# Frequency-Ridership Spirals

I was reticent to post about this topic; I polled it on Patreon in December and it got just under 50% while the two topics I did blog, difficult urban geography and cross-platform transfers, got 64% and 50% respectively. However, between how close the vote was and the conversation about the current state of the subway in New York, I felt obligated to explain what’s been going on. The short version is that practically the entire change in subway ridership in New York over the last generation or two has come from the off-peak, and the way American cities set their frequency guidelines off-peak amplify small changes in demand, so that a minor setback can lead to collapse and a minor boost can lead to boom.

The good news is that by setting frequency to be high even if it does not look like ridership justifies it, cities can generate a virtuous cycle on the upswing and avoid a vicious one on the downswing. However, it requires the discipline to run good service even in bad times, when bean counters and budget cutters insist on retrenchment. The Chainsaw Al school of management looks appealing in recessions or when ridership is falling, and this is precisely when people who run transit agencies must resist the urge to cut frequency to levels that lead to a positive feedback loop wrecking the system.

Ridership-frequency elasticity

The key to the frequency-ridership spiral is that cutting frequency on transit makes it less useful to passengers, since door-to-door trip times are longer and less reliable. The size of this effect can be measured as the elasticity of ridership with respect to service: if increasing service provision by 1% is demonstrated to raise ridership by e%, we say that the elasticity is e.

Fortunately, this question is fundamental enough to transit that there is extensive published literature on the subject:

• In a classical TRB paper, Armando Lago, Patrick Mayworm, and Matthew McEnroe look at data from several American cities as well as one British one, disaggregating elasticity by frequency, mode (bus or commuter rail), and period (peak or off-peak). The aggregate average value is e = 0.44 for buses and e = 0.5 for commuter rail, but when frequency is better than every 10 minutes, e = 0.22 on average.
• Todd Litman of the advocacy organization VTPI has a summary mostly about fare elasticity but also service elasticity, suggesting e is in the 0.5-0.7 range in the short term and in the 0.7-1.1 range in the long term.
• A paper by Joe Totten and David Levinson includes its own lit review of several studies, including the two above, finding a range of 0.3 to 1.1 across a number of papers, with the lower figures associated with urban service and the higher ones with low-frequency suburban service. The paper’s own research, focusing on transit in Minneapolis, finds that on weekdays, e = 0.39.

One factor that I have unfortunately not seen in the papers I have read is trip length. Frequency is more important for short trips than long ones. This is significant, since when the headway is shorter relative to in-vehicle trip time we should expect lower elasticity with respect to the headway. Waiting 10 minutes rather than 5 minutes for an hour-long trip is not much of an imposition; waiting 30 minutes rather than 15 for the same trip is a greater imposition, as is waiting 10 minutes rather than 5 for a 20-minute trip.

In New York, the average unlinked subway trip is 13.5 minutes long, so the difference between 10 and 5 minutes is very large. Lago-Mayworm-McEnroe cite research saying passengers’ disutility for out-of-vehicle time is 2-3 times as large as for in-vehicle time; the MTA’s own ridership screen states that this penalty is 1.75, the MBTA’s states that it is 2.25, and a study by Coen Teulings, Ioulina Ossokina, and Henri de Groot says that it is 2 in the Netherlands. Figuring that this penalty is 2, the worst-case scenario for off-peak weekday wait time in New York, 10 minutes, has passengers spending more perceived time waiting for the train than riding it, and even in the average case, 10/2 = 5 minutes, it is close. In that case, higher values of e are defensible. Lago-Mayworm-McEnroe have less data about in-vehicle time elasticity and do not attempt to aggregate in- and out-of-vehicle time. But adding everything together is consistent with e = 0.8 relative to speed averaged over the total wait and in-vehicle time, and then e is maybe 0.4 relative to frequency.

The impact of service cuts

If the elasticity of ridership relative to frequency is 0.4, then cutting service by 1% means cutting ridership by 0.4%. If half the operating costs are covered by fares, then revenue drops by 0.2% of total operating expenses, so the 1% cut only saves 0.8% of the total subsidy. Achieving a 1% cut in operating costs net of fare revenue thus requires a 1.25% cut in service, which reduces ridership by 0.5%.

This may not sound too bad, but that’s because the above analysis does not incorporate fixed costs. Rail comes equipped with fixed costs for maintenance, station staffing, rolling stock, and administration, regardless of how much service the agency runs. Lisa Schweitzer uses this fact to defend Los Angeles’s MTA from my charge of high operating costs: she notes that Los Angeles runs much less service than my comparison cases in the US and Europe and thus average cost per train-km is higher even without undue inefficiency. In contrast, bus costs are dominated by driver wages, which are not fixed.

