial ramping up) and for a horizon year of 2045.  I will focus here on the 2045 forecasts, and specifically on the alternative where the destination station in Baltimore is Camden Yards.  The DEIS also has forecasts for ridership in an alternative where the SCMAGLEV line would end in the less convenient Cherry Hill neighborhood of Baltimore, which is significantly further from downtown and with poorer connections to local transit options.  The Camden Yards station is more comparable to Penn Station – Baltimore, which the Acela (and Amtrak Regional trains and one of the MARC lines) use.  Penn Station – Baltimore has better local transit connections and would be more convenient for many potential riders, but this will of course depend on the particular circumstances of the rider – where he or she will be starting from and where their particular destination will be.  It will, in particular, be more convenient for riders coming from North and Northeast of Baltimore than Camden Yards would be.  And those from South and Southwest of Baltimore would be more likely to drive directly to the DC region than try to reach Camden Yards, or they would alight at BWI.

The DEIS also provides forecasts of what ridership would be on the existing train services between Baltimore and Washington:  the Acela services (operated by Amtrak), the regular Amtrak Regional trains, and the MARC commuter service operated by the State of Maryland.  Note also that the 2045 forecasts for the train services are for both a scenario where the SCMAGLEV is not built and then what they forecast the reduced ridership would be with a SCMAGLEV option.  For the purposes here, what is of interest is the scenario with no SCMAGLEV.

The SCMAGLEV would provide a premium service, requiring 15 minutes to go between downtown Baltimore and downtown Washington, DC.  Acela also provides a premium service and currently takes 30 minutes, while the regular Amtrak Regional trains take 40 to 45 minutes and MARC service takes 60 minutes.  But the fares differ substantially.  Using the DEIS figures (with all prices and fares expressed in base year 2018 dollars), the SCMAGLEV would charge an average fare of $120 for a round-trip (Baltimore-Washington), and up to $160 for a roundtrip at peak times.  The Acela also has a high fare for its also premium service, although not as high as SCMAGLEV, charging an average of $104 for a roundtrip (using the DEIS figures).  But Amtrak Regional trains charge only $34 for a similar roundtrip, and MARC only $16.

Acela service thus provides a reasonable basis for comparison to what SCMAGLEV would provide, with the great advantage that we know now what Acela ridership has actually been.  This provides a firm base for a forecast of what Acela ridership would be in a future year in a scenario where the SCMAGLEV is not built.  And while the ridership on the two would not be exactly the same, one should expect them to be in the same ballpark.

But they are far from that:

  DEIS Forecasts of SCMAGLEV vs. Acela Ridership, Annual Trips in 2045

Route

SCMAGLEV Trips

Acela Trips

Ratio

Baltimore – DC only

19,277,578

125,226

154 times as much

All, including BWI

24,938,652

187,887

133 times as much

Sources:  DEIS, Main Report Table 4.2-3; and Table D-4-48 of Appendix D.4 of the DEIS

Using estimates just from the DEIS, the project sponsor is forecasting that annual (one-way) trips on the SCMAGLEV in 2045 would be 133 times what they would be in that year on the Acela (in a scenario where the SCMAGLEV is not built).  And it would be 154 times as much for the Baltimore – Washington riders only.  This is nonsense.  One could have a reasonable debate if the SCMAGLEV figures were twice as high, and maybe even if they were three times as high.  But it is absurd that they would be 133 or 154 times as high.

And it gets worse.  The figures above are all taken from the DEIS.  But the base year Acela ridership figures in the DEIS (Appendix D.4, Table D.4-45) differ substantially from figures Amtrak itself has produced in its recent NEC FUTURE study.  This review of future investment options in Northeast Corridor (Washington to Boston) Amtrak service was concluded in July 2017.  As part of this it provided forecasts of what future Acela ridership would be under various alternatives, including one (its Alternative 3) where Acela trains would be substantially upgraded and require just 20 minutes for the trip between downtown Baltimore and downtown Washington, DC.  This would be quite similar to what SCMAGLEV service would be.

But for reasons that are not clear, the base year figures for Acela ridership between Baltimore and Washington differ substantially between what the SCMAGLEV DEIS has and what NEC FUTURE has.  The figure in the NEC FUTURE study (for a base year of 2013) puts the number of riders (one-way) between Baltimore and Washington (and not counting those who boarded north of Baltimore, at Philadelphia or New York for example, and then rode through to Washington, and similarly for those going from Washington to Baltimore) at just 17,595.  The DEIS for the SCMAGLEV put the similar Acela ridership (for a base year of 2017) at 147,831 (calculated from Table D.4-45, of Appendix D.4).  While the base years differ (2013 vs. 2017), the disparity cannot be explained by that.  It is far too large.  My guess would be that the DEIS counted all Acela travelers taking up seats between Baltimore and Washington, including those who alighted north of Baltimore (or whose destination from Washington was north of Baltimore), and not just those travelers traveling solely between Washington and Baltimore.  But the SCMAGLEV will be serving only the Baltimore-Washington market, with no interconnections with the train routes coming from north of Baltimore.

What was the source of the Acela ridership figure in the DEIS of 147,831 in 2017?  That is not clear.  Table D.4-45 of Appendix D.4 says that its source is Table 3-10 of the “SCMAGLEV Final Ridership Report”, dated November 8, 2018.  But that report, which is available along with the other DEIS reports (with a direct link at https://bwmaglev.info/index.php/component/jdownloads/?task=download.send&id=71&catid=6&m=0&Itemid=101), does not have a Table 3-10.  Significant portions of that report were redacted, but in its Table of Contents no reference is shown to a Table 3-10 (even though other redacted tables, such as Tables 5-2 and 6-3, are still referenced in the Table of Contents, but labeled as redacted).

One can only speculate on why there is no Table 3-10 in the Final Ridership Report.  Perhaps it was deleted when someone discovered that the figures reported there, which were then later used as part of the database for the ridership forecast models, were grossly out of line with the Amtrak figures.  The Amtrak figure for Acela ridership for Baltimore-Washington passengers of 17,595 (in 2013) is less than one-eighth of the figure on Acela ridership shown in the DEIS or 147,831 (in 2017).

It can be difficult for an outsider to know how many of those riding on the Acela between Washington and Baltimore are passengers going just between those two cities (as well as BWI).  Most of the passengers riding on that segment will be going on to (or coming from) cities further north.  One would need access to ticket sales data.  But it is reasonable to assume that Amtrak itself would know this, and therefore that the figures in the NEC FUTURE study would likely be accurate.  Furthermore, in the forecast horizon years, where Amtrak is trying to show what Acela (and other rail) ridership would grow to with alternative investment programs, it is reasonable to assume that Amtrak would provide relatively optimistic (i.e. higher) estimates, as higher estimates are more likely to convince Congress to provide the funding that would be required for such investments.

The Amtrak figures would in any case provide a suitable comparison to what SCMAGLEV’s future ridership might be.  The Amtrak forecasts are for 2040, so for the SCMAGLEV forecasts I interpolated to produce an estimate for 2040 assuming a constant rate of growth between the forecast SCMAGLEV ridership in 2030 and that for 2045.  Both the NEC FUTURE and SCMAGLEV figures include the stop at BWI.