New York does not keep a headcount of transit employees in a searchable format – the Manhattan Institute’s See Through New York applet helps somewhat but is designed around shaming workers who make a lot of money through overtime rather than around figuring out how many people work (say) maintenance. But Chicago does, and we can use its numbers to estimate the fixed and variable costs of running the L.

The CTA has somewhat more than 10,000 workers, split fairly evenly between bus and rail. The rail workers include about 800 working for the director of maintenance, working on the rolling stock, which needs regular servicing and inspections regardless of how often it’s run; 550 working for facilities maintenance; (say) 400 out of 800 workers in administrative capacity like communications, general counsel, purchasing, and the chief engineer’s office; 600 workers in power and way maintenance; nearly 1,000 customer service agents; and 450 workers in flagging, switching, and the control towers. Only 500 workers drive trains, called rapid transit operators or extra board, and there may charitably be another 200 clerks, managers, and work train operators whose jobs can be cut if there is a service cut. A service cut would only affect 15% of the workers, maybe 20% if some rolling stock maintenance work can be cut.

In New York the corresponding percentage is somewhat higher than 15% since trains have conductors. Train operators and conductors together are about 13% of the NYCT headcount, so maybe 20% of subway employees, or 25% with some extra avoidable maintenance work.

What this means is that achieving a 2% cut in subsidy through reducing service requires a service cut of much more than 2%. If only 25% of workers are affected then, even without any frequency-ridership elasticity, the agency needs to cut service by 8% to cut operating costs by 2%.

The Uber effect

The combination of elasticity and fixed costs means that rail ridership responds wildly to small shocks to ridership. For a start, if the agency cuts service by 1%, then operating costs fall by 0.25%. Ridership falls by 0.4%, and thus revenue also falls by 0.4%, which is 0.2% of total operating costs. Thus operating costs net of revenue only fall by 0.05%. The only saving grace is that this is 0.05% of total operating costs; since by assumption fare revenue covers half of operating costs, this saves a full 0.1% of the public subsidy.

Read the above paragraph again: taking fixed costs and elasticity into account, cutting service by 1% only reduces the public subsidy to rail service by 0.1%. A 2% cut in subsidy in a recession requires a brutal 20% cut in service, cutting ridership by 8%. And this only works because New York overstaffs its trains by a factor of 2, so that it’s plausible that 25% of employees can be furloughed in a service cut; using Chicago numbers this proportion is at most 20%, in which case revenue falls one-to-one with operating costs and there is no way to reduce the public subsidy to rail operations through service cuts.

Of course, this has a positive side: a large increase in service only requires a modest increase in the public subsidy. Moreover, if trains have the operating costs of Chicago, which are near the low end in the developed world, then the combined impact of fixed costs and elasticity is such that the public subsidy to rapid transit does not depend on frequency, and thus the agency could costlessly increase service.

This is relevant to the Uber effect – namely, the research arguing that the introduction of ride-hailing apps, i.e. Uber and Lyft, reduces transit ridership. I was skeptical of Bruce Schaller’s study to that effect since it came out two years ago, since the observed reduction in transit ridership in New York in 2016 was a large multiple of the increase in total taxi and ride-hailing traffic once one concentrated on the off-peak and weekends, when the latter rose the most.

But if small shocks to ridership are magnified by the frequency-ridership spiral, then the discrepancy is accounted for. If a shock cuts ridership by 1%, which could be slower trains, service disruptions due to maintenance, or the Uber effect, then revenue falls 1% and the subsidy has to rise 1% to compensate. To cover the subsidy through service cuts requires a 10% cut in service, further cutting ridership by 4%.

Off-peak service guidelines

The above analysis is sobering enough. However, it assumes that service cuts and increases are uniformly distributed throughout the day. This is not the actual case for American transit agency practice, which is to concentrate both cuts and increases in the off-peak.

Unfortunately, cuts in off-peak service rather than at rush hour do not touch semi-fixed labor costs. The number of employees required to run service is governed by the peak, so running a lot of peak service without off-peak service leads to awkward shift scheduling and poor crew utilization. Higher ratios of peak to base frequency correlate with lower total service-hours per train driver: in addition to the examples I cite in a post from 2016, I have data for Berlin, where the U-Bahn’s peak-to-base ratio is close to 1, and there are 829 annual service-hours per driver.