    Forecasts of SCMAGLEV (DEIS) vs. Acela (NEC FUTURE) Ridership between Baltimore and Washington, Annual Trips in 2040 

Alternative

SCMAGLEV Trips

Acela Trips

Ratio

No Action

22,761,428

26,177

870 times as much

Alternative 1

22,761,428

26,779

850 times as much

Alternative 2

22,761,428

29,170

780 times as much

Alternative 3

22,761,428

31,291

727 times as much

Sources:  SCMAGLEV trips interpolated from figures on forecast ridership in 2030 and 2045 (Camden Yards) in Table 4.2-3 of DEIS.  Acela trips from NEC FUTURE Final EIS, Volume 2, Appendix B.08.

The Acela ridership figures are those estimated under various investment scenarios in the rail service in the Northeast Corridor.  NEC FUTURE examined a “No Action” scenario with just minimal investments, and then various alternative investment levels to produce increasingly capable services.  Alternative 3 (of which there were four sub-variants, but all addressing alternative investments between New York and Boston and thus not affecting directly the Washington-Baltimore route) would upgrade Acela service to the extent that it would go between Baltimore and Washington in just 20 minutes.  This would be very close to the 15 minutes for the SCMAGLEV.  Yet even with such a comparable service, the SCMAGLEV DEIS is forecasting that its service would carry 727 times as many riders as what Amtrak has forecast for its Acela service (in a scenario where there is no SCMAGLEV).  This is complete nonsense.

To be clear, I would stress again that the forecast future Acela ridership figures are a scenario under various possible investment programs by Amtrak.  The investment program in Alternative 3 would upgrade Acela service to a degree where the Baltimore – Washington trip (with a stop at BWI) would take just 20 minutes.  The NEC FUTURE study forecasts that in such a scenario the Baltimore-Washington ridership on Acela would total a bit over 31,000 trips in the year 2040.  In contrast, the DEIS for the SCMAGLEV forecasts that there would in that year be close to 23 million trips taken on the similar SCMAGLEV service, requiring 15 minutes to make such a trip.  Such a disparity makes no sense.

C.  How Could the Forecasts be so Wrong?

A well-known consulting firm, Louis Berger, prepared the ridership forecasts, and their “Final Ridership Report” dated November 8, 2018, referenced above, provides an overview on the approach they took.  Unfortunately, while I appreciate that the project sponsor provided a link to this report along with the rest of the DEIS (I had asked for this, having seen references to it in the DEIS), the report that was posted had significant sections redacted.  Due to those redactions, and possibly also limitations in what the full report itself might have included (such as summaries of the underlying data), it is impossible to say for sure why the forecasts of SCMAGLEV ridership were close to three orders of magnitude greater than what ridership has been and is expected to be on comparable Acela service.

Thus I can only speculate.  But there are several indications of what may have led the SCMAGLEV estimates to be so out of line with ridership on a service that is at least broadly comparable.  Specifically:

1)  As noted above, there were apparent problems in assembling existing data on rail ridership for the Baltimore-Washington market, in particular for the Acela.  The ridership numbers for the Acela in the DEIS were more than eight times higher in their base year (2017) than what Amtrak had in an only slightly earlier base year (2013).  The ridership numbers on Amtrak Regional trains (for Baltimore-Washington riders) were closer but still substantially different:  409,671 in Table D.4-45 of the DEIS (for 2017), vs. 172,151 in NEC FUTURE (for 2013).

Table D.4-45 states that its source for this data on rail ridership is a Table 3-10 in the Final Ridership Report of November 8, 2018.  But as noted previously, such a table is not there – it was either never there or it was redacted.  Thus it is impossible to determine why their figures differ so much from those of Amtrak.  But the differences for the Acela figures (more than a factor of eight) are huge, i.e. close to an order of magnitude by itself.  While it is impossible to say for sure, my guess (as noted above) is that the Acela ridership numbers in the DEIS included travelers whose trip began, or would end, in destinations north of Baltimore, who then traveled through Baltimore on their way to, or from, Washington, DC.  But such travelers are not part of the market the SCMAGLEV would serve.

2)  In modeling the choice those traveling between Baltimore and Washington would have between SCMAGLEV and alternatives, the analysts collapsed all the train options (Acela, Amtrak Regional, and MARC) into one.  See page 61 of the Ridership Report.  They create a weighted average for a single “train” alternative, and they note that since (in their figures) MARC ridership makes up almost 90% of the rail market, the weighted averages for travel time and the fare will be essentially that of MARC.

Thus they never looked at Acela as an alternative, with a service level not far from that of SCMAGLEV.  Nor do they even consider the question of why so many MARC riders (67.5% of MARC riders in 2045 if the Camden Yards option is chosen – see page D-56 of Appendix D-4 of the DEIS) are forecast to divert to the SCMAGLEV, but are not doing so now (nor in the future) to Acela.  According to Table D-45 of Appendix D.4 of the DEIS, in their data for their 2017 base year, there are 28 times as many MARC riders as on Acela between downtown Baltimore and downtown Washington, and 20 times as many with those going to and from the BWI stop included.  Evidently, they do not find the Acela option attractive.  Why should they then find the SCMAGLEV train attractive?

3)  The answer as to why MARC riders have not chosen to ride on the Acela almost certainly has something to do with the difference in the fares.  A round-trip on MARC costs $16 a day.  A round trip on Acela costs, according to the DEIS, an average of $104 a day.  That is not a small difference.  For someone commuting 5 days a week and 50 weeks a year (or 250 days a year), the annual cost on MARC would be $4,000 but $26,000 a year on the Acela.  And it would be an even higher $30,000 a year on the SCMAGLEV (based on an average fare of $120 for a round trip), and $40,000 a year ($160 a day) at peak hours (which would cover the times commuters would normally use).  Even for those moderately well off, $40,000 a year for commuting would be a significant expense, and not an attractive alternative to MARC with its cost of just one-tenth of this.

If such costs were properly taken into account in the forecasting model, why did it nonetheless predict that most MARC riders would switch to the SCMAGLEV?  This is not fully clear as the model details were not presented in the redacted report, but note that the modelers assigned high dollar amounts for the time value of money ($31.00 to $46.50 for commuters and other non-business travel, and $50.60 to $75.80 for business travel – see page 53 of the Ridership Report).  However, even at such high values, the numbers do not appear to be consistent.  Taking a SCMAGLEV (15 minute trip) rather than MARC (60 minutes) would save 45 minutes each way or 1 1/2 hours a day.  Only at the very high end value of time for business travelers (of $75.80 per hour, or $113.70 for 1 1/2 hours) would this value of time offset the fare difference of $104 (using the average SCMAGLEV fare of $120 minus the MARC fare of $16).  And even that would not suffice for travelers at peak hours (with its SCMAGLEV fare of $160).

But there is also a more basic problem.  It is wrong to assume that travelers on MARC treat their 60 minutes on the train as all wasted time.  They can read, do some work, check their emails, get some sleep, or plan their day.  The presumption that they would pay amounts similar to what some might on average earn in an hour based on their annual salaries is simply incorrect.  And as noted above, if it were correct, then one would see many more riders on the Acela than one does (and similarly riders on the Amtrak Regional trains, that require about 40 minutes for the Washington to Baltimore trip, with an average fare of $34 for a round trip).

There is a similar issue for those who drive.  Those who drive do not place a value on the time spent in their cars equal to what they would earn in an hourly equivalent of their regular salary.  They may well want to avoid traffic jams, which are stressful and frustrating for other reasons, but numerous studies have found that a simple value-of-time calculation based on annual salaries does not explain why so many commuters choose to drive.

4)  Data for the forecasting model also came in part from two personal surveys.  One was an in-person survey of travelers encountered on MARC, at either the MARC BWI Station or onboard Penn Line trains, or at BWI airport.  The other was an online internet survey, where they unfortunately redacted out how they chose possible respondents.

But such surveys are unreliable, with answers that depend critically on how the questions are phrased.  The Final Ridership report does not include the questionnaire itself (most such reports would), so one cannot know what bias there might have been in how the questions were worded.  As an example (and admittedly an exaggerated example, to make the point) were the MARC riders simply asked whether they would prefer a much faster, 15 minute, trip?  Or were they asked whether they would pay an extra $104 per day ($144 at peak hours) to ride a service that would save them 45 minutes each way on the train?

But even such willingness to pay questions are notoriously unreliable.  An appropriate follow-up question to a MARC rider saying they would be willing to pay up to an extra $144 a day to ride a SCMAGLEV, would be why are they evidently not now riding the Acela (at an extra $88 a day) for a ride just 15 minutes longer than what it would be on the SCMAGLEV.

One therefore has to be careful in interpreting and using the results from such a survey in forecasting how travelers would behave.  If current choices (e.g. using the MARC rather than the Acela) do not reflect the responses provided, one should be concerned.

5)  Finally, the particular mathematical form used to model the choices the future travelers would make can make a big difference to the findings.  The Final Ridership Report briefly explains (page 53) that it used a multinomial logit model as the basis for its modeling.  Logit functions assign a continuous probability (starting from 0 and rising to 100%) of some event occurring.  In this model, the event is that a traveler going from one travel zone to another will choose to travel via the SCMAGLEV, or not.  The likelihood of choosing to travel via the SCMAGLEV will be depicted as an S-shaped function, starting at zero and then smoothly rising (following the S-shape) until it reaches 100%, depending on, among other factors, what the travel time savings might be.

The results that such a model will predict will depend critically, of course, on the particular parameters chosen.  But the heavily redacted Final Ridership Report does not show what those parameters were nor how they were chosen or possibly estimated, nor even the complete set of variables used in that function.  The report says little (in what remains after the redactions) beyond that they used that functional form.

A feature of such logit models is that while the choices are discrete (one either will ride the SCMAGLEV or will not), it allows for “fuzziness” around the turning points, that recognize that between individuals, even if they confront a similar combination of variables (a combination of cost, travel time, and other measured attributes), some will simply prefer to drive while some will prefer to take the train.  That is how people are.  But then, while a higher share might prefer to take a train (or the SCMAGLEV) when travel times fall (by close to 45 minutes with the SCMAGLEV when compared to their single “train” option that is 90% MARC, and by variable amounts for those who drive depending on the travel zone pairs), how much higher that share will be will depend on the parameters they selected for their logit.

With certain parameters, the responses can be sensitive to even small reductions in travel times, and the predicted resulting shifts then large.  But are those parameters reasonable?  As noted previously, a test would have been whether the model, with the parameters chosen, would have predicted accurately the number of riders actually observed on the Acela trains in the base year.  But it does not appear such a test was done.  At least no such results were reported to test whether the model was validated or not.

Thus there are a number of possible reasons why the forecast ridership on the SCMAGLEV differs so much from what one currently observes for ridership on the Acela, and from what one might reasonably expect Acela ridership to be in the future.  It is not possible to say whether these are indeed the reasons why the SCMAGLEV forecasts are so incredibly out of line with what one observes for the Acela.  There may be, and indeed likely are, other reasons as well.  But due to issues such as those outlined here, one can understand the possible factors behind SCMAGLEV ridership forecasts that deviate so markedly from plausibility.

D.  Conclusion

The ridership forecasts for the SCMAGLEV are vastly over-estimated.  Predicted ridership on the SCMAGLEV is a minimum of two, and up to three, orders of magnitude higher than what has been observed on, and can reasonably be forecast for, the Acela.  One should not be getting predicted ridership that is more than 100 times what one observes on a comparable, existing (and thus knowable), service.

With ridership on the proposed system far less than what the project sponsors have forecast, the case for building the SCMAGLEV collapses.  Operational and maintenance costs would not be covered, much less any possibility of paying back a portion of the billions of dollars spent to build it, nor will the purported economic benefits follow.

However, the harm to the environment will have been done.  Even if the system is then shut down (due to the forecast ridership never materializing), it will not be possible to reverse much of that environmental damage.

The US very much needs to improve its public transit.  It is far too difficult, with resulting harm both to the economy and to the population, to move around in the Baltimore-Washington region.  But fixing this will require a focus on the basic nuts and bolts of operating, maintaining, and investing in the transit systems we have, including the trains and buses.  This might not look as attractive as a magnetically levitating train, but will be of benefit.  And it will be of benefit to the general public – in particular to those who rely on public transit – and not just to a narrow elite that can afford $120 fares.  Money for public transit is scarce.  It should not be wasted on shiny new toys.

The Purple Line Ridership Forecasts Are Wrong: An Example of Why We Get Our Infrastructure Wrong

Executive Summary

There are several major problems with the forecast ridership figures for the Purple Line, a proposed 16-mile light rail line that would pass in a partial arc around Washington, DC, in suburban Maryland.  The forecasts, as presented and described in the “Travel Forecasts Results Technical Report” of the Final Environmental Impact Statement for the project, are in a number of cases simply impossible.

Problems include:

a)  Forecast ridership in 2040 between many of the Transit Analysis Zone pairs along the Purple Line corridor would be higher on the Purple Line itself than it would be for total transit ridership (which includes bus, Metrorail, and commuter rail ridership, in addition to ridership on the Purple Line) between these zones.  This is impossible. Such cases are not only numerous (found in more than half of the possible cases for zones within the corridor) but often very large (12 times as high in one case).  If the forecasts for total transit ridership are correct, then correcting for this, with Purple Line ridership some reasonable share of the totals, would lead to far lower figures for Purple Line ridership.

b)  Figures on forecast hours of user benefits (primarily forecast time savings from a rail line) in a scenario where the Purple Line is built as compared to one where it is not, are often implausibly high.  In two extreme cases, the figures indicate average user benefits per trip between two specific zones, should the Purple Line be built, of 9.7 hours and 11.5 hours.  These cannot be right; one could walk faster.  But other figures on overall user benefits are also high, leading to an overall average predicted benefit of 30 minutes per trip.  Even with adjustments to the pure time savings that assign a premium to rail service, this is far too high and overestimates benefits by at least a factor of two or even three.  The user benefit figures are important for two reasons:  1) An overestimate leads to a cost-effectiveness estimate (an estimate of the cost of the project per hour of user benefits) that will be far off;  and 2) The figures used for user benefits from taking the proposed rail line enter directly into the estimation of ridership on the rail line (as part of the choice on whether to take the rail line rather than some other transit option, or to drive).  If the user benefit figures are overstated, ridership will be less.  With the user benefit figures overstated by a large margin, ridership will be far less.

c)  Figures on ridership from station to station are clearly incorrect.  They indicate, for example, that far more riders would exit at the Bethesda station (an end point on the line) each day (19,800) than would board there (10,210).  This is impossible.  More significantly, the figures indicate system capacity must be sufficient to handle 21,400 riders each day on the busiest segment (on the segment leaving Silver Spring heading towards Bethesda).  Even if the overall ridership numbers were correct, the figure for ridership on this segment is clearly too high (and it is this number which leads to the far higher number of those exiting the system in Bethesda than would enter there each day).  The figure is important as the rail line has been designed to a capacity sufficient to carry such a load.  With the true number far lower, there is even less of a case for investing in an expensive rail option.  Upgraded bus services could provide the capacity needed, and at far lower cost.