I discussed the fact that the marginal cost of adding peak service is several times that of adding off-peak service in a post from last year. However, even if we take rolling stock acquisition as a given, perhaps funded by a separate capital plan, marginal crew costs are noticeably higher at the peak than off-peak.

In New York, the rule is that off-peak subway frequency is set so that at the most crowded point of each route, the average train will be filled to 125% seated capacity; before the round of service cuts in 2010 this was set at 100%, so the service cut amounted to reducing frequency by 20%. The only backstop to a vicious cycle is that the minimum frequency on weekdays is set at 10 minutes; on weekends I have heard both 10 and 12 minutes as the minimum, and late at night there is a uniform 20-minute frequency regardless of crowding.

Peak frequency is governed by peak crowding levels as well, but much higher crowding than 125% is permitted. However, the busiest lines are more crowded than the guidelines and run as frequently as there is capacity for more trains, so there is no feedback loop there between ridership and service.

The saving grace is that revenue is less sensitive to off-peak ridership, since passengers who get monthly passes for their rush hour trips ride for free off-peak. However, this factor requires there to be substantial enough season pass discounts so that even rush hour-only riders would use them. Berlin, where U-Bahn tickets cost €2.25 apiece in bundles of 4 and monthly passes cost €81, is such a city: 18 roundtrips per month are enough to justify a monthly. New York is not: with a pay-per-ride bonus a single ride costs \$2.62 whereas a 30-day pass costs \$121, so 23.1 roundtrips per month are required, so the breakeven point requires a roundtrip every weekday and every other weekend.

New York subway ridership evolution

The subway’s crisis in the 1970s reduced ridership to less than 1 billion, a level not seen since 1918. This was on the heels of a steady reduction in ridership over the 1950s and 60s, caused by suburbanization. In 1991, ridership was down to 930 million, but the subsequent increase in reliability and fall in crime led to a 24-year rally to a peak of 1,760 million in 2015.

Throughout this period, there was no increase in peak crowding. On the contrary. Look at the 1989 Hub Bound Report: total subway ridership entering Manhattan south of 60th Street between 7 and 10 am averaged about 1 million, down from 1.1 million in 1971 – and per the 2016 report, the 2015 peak was only 922,000. Between 1989 and 2015, NYCT actually opened a new route into Manhattan, connecting the 63rd Street Tunnel to the Queens Boulevard Line; moreover, a preexisting route, the Manhattan Bridge, had been reduced from four tracks to two in 1986 and went back to four tracks in 2004.

Nor was there much of an increase in mode share. The metropolitan statistical area’s transit mode share for work trips rose from 27% in 2000 to 30% in 2010. In the city proper it rose from 52% in 1990 to 57% in 2016. No: more than 100% of the increase in New York subway ridership between 1991 and 2015 was outside the peak commute hours, and nearly 100% of it involved non-work trips. These trips are especially affected by the frequency-ridership spiral, since frequency is lower then, and thus a mild positive shock coming from better maintenance, a lower crime rate, and perhaps other factors translated to a doubling in total ridership, and a tripling of off-peak ridership. Conversely, today, a very small negative shock is magnified to a minor crisis, even if ridership remains well above the levels of the 1990s.

The way out

Managers like peak trains. Peak trains are full, so there’s no perception of wasting service on people who don’t use it. Managers also like peak trains because they themselves are likelier to ride them: they work normal business hours, and are rich enough to afford cars. That current NYCT head Andy Byford does not own a car and uses the city’s transit network to get around scandalizes some of the longstanding senior managers, who don’t use their own system. Thus, the instinct of the typical manager is to save money by pinching pennies on off-peak service.

In contrast, the best practice is to run more service where possible. In Berlin, nearly all U-Bahn trains run every 5 minutes flat; a few lines get 4-minute peak service, and a few outer ends and branches only get half-service, a train every 10 minutes. At such high frequency, the frequency-ridership spiral is less relevant: an increase to a train every 4 minutes would require increasing service by 25%, raising costs by around 5% (Berlin’s one-person crews are comparable to Chicago’s, not New York’s), but not result in a significant increase in ridership as the shorter headway is such a minute proportion of total travel time. However, New York’s 10-minute off-peak frequency is so low that there is room to significantly increase ridership purely by running more service.

In 2015 I criticized the frequency guidelines in New York on the grounds of branching: a complexly branched system must run interlined services at the same frequency, even if one branch of a trunk line is somewhat busier than the other. However, the frequency-ridership spiral adds another reason to discard the current frequency guidelines. All branches in New York should run at worst every 6 minutes during the daytime, yielding 3-minute frequency on most trunks, and the schedules should be designed to avoid conflicts at junctions; non-branching trunk lines, that is the 1, 6, 7, and L trains, should run more frequently, ideally no more than every 4 minutes, the lower figure than in Berlin following from the fact that the 1 and 6 trains are both local and mostly serve short trips.