There appear to be other problems as well.  But even just these three indicate there are major issues with these forecasts.  This may also explain why a number of independent observers have noted for some time that the Purple Line ridership forecasts look implausibly high.  The figure for Purple Line ridership in 2040 of 69,300 per day is three times the average daily ridership actually observed in 2012 on 31 light rail lines built in the US over the last three decades.  It would also be 58% higher on the Purple Line than on the highest amongst those 31.  Yet the Purple Line would pass solely through suburban neighborhoods, of generally medium to low density.  Most of these other light rail lines in the US serve travel to and from downtown areas.

The causes of these errors in the ridership forecasts for the Purple Line are not always clear.  But the issues suggest at a minimum that quality checks were insufficient.  And while the Purple Line is just one example, inadequate attention to such issues might explain in part why ridership forecasts for light rail lines have often proven to be substantially wrong.

 

A.  Introduction

The Purple Line is a proposed light rail line that would be built in Suburban Maryland, stretching in a partial arc from east of Washington, DC, to north of the city.  I have written several posts previously in this blog on the proposed project (see the posts here, here, here, and here) and have been highly critical of it.  It is an extremely expensive project (the total cost to be paid to the private concessionaire to build and then operate the line for 30 years will sum to $5.6 billion, and other costs borne directly by the state and/or local counties will add at least a further $600 million to this).  And the state’s own analyses of the project found that upgraded bus services (including any one of several bus rapid transit, or BRT, options) to provide the transit services that are indeed needed in the corridor, would be both cheaper and more cost-effective.  Such alternatives would also avoid the environmental damage that is inevitable with the construction of dual rail lines along the proposed route, including the destruction of 48 acres of forest cover, the filling in of important wetland areas, and the destruction of a linear urban park that has the most visited trail in the state.

The state’s rationale for building a rail line rather than providing upgraded bus services is that ridership will be so high that at some point in the future (beyond 2040) only rail service would be able to handle the load.  But many independent analysts have long questioned those ridership forecasts.  A study from 2015 found that the forecast ridership on the Purple Line would be three times as high as the ridership actually observed in 2012 on 31 light rail lines built in the US over the last three decades.  Furthermore, the forecast Purple Line ridership would be 58% higher than ridership actually observed on the highest line among those 31.  And with the Purple Line route passing through suburban areas of generally medium to low density, in contrast to routes to and from major downtown areas for most of those 31, many have concluded the Purple Line forecasts are simply not credible.

Why did the Purple Line figures come out so high?  The most complete description provided by the State of Maryland of the ridership forecasts are provided in the chapter titled “Travel Forecasts Results Technical Report”, which is part of Volume III of the Final Environmental Impact Statement (FEIS) for the Purple Line, dated August 2013 (which I will hereafter often refer to simply as the “FEIS Travel Forecasts chapter”).  A close examination of that material indicates several clear problems with the figures.  This post will discuss three, although there might well be more.

These three are:

a)  The FEIS forecast ridership for 2040 on the Purple Line alone would be higher (in a number of cases far higher) in most of the 49 possible combinations of travel between the 7 Transit Analysis Zones (TAZs) defined along the Purple Line route, than the total number of transit riders among those zones (by bus, Metrorail, commuter rail, and the Purple Line itself).  This is impossible.

b)  Figures on user benefits per Purple Line trip (primarily the time forecast to be saved by use of a rail line) are implausibly high.  In two cases they come to 9.7 hours and 11.5 hours, respectively, per trip.  This cannot be.  One could walk faster.  But these figures for minutes of user benefits per trip were then passed through in the computations to the total forecast hours of user benefits that would accrue as a consequence of building the Purple Line, thus grossly over-estimating the benefits. Such user benefit figures would also have been used in the estimation of how many will choose to ride the Purple Line.  If these user benefit figures are overestimated (sometimes hugely overestimated), then the Purple Line ridership forecasts will be overestimated.

c)  The figure presenting rail ridership by line segment from station to station (which then was used to determine what ridership capacity would be needed to service the proposed route) shows almost twice as many riders exiting at the Bethesda station (an end of the line) as would board there each day (19,800 arriving versus 10,210 leaving each day).  While there could be some small difference (i.e. some people might take transit to work in the morning, and then get a car ride home with a colleague in the evening), it could not be so large.  The figures would imply that Bethesda would be accumulating close to 9,600 new residents each day.  The forecast ridership by line segment (which is what determines these figures) is critical as it determines what the capacity will need to be of the transit system to service such a number of riders.  With these figures over-stated, the design capacity is too high, and there is even less of a rationale for building a rail line as opposed to simply upgrading bus services in the corridor.

These three issues are clear just from an examination of the numbers presented.  But as noted, there might well be more.  We cannot say for sure what all the errors might be as the FEIS Travel Forecasts chapter does not give a complete set of the numbers and assumed relationships needed as inputs to the analysis and then resulting from it, nor more than just a cursory explanation of how the results were arrived at.  But with anomalies such as these, and with no explanations for them, one cannot treat any of the results with confidence.

And while necessarily more speculative, I will also discuss some possible reasons for why the mistakes may have been made.  This matters less than the errors themselves, but might provide a sense for why they arose.  Broadly, while the FEIS Travel Forecasts chapter (and indeed the entire FEIS report) only shows the Maryland Transit Administration (MTA) as the source for the documents, the MTA has acknowledged (and as would be the norm) that major portions of the work – in particular the ridership forecasts – were undertaken or led by hired consulting firms.  The consulting firms use standard but large models to prepare such ridership forecasts, but such models must be used carefully to ensure reliable results.  It is likely that results were generated by what might have been close to a “black box” to the user, that there were then less than sufficient quality checks to ensure the results were reasonable, and that the person assigned to write up the results (who may well have differed from the person generating the numbers) did not detect these anomalous results.

I will readily admit that this is speculation as to the possible underlying causes, and that I could be wrong on this.  But it might explain why figures were presented in the final report which were on their face impossible, with no explanation given.  In any case, what is most important is the problems themselves, regardless of the possible explanations on why they arose.

Each of the three issues will be taken up in turn.

B.  Forecast Ridership on the Purple Line Alone Would Be Higher in Many Cases than Total Transit Ridership

The first issue is that, according to the forecasts presented, there would be more riders on the Purple Line alone between many of the Transit Analysis Zones (TAZs) than the number of riders on all forms of transit.  This is impossible.

Forecast Ridership on All Transit Options in 2040:

Forecast Ridership on Purple Line Alone in 2040:

These two tables are screenshots of the upper left-hand corners of Table 16 and 22 from the FEIS Travel Forecasts chapter.  While they show the key numbers, I would recommend that the reader examine the full tables in the original FEIS Travel Forecasts chapter. Indeed, if your computer can handle it, it would be best to open the document twice in two separate browsers and then scroll down to the two tables to allow them to be compared side by side on your screen.

The tables show forecast ridership in 2040 on all forms of transit in the “Preferred Alternative” scenario where the Purple Line is built (Table 16), or for the sub-group of riders just on the Purple Line (Table 22).  And based on the total ridership figures presented at the bottoms of the full tables, the titles appear to be correct. That is, Table 16 forecasts that total transit ridership in the Washington metro region would be about 1.5 million trips per day in 2040, which is plausible (Table 13 says it was 1.1 million trips per day in 2005, which is consistent with WMATA bus and rail ridership, where WMATA accounts for 80 – 85% of total ridership in the region).  And Table 22 says the total number of trips per day on the Purple Line in 2040 would be 68,650, which is consistent (although still somewhat different from, with no explanation) with figures given elsewhere in the chapter on forecast total Purple Line trips per day in 2040 (of 69,330 in Table 24, for example, or 69,300 in Tables 25 and 26, with that small difference probably just rounding). So it does not appear that the tables were mislabeled, which was my first thought.

The full tables show the ridership between any two pairs of 22 defined Transit Analysis Zones (TAZs), in production/attraction format (which I will discuss below).  The 22 TAZs cover the entire Washington metro region, and are defined as relatively compact geographic zones along the Purple Line corridor and then progressively larger geographic areas as one goes further and further away.  They have seven TAZs defined along the Purple Line corridor itself (starting at the Bethesda zone and ending at the New Carrollton zone), but Northern Virginia has just two zones (where one, labeled “South”, also covers most of Southern Prince George’s County in Maryland).  See the map shown as Figure 4 on page 13 of the FEIS Travel Forecasts chapter for the full picture.  This aggregation to a manageable set of TAZs, with a focus on the Purple Line corridor itself, is reasonable.

The tables then show the forecast ridership between any two TAZ pairs.  For example, Table 16 says there will on average be 1,589 riders on all forms of transit each day in 2040 between Bethesda (TAZ 1, as a “producer” zone) and Silver Spring (TAZ 3, as an “attractor” zone).  But Table 22 says there will be 2,233 riders each day on average between these same two TAZs on the Purple Line alone.  This is impossible.  And there are many such impossibilities.  For the 49 possible pairs (7 x 7) for the 7 TAZs directly on the Purple Line corridor, more than half (29) have more riders on the Purple Line than on all forms of transit.  And for one pair, between Bethesda (TAZ 1) and New Carrollton (TAZ 7), the forecast is that there would be close to 12 times as many riders taking the Purple Line each day as would take all forms of public transit (which includes the Purple Line and more).

Furthermore, if one adds up all the transit ridership between these 49 possible pairs (where the totals are presented at the bottom of the tables; see the FEIS Travel Forecasts chapter), the total number of trips per day on all forms of transit sums to 29,890 among these 7 TAZs (Table 16), while the total for the Purple Line alone sums to 30,560 (Table 22).

How could such a mistake have been made?  One can only speculate, as the FEIS chapter had next to no description of the methods they followed.  One instead has to infer a good deal based on what was presented, in what sequence, and from what is commonly done in the profession to produce such forecasts.  This goes into fairly technical issues, and readers not interested in these details can skip directly to the next section below.  But it will likely be of interest at least to some, provides a short review of the modeling process commonly used to generate such ridership forecasts, and will be helpful to an understanding of the other two obvious errors in the forecasts discussed below.

To start, note that the tables say they are being presented in “production/attraction” format.  This is not the more intuitive “origin/destination” format that would have been more useful to show.  And I suspect that over 99% of readers have interpreted the figures as if they are showing travel between origin and destination pairs.  But that is not what is being shown.

The production/attraction format is an intermediate stage in the modeling process that is commonly used for such forecasts.  That modeling process is called the “four-step model”.  See this post from the Metropolitan Washington Council of Governments (MWCOG) for a non-technical short description, or this post for a more academic description.  The first step in the four-step model is to try to estimate (via a statistical regression process normally) how many trips will be “produced” in each TAZ by households and by businesses, based on their characteristics.  Trips to work, for example, will be “produced” by households at the TAZ where they live, and “attracted” by businesses at the TAZ where those businesses are located.  The number of trips so produced will be forecast based on some set of statistical regression equations (with parameters possibly taken from what might have been estimated for some other metro area, if the data does not exist here).  The number of trips per day by household will be some function of average household size in the TAZ, average household income, how many cars the households own, and other such factors.  Trips “attracted” by businesses in some TAZ will similarly be some function of how many people are employed by businesses in that TAZ, perhaps the nature of the businesses, and so on.  Businesses will also “produce” their own trips, for example for delivery of goods to other businesses, and statistical estimates will be made also for such trips.

Such estimates are unfortunately quite rough (statistical error is high), and the totals calculated for the region as a whole of the number of trips “produced” and the number of trips “attracted” will always be somewhat different, and often far different.  But by definition the totals have to be the same, as all trips involve going from somewhere to somewhere. Hence some scaling process will commonly be used to equate the totals.

This will then yield the total number of trips produced in each TAZ, and the total number attracted to each TAZ.  But this does not tell us yet the distribution of the trips.  That is, one will have the total number of trips produced in TAZ 1, say, but not how many go from TAZ 1 to TAZ 2 or to TAZ 3 or to TAZ 4, and so on.  For this, forecasters generally assume the travel patterns will fit what is called a “gravity model”, where it is assumed the trips from each TAZ will be distributed to the “attractor” TAZs in some statistical relationship which is higher depending on the “mass” (i.e. the number of jobs in some TAZ) and lower depending on the distance between them (typically measured in terms of travel times). This is also rough, and some iterative rescaling process will be needed to ensure the trips produced in each TAZ and attracted to each TAZ sum to the already determined totals for each.

This all seems crude, and it is.  Many might ask why not determine such trip distributions from a straightforward survey of households asking where they travel to.  Surveys are indeed important, and help inform what the parameters of these functions might be, but one must recognize that any practicable survey could not suffice.  The 22 TAZs defined for the Purple Line analysis were constructed (it appears; see below) from a more detailed set of TAZs defined by the Metropolitan Washington Council of Governments.  But MWCOG now identifies 3,722 separate TAZs for the Washington metro region, and travel between them would potentially involve 13.9 million possible pairs (3,722 squared)!  No survey could cover that.  Hence MWCOG had to use some form of a gravity model to allocate the trips from each zone to each zone, and that is indeed precisely what they say they did.

At this point in the process, one will have the total number of trips produced by each TAZ going to each TAZ as an attractor, which for 2040 appears as Table 8 in the FEIS chapter. This covers trips by all options, including driving.  The next step is to separate the total number of trips between those taken by car from those taken by transit, and then, at the level below, the separation of those taken by transit into each of the various transit options (e.g. Metrorail, bus, commuter rail, and the Purple Line in the scenario where it is built). This is the mode choice issue, and note that these are discrete choices where one chooses one or the other.  (A combined option such as taking a bus to a Metrorail station and then taking the train would be modeled as a separate mode choice.)  This separation into various travel modes is normally then done by what is called a nested logit (or logistic) regression model, where the choice is assumed to be a function of variables such as travel time required, out of pocket costs (such as for fares or tolls or parking), personal income, and so on.