Moreover, the frequency should be fixed by a repeating schedule, which should be clockface at least on the A train, where the outer branches would only get 12-minute frequency. If ridership increases by a little, trains should be a little more crowded, and if it decreases by a little, they should be a little less crowded. Some revision of schedules based on demand may be warranted but only in the long run, never in the short run. Ideally the system should aim at 5-minute frequency on every route, but as the N, R, and W share tracks, this would require some deinterlining in order to move more service to Second Avenue.

This increase in frequency is not possible if politicians and senior managers respond to every problem by cutting service while dragging their feet about increasing service when ridership increases. It requires proactive leadership, interested in increasing public transit usage rather than in avoiding scandal. But the actual monetary expense required for such frequency is not large, since large increases in frequency, especially in the off-peak, mostly pay for themselves through extra ridership. The initial outlay required to turn the vicious cycle into a virtuous one is not large; all that is required is interest from the people in charge of American transit systems.

1. Herbert

Do you have data for the “S-Bahn crisis” that Berlin went through about a decade ago? It was in part caused by badly handled deferred maintenance and penny pinching….

By the way, if media and IGEB (the Berlin transit riders pressure group) are to be believed there is currently an U-Bahn crisis caused in part by lack of rolling stock – the F79 falling apart “ahead of schedule” exacerbating a long low simmering crisis to disaster levels…

Another interesting factor is “rail bonus” – studies show that replacing the same bus service with a tram increases ridership. Maybe this can cause another of the frequency ridership speaks you describe…. So what used to be a bus every ten minutes first goes to a tram every ten minutes but later to a tram every five minutes…

2. electricangel

Well, every few months you create a post that is absolutely path-setting in importance, Alon. Congratulations on actually using the numbers. Maybe we can pull out of the onrushing death spiral.

3. orulz

Fantastic stuff. Lately between this, your coverage of operating and construction costs, and the “fish rots from the head” article it seems like you are dealing lightning bolts with both hand. It seems you have the attention of the whole industry and I hope I am right about that. Keep up the good work.

Oh, and give Japan a visit sometime. Would love to see you integrate some on the ground experience there with your insights.

4. Stephen Bauman

“practically the entire change in subway ridership in New York over the last generation or two has come from the off-peak, ”

The yearly Hub Bound Reports show that peak hour (8-9) and peak period (7-10) have continued to decline, while daily use has increased. The 1963 report shows peak hour to be around 600K, whereas 2016 report shows peak hour was around 400K. Peak hour trains are crowded. How did the Transit Authority manage to carry 50% more passengers during the peak hour in 1963, than they do today?

• Alon Levy

The trains were more crowded then. Unfortunately the data definitions have changed, so I don’t know whether the ~3.6 ft^2 per passenger figure from the mid-1980s, or about 3 passengers per m^2, is comparable to total standees per standing area today, which on average is 2.75/m^2, or total passengers per car area, which is a lot lower since seated passengers get more space. I’m told crowding levels were even higher in the 1970s, and presumably they were also higher in the 1960s. And don’t forget, peak frequency was higher then – in the 1960s lines routinely ran 30 peak tph still, whereas today they’re restricted to 24.

• Stephen Bauman

“peak frequency was higher then – in the 1960s lines routinely ran 30 peak tph still, whereas today they’re restricted to 24.”

What’s the reason for today’s restriction?

• Russell.FL

1) People were skinnier back then
2) People carried around less stuff than they do right now

People don’t relentlessly work 9-5 as much as they did back then either.

5. Stephen Bauman

“One factor that I have unfortunately not seen in the papers I have read is trip length. Frequency is more important for short trips than long ones. This is significant, since when the headway is shorter relative to in-vehicle trip time we should expect lower elasticity with respect to the headway.”

The same principle should apply to bus rides. The average local NYC bus trip is 2.1 miles, according to the NTD. Dividing the trip distance by the 7.05 mph average speed yields that the average local bus trip duration is slightly under 18 minutes. This means that the average NYC bus trip duration already takes less time than most cities where the average bus speed is greater. Most strategies for reversing the decline in bus use has been to increase bus speed.

When the walk to/from the bus stop is taken into account, strategies that increase bus speed by reducing the distance between bus stops becomes counter productive. Any time savings created by eliminating a bus stop is matched by increased walking time to the bus stop.