Up to this stage, the modeling work as described above would have been carried out by MWCOG as part of its regular work program (although in the scenario of no Purple Line). Appendix A of the FEIS Travel Forecasts chapter, says specifically that the modelers producing the Purple Line ridership forecasts started from the MWCOG model results (Round 8.0 of that model for the FEIS forecasts).  By aggregating from the TAZs used by MWCOG (3,722 currently, but possibly some different number in the Round 8.0 version), to the 22 defined for the Purple Line work, the team doing the FEIS forecasts would have been able to arrive at the table showing total daily trips by all forms of transportation (including driving) between the 22 TAZs (Table 8 of the FEIS chapter), as well as the total trips by some form of transit between the 22 in the base case of no Purple Line being built (the “No Build” alternative; Table 14 of the FEIS chapter).

The next step was then to model how many total transit trips would be taken in the case where the Purple Line has been built and is operating in 2040, as well as how many of such transit trips will be taken on the Purple Line specifically.  The team producing the FEIS forecasts would likely have taken the nested logit model produced by MWCOG, and then adjusted it to incorporate the addition of the Purple Line travel option, with consequent changes in the TAZ to TAZ travel times and costs.  At the top level they then would have modeled the split in travel between by car or by any form of transit, and at the next level then modeled the split of any form of transit between the various transit options (bus, Metrorail, commuter rail, and the Purple Line itself).

This then would have led to the figures shown in Table 16 of the FEIS chapter for total transit trips each day by any transit mode (with the Purple Line built), and Table 22 for trips on the Purple Line only.  Portions of those tables are shown above.  They are still in “production/attraction” format, as noted in their headings.

While understandable as a step in the process by which such ridership forecasts are generated (as just described), trips among TAZs in production/attraction format are not terribly interesting in themselves.  They really should have gone one further step, which would have been to convert from a production/attraction format to an origin/destination format.  The fact that they did not is telling.

As discussed above, a production/attraction format will show the number of trips between each production TAZ and each attraction TAZ.  Thus a regular commute for a worker from home (production TAZ) to work (attraction TAZ) each day will appear as two trips each day between the production TAZ and the attraction TAZ.  Thus, for example, the 1,589 trips shown as total transit trips (Table 16) between TAZ 1 (Bethesda) and TAZ 3 (Silver Spring) includes not only the trips by a commuter from Bethesda to Silver Spring in the morning, but also the return trip from Silver Spring to Bethesda in the evening.  The return trip does not appear in this production/attraction format in the 4,379 trips from Silver Spring (TAZ 3) to Bethesda (TAZ 1) element of the matrix (see the portion of Table 16 shown above).  The latter is the forecast of the number of trips each day between Silver Spring as a production zone and Bethesda as an attractor.

This is easy to confuse, and I suspect that most readers seeing these tables are so confused.  What interests the reader is not this production/attraction format of the trips, which is just an intermediate stage in the modeling process, but rather the final stage showing trips from each origin TAZ to each destination TAZ.  And it only requires simple arithmetic to generate that, if one has the underlying information from the models on how many trips were produced from home to go to work or to shop or for some other purpose (where people will always then return home each day), and separately how many were produced by what they call in the profession “non-home based” activities (such as trips during the workday from business to business).

I strongly suspect that the standard software used for such models would have generated such trip distributions in origin/destination format, but they are never presented in the FEIS Travel Forecasts chapter.  Had they been, one would have seen what the forecast travel would have been between each of the TAZ pairs in each of the two possible directions. One would probably have observed an approximate (but not necessarily exact) symmetry in the matrix, as travel from one TAZ to another in one direction will mostly (but not necessarily fully) be matched by a similar flow in the reverse direction, when added up over the course of a day.  For that reason also, the row totals will match or almost match each of the column totals.  But that will not be the case in the production/attraction format.

That the person writing up the results for this FEIS chapter did not understand that an origin/destination presentation of the travel would have been of far greater interest to most readers than the production/attraction format is telling, I suspect.  They did not see the significance.  Rather, what was written up was mostly simply a restatement of some of the key numbers from the tables, with little to no attempt to explain why they were what they were.  It is perhaps then not surprising that the author did not notice the impossibility of the forecast ridership between many of the TAZ pairs being higher on the Purple Line alone (Table 22) than the total ridership on all transit options together (Table 16).

C.  User Benefits and Time Savings

The modeling exercise also produced a forecast of “user benefits” in the target year. These benefits are measured in units of time (minutes or hours) and arise primarily from the forecast savings in the time required for a trip, where estimates are made as to how much less time will be required for a trip if one has built the light rail line.  I would note that there are questions as to whether there would in fact be any time savings at all (light rail lines are slow, particularly in designs where they travel on streets with other traffic, which will be the case here for much of the proposed route), but for the moment let’s look at what the modelers evidently assumed.

“User benefits” then include a time-value equivalent of any out-of-pocket cost savings (to the extent any exists; it will be minor here for most), plus a subjective premium for what is judged to be the superior quality of a ride on a rail car rather than a regular bus. The figures in the AA/DEIS (see Table 6-2 in Chapter 6) indicate a premium of 19% was added in the case of the medium light rail alternative – the alternative that evolved into what is now the Purple Line.  The FEIS Travel Forecasts chapter does not indicate what premium they now included, but presumably it was similar.  User benefits are thus largely time savings, with some markup to reflect a subjective premium.

Forecast user benefits are important for two reasons.  One is that it is such benefits which are, to the extent they in fact exist, the primary driver of predicted ridership on the Purple Line, i.e. travelers switching to the Purple Line from other transit options (as well as from driving, although the forecast shifts out of driving were relatively small).  Second, the forecast user benefits are also important as they provide the primary metric used to estimate the benefit of building the Purple Line. Thus if the inputs used to indicate what the time savings would be by riding the Purple Line as opposed to some other option were over-estimated, one will be both over-estimating ridership on the line and over-estimating the benefits.

And it does appear that those time savings and user benefits were over-estimated.  Table 23 of the FEIS chapter presents what it labels the “Minutes of User Benefits per Project Trip”.  A screenshot of the upper left corner, focussed on the travel within the 7 TAZs through which the Purple Line would pass, is:

Note that while the author of the chapter never says what was actually done, it appears that Table 23 was calculated implicitly by dividing the figures in Table 21 of the FEIS Travel Forecasts chapter (showing calculated total hours of time savings daily for each TAZ pair) by those in Table 22 (showing the number of daily trips on the Purple Line, the same table as was discussed in the section above).  This would have been a reasonable approach, given that the time savings figures include that saved by all the forecast shifts among transit alternatives (as well as from driving) should the new rail line be built.  The Table 23 numbers thus show the overall time saved across all travel modes, per Purple Line trip.

But the figures are implausible.  Taking the most extreme cases first, the table says that there would be an average of 582 minutes of user benefits per trip for travel on the Purple line between Bethesda (TAZ 1) and Riverdale Park (TAZ 6), and 691 minutes per trip between Bethesda (TAZ 1) and New Carrollton (TAZ 7).  This works out to user benefits per trip of 9.7 hours and 11.5 hours respectively!  One could walk faster!  And this does not even take into account that travel between Bethesda and New Carrollton would be faster on Metrorail (assuming the system is still functioning in 2040).  The FEIS Travel Forecasts chapter itself, in its Table 6, shows that Metrorail between these two stations currently requires 55 minutes.  That time should remain unchanged in the future, assuming Metrorail continues to operate.  But traveling via the Purple Line would require 63 minutes (Table 11) for the same trip.  There would in fact be no time savings at all, but rather a time cost, if there were any riders between those two points.