• Alon Levy

Even when you take into account the fact that passengers are indifferent between spending 1 minute walking or waiting and spending 2 in motion, bus stop consolidation leads to lower overall weighted trip times. Plugging in New York numbers here yields an optimum of maybe 500 meters (less if destinations are randomly distributed, more if they’re all at definite stop locations like subway stations).

But more broadly, when I wrote this post I expected the theory to produce the same frequency-ridership spiral for buses and for trains, but this is not so. The important bit is that on the NYC subway, only about one quarter of workers are affected by service cuts, so to cut the workforce by 1% you need to cut service by 4%. This is not the case on the buses, where drivers are around 70% of all employees (link, PDF-p. 273), so figure maybe three quarters are affected by service cuts if you add in some maintenance workers and supervisors.

The formula for the ratio of subsidy to service cut is $(v-er)/(1-r)$ where v is the proportion of cost that’s variable, e is the elasticity of ridership with respect to frequency, and r is the operating ratio. For the subway, one gets (0.25 – 0.4*0.5)/0.5 = 0.1. For the buses, one gets (0.75 – 0.4*0.5)/0.5 = 1.1. So cutting subsidy by 1% means cutting service by just 0.9% and cutting ridership by just 0.36%.

Similarly, the formula for the impact of an exogenous shock is $r/(v-er)$ on service and $er/(v-er)$ on ridership. With subway parameters these work out to 10 and 4 respectively, so a 24.6% positive shock actually triples ridership since 1.246^4 = 3. With bus parameters these values are only 0.91 and 0.36, so a positive shock is magnified with an exponent of 1.36 rather than 5. This intuitively makes sense: the positive exogenous shock of the 1990s did not lead to a tripling of bus ridership – on the contrary, bus ridership peaked in 2002. The key here is that spirals work both ways, so if service cuts are vicious cycle, then service increases should be a virtuous one.

• Michael

“When the walk to/from the bus stop is taken into account, strategies that increase bus speed by reducing the distance between bus stops becomes counter productive”

Depends on what the goals are – ridership versus costs. Holding Level of Service constant, labor hours needed in a bus system is function of average speed. So if the bus can be sped up considerably by better stop spacing, stop design, signal timing, reducing schedule padding, etc, labor hours can be reduced proportionally with little-to-no change in service. i.e. if buses averaged 14 MPH instead of 7, we’d only need half as many drivers to provide the same level of service. Since buses wear out by the mile & bus system costs are primarily labor, speeding up a bus substantially reduces costs holding LOS constant. Similarly, LOS can increase significantly while holding cost constant by increasing speed.

On that same note. For all the neocons that want public transit to “breakeven” on fare box… the formula is full dedicated lanes, aggressive signal preemption, & getting the buses moving about 3X combined with laying off 2/3 of drivers.

They are all for mass transit breaking even but since all Real Americans(tm) drive everywhere those subsidies are okay. I want them to get back to me once automobiles start breaking even.

6. mdahmus

Great post and one I am definitely going to come back to multiple times.

Possible typo here:

“The combination of elasticity and fixed costs means that rail ridership responds wildly to small shocks to ridership.”

did you mean the second “ridership” to be “service”?

• Alon Levy

No, I mean ridership. Using NYC subway parameters, a small shock to ridership, like a change in the crime rate, train comfort or speed, gas prices, travel behavior, etc., is magnified with an exponent of 5. This way, if 1% of subway riders switch to Uber, the ensuing service cuts lead to a loss of 5% of subway ridership, which may evaporate, go over to Uber, or go to private cars.

• mdahmus

Got it. I think it might be helpful to reword to make the implied “service cuts” in the middle of the sentence more obvious.

7. ckrueger99

How would we examine the effect of fares on ridership? Particularly in cases like LIRR/MTA and SEPTA where regional rail fares are much higher than bus and subway. I ask this because the solution to making better use of city RR stations/lines is both higher frequency and fares closer to that of a subway swipe.

• Alon Levy

There’s literature on that as well. Lago-Mayworm-McEnroe has some lit review, and the VTPI paper has a lot more lit review.

However, I believe the test cases all involve raising or lowering the fare on a system, rather than offering two services at different price points (like US commuter rail vs. urban transit).

• ckrueger99

I would assume you get non-linear elasticity for city RR stations. No change in demand until you get close to swipe price, then boom. Or integration into the free transfer or monthly pass systems

Most places in the US don’t have two services. And the few that do are serving different markets because there is actually enough people to have two.

9. Michael Whelan

Alon, when these frequency-derived ridership