Perhaps some of these individual cases were coding errors of some sort.  I cannot think of anything else which would have led to such results.  But even if one sets such individual cases aside, I find it impossible to understand how any of these user benefit figures could have followed from building a rail line.  They are all too large.  For example, the FEIS chapter provides in its Table 18 a detailed calculation of how much time would be saved by taking a bus (under the No Build alternative specifically) versus taking the proposed Purple Line.  Including average wait times, walking times, and transfers (when necessary), it found a savings of 11.4 minutes for a trip from Silver Spring (TAZ 3) to Bethesda (TAZ 1); 2.6 minutes for a trip from Bethesda (TAZ 1) to Glenmont (TAZ 9); and 8.0 minutes for a trip from North DC (TAZ 15) to Bethesda (TAZ 1).  Yet the minutes of user benefits per trip for these three examples from Table 23 (see the full table in the FEIS chapter) were 25 minutes, 19 minutes, and 25 minutes, respectively.  Even with a substantial premium for the rail options, I do not see how one could have arrived at such estimates.

And the figures matter.  The overall average minutes of user benefits per project trip (shown at the bottom of Table 23 in the FEIS chapter) came to 30 minutes.  If this were a more plausible average of 10 minutes, say, then with all else equal, the cost-effectiveness ratio would be three times worse.  This is not a small difference.

Importantly, the assumed figures on time savings will also matter to the estimates made of the total ridership on the Purple Line.  The forecast number of daily riders in 2040 of 68,650 (Table 22) or 69,300 (in other places in the FEIS chapter) was estimated based on inputs of travel times required by each of the various modes, and from this how much time would be saved by taking the Purple Line rather than some other option.  With implausibly large figures for travel time savings being fed in, the ridership forecasts will be too high.  If the time savings figures being fed in are far too large, the ridership forecasts will be far too high.  This is not a minor matter.

D.  Ridership by Line Segment

An important estimate is of how many riders there will be between any two station to station line segments, as that will determine what the system capacity will need to be.  Rail lines are inflexible, and completely so when, as would be the case here, the trains would be operated in full from one end of the line to the other.  The rider capacity (size) of the train cars and the spacing between each train (the headway) will then be set to accommodate what is needed to service ridership on what would be the most crowded segment.

Figure 10 of the FEIS Travel Forecasts chapter provides what would be a highly important and useful chart of ridership on each line segment, showing, it says, how many riders would (in terms of the daily average) arrive at each station, how many of those riders would get off at that station, and then how many riders would board at that station.  That would then produce the figure for how many riders will be on board traveling to the next station.  And one needs to work this out for going in each direction on the line.

Here is a portion of that figure, showing the upper left-hand corner:

Focussing on Bethesda (one end of the proposed line), the chart indicates 10,210 riders would board at Bethesda each day, while 19,800 riders would exit each day from arriving trains.  But how could that be?  While there might be a few riders who might take the Purple Line in one direction to go to work or for shopping or for whatever purpose, and then take an alternative transportation option to return home, that number is small, and would to some extent balance out by riders going in the opposite direction.  Setting this small possible number aside, the figures in the chart imply that close to twice as many riders will be exiting in Bethesda as will be entering.  They imply that Bethesda would be seeing its population grow by almost 9,600 people per day.  This is not possible.

But what happened is clear.  The tables immediately preceding this figure in the FEIS Travel Forecasts chapter (Tables 24 and 25) purport to show for each of the 21 stations on the proposed rail line, what the daily station boardings will be, with a column labeled “Total On” at each station and a column labeled “Total Off”.  Thus for Bethesda, the table indicates 10,210 riders will be getting on, while 19,800 will be getting off.  While for most of the stations, the riders getting on at that station could be taking the rail line in either direction (and those getting off could be arriving from either direction), for the two stations at the ends of the line (Bethesda, and at the other end New Carrollton) they can only go in one direction.

But as an asterisk for the “Total On” and “Total Off” column headings explicitly indicates, the figures in these two columns of Table 24 are in production/attraction format.  That is, they indicate that Bethesda will be “producing” (mostly from its households) a forecast total of 10,210 riders each day, and will be “attracting” (mostly from its businesses) 19,800 riders each day.  But as discussed above, one must not confuse the production/attraction presentation of the figures, with ridership according to origin/destination.  A household where a worker will be commuting each day to his or her office will be shown, in the production/attraction format, as two trips each day from the production TAZ going to the attraction TAZ.  They will not be shown as one trip in each direction, as they would have been had the figures been converted to an origin/destination presentation.  The person that generated the Figure 10 numbers confused this.

This was a simple and obvious error, but an important one.  Because of this mistake, the figures shown in Figure 10 for ridership between each of the station stops are completely wrong.  This is also important because ridership forecasts by line segment, such as what Figure 10 was supposed to show, are needed in order to determine system capacity.  The calculations depicted in the chart conclude that peak ridership in the line would be 21,400 each day on the segment heading west from the Woodside / 16th Street station (still part of Silver Spring) towards Lyttonsville.  Hence the train car sizes and the train frequency would need to be, according to these figures (but incorrectly), adequate to carry 21,400 riders each day. That is their forecast of ridership on the busiest segment.  The text of the chapter notes this specifically as well (see page 56).

That figure is critically important because the primary argument given by the State of Maryland for choosing a rail line rather than one of the less expensive as well as more cost-effective bus options, is that ridership will be so high at some point (not yet in 2040, but at some uncertain date not too long thereafter) that buses would be physically incapable of handling the load.  This all depends on whether the 21,400 figure for the maximum segment load in 2040 has any validity.  But it is clearly far too high; it leads to almost twice as many riders going into Bethesda as leave.  It was based on confusing ridership in a production/attraction format with ridership by origin/destination.

Correcting for this would lead to a far lower maximum load, even assuming the rest of the ridership forecasts were correct.  And at a far lower maximum load, there is even less of a case against investing in a far less expensive, as well as more cost-effective, system of upgraded bus services for the corridor.

E.  Other Issues

There are numerous other issues in the FEIS Travel Forecasts chapter which leads one to question how carefully the work was done.  One oddity, as an example and perhaps not important in itself, is that Tables 17 and 19, while titled differently, are large matrices where all the numbers contained therein are identical.  Table 17 is titled “Difference in Daily Transit Trips (2040 Preferred Alternative minus No Build Alternative) (Production/Attraction Format)”, while Table 19 is titled “New Transit Trips with the Preferred Alternative (Production/Attraction Format)”.  That the figures are all identical is not surprising – the titles suggest they should be the same.  But why show them twice?  And why, in the text discussing the tables (pp. 41-42), does the author treat them as if they were two different tables, showing different things?

But more importantly, there are a large number of inconsistencies in key figures between different parts of the chapter.  Examples include:

a)  New transit trips in 2040:  Table 17 (as well as 19) has that there would be 19,700 new transit trips daily in the Washington region in 2040, if the Purple Line is built (relative to the No Build alternative).  But on page 62, the text says the number would be 16,330 new transit trips in 2040 if it is built.  And Table B-1 on page 67 says there would be 28,626 new transit trips in 2040 (again relative to No Build).  Which is correct?  One is 75% higher than another, which is not a small difference.

b)  Total transit trips in 2040:  Table 16 says that there would be a total of 1,470,620 total transit trips in the Washington region in 2040 if the Purple Line is built, but Table B-1 on page 67 puts the figure at 1,683,700, a difference of over 213,000.

c)  Average travel time savings:  Table 23 indicates that average minutes of “user benefits” per project trip would be 30 minutes in 2040 if the Purple Line is built, but the text on page 62 says that average travel time savings would “range between 14 and 18 minutes per project trip”.  This might be explained if they assigned a 100% premium to the time savings for riding a rail line, but if so, such an assumed premium would be huge.  As noted above, the premium assigned in the AA/DEIS for the Medium Light Rail alternative (which was the alternative later chosen for the Purple Line) was just 19%.  And the 14 to 18 minutes figure for average time savings per trip itself looks too large. The simple average of the three representative examples worked out in Table 18 of the chapter was just 7.3 minutes.

d)  Total user benefit hours per day in 2040:  The text on page 62 says that the total user benefit hours per day in 2040 would sum to 17,175.  But Table B-5 says the total would come to 24,073 hours (shown as 1,444,403 minutes, and then divided by 60), while Table 21 gives a figure of 33,960 hours.  The highest figure is almost double the lowest.  Note the 33,960 hours figure is also shown in Table 20, but then shows this as 203,760 minutes (but should be 2,037,600 minutes – they multiplied by 6, not 60, for the conversion of hours to minutes).

There are other inconsistencies as well.  Perhaps some can be explained.  But they suggest that inadequate attention was paid to ensure accuracy.

F.  Conclusion

There are major problems with the forecasts of ridership on the proposed Purple Line.  The discussion above examined several of the more obvious ones.  There may well be more. Little explanation was provided in the documentation on how the forecasts were made and on the intermediate steps, so one cannot work through precisely what was done to see if all is reasonable and internally consistent.  Rather, the FEIS Travel Forecasts chapter largely presented just the final outcomes, with little description of why the numbers turned out to be what they were presented to be.

But the problems that are clear even with the limited information provided indicate that the correct Purple Line ridership forecasts would likely be well less than what their exercise produced.  Specifically:

a)  Since the Purple Line share of total transit use can never be greater than 100% (and will in general be far less), a proper division of transit ridership between the Purple Line and other transit modes will result in a figure that is well less than the 30,560 forecast for Purple Line ridership for trips wholly within the Purple Line corridor alone (shown in Table 22).  The corridor covers seven geographic zones which, as defined, stretch often from the Beltway to the DC line (or even into DC), and from Bethesda to New Carrollton.  There is a good deal of transit ridership within and between those zones, which include four Metrorail lines with a number of stations on each, plus numerous bus routes.  Based on the historical estimates for transit ridership (for 2005), the forecasts for total transit ridership in 2040 within and between those zones look reasonable.  The problem, rather, is with the specific Purple Line figures, with figures that are often higher (often far higher) than the figures for total transit use.  This is impossible.  Rather, one would expect Purple Line ridership to be some relatively small share (no more than a quarter or so, and probably well less than that) of all transit users in those zones.  Thus the Purple Line ridership forecasts, if properly done, would have been far lower than what was presented.  And while one cannot say what the precise figure would have been, it is a mathematical certainty that it cannot account for more than 100% of total transit use within and between those zones.

b)  The figures on user benefits per trip (Table 23) appear to be generally high (an overall average of 30 minutes) and sometimes ridiculously high (9.7 hours and 11.5 hours per trip in two cases).  At more plausible figures for time savings, Purple Line ridership would be far less.

c)  Even with total Purple Line ridership at the official forecast level (69,300), there will not be a concentration in ridership on the busiest segment of 21,400 (Figure 10).  The 21,400 figure was derived based on an obvious error – from a confusion in the meaning of the production/attraction format.  Furthermore, as just noted above, correcting for other obvious errors imply that total Purple Line ridership will also be far less than the 69,300 figure forecast, and hence the station to station loads will be far less.  The design capacity required to carry transit users in this corridor can therefore be far less than what these FEIS forecasts said it would need to be.  There is no need for a rail line.

These impossibilities, as well as inconsistencies in the figures cited at different points in the chapter for several of the key results, all suggest insufficient checks in the process to ensure the forecasts were, at a minimum, plausible and internally consistent.  For this, or whatever, reason, forecasts that are on their face impossible were nonetheless accepted and used to justify building an expensive rail line in this corridor.

And while the examination here has only been of the Purple Line, I suspect that such issues often arise in other such transit projects, and indeed in many proposed public infrastructure projects in the US.  When agencies responsible for assessing whether the projects are justified instead see their mission as project advocates, a hard look may not be taken at analyses whose results support going ahead.

The consequence is that a substantial share of the scarce funds available for transit and other public infrastructure projects is wasted.  Expensive new projects get funded (although only a few, as money is limited), while boring simple projects, as well as the maintenance of existing transit systems, get short-changed, and we end up with a public infrastructure that is far from what we need.

More on the High Cost of the Purple Line: A Comparison to BRT on the Silver Spring to Bethesda Segment

Comparison of Purple Line to BRT Cost, Silver Spring to Bethesda

This is a quick post drawing on a report in today’s Washington Post on the implementation of bus rapid transit (BRT) in Montgomery County, Maryland.  The article notes that one of the early BRT routes planned in the county would run from Burtonsville to Silver Spring down US Highway 29, with an estimated capital cost of $200 million.

This would be a distance of 10.2 miles, so the cost would be $19.6 million per mile on average.  This BRT line is currently slated to stop in Silver Spring, but it would be straightforward to extend it along East-West Highway for a further 3.7 miles to Bethesda. Assuming the same average cost per mile, the capital cost of this addition would be $72 million.

The current plan is for the Purple Line light rail line to cover this same basic route, connecting Silver Spring to Bethesda.  As I have discussed in earlier blog posts, the Purple Line is incredibly expensive, even if one ignores (as the official cost estimates do) the environment costs of building and operating the line (including the value of parkland destroyed, which is implicitly being valued at zero, as well as the environmental costs from storm water run-off, habitat destruction, hazardous waste issues, higher greenhouse gas emissions, and more).  The current capital cost estimate, following the service and other cuts that Governor Hogan has imposed to bring down costs, is $2.25 billion.  This also does not include the costs that Montgomery County will cover directly for building the Bethesda station and well as the cost of a utilitarian path to be built adjacent to the train tracks.  The Purple Line would also cost more to operate per rider than the Montgomery County BRT routes are expected to cost, so there is no cost savings from lower operating costs.

The Purple Line would be 16.2 miles long in total.  Using just the $2.25 billion cost figure, this comes to $139 million per mile.  This is extremely high.  Indeed, the Columbia Pike streetcar line in Arlington County, which was recently cancelled due to its high cost, would have cost “only” $117 million per mile despite it being built through a high density urban corridor for most of its entire route.

The distance from Silver Spring to Bethesda on the Purple Line will be 4.4 miles if it is built. This is longer than the direct route by road since it will follow a more indirect path passing up and around the direct route.  Assuming the cost of this 4.4 miles is the same on average as for the rest of the Purple Line (it might be higher due to the need to build some major bridges, including over Rock Creek), the cost would come to $612 million.

The choice therefore is between spending $612 million to build this segment of the Purple Line from Silver Spring to Bethesda, or spending $72 million by extending the BRT.  The Purple Line cost is 8.5 times as much, and government could save $540 million ( = $612m – $72m) by terminating the Purple Line in Silver Spring and using BRT service instead.

As an earlier blog post argued, new thinking is necessary if we are to resolve the very real transportation issues we face in this region.  This is one more example of what could be done.  A half billion dollar savings is not small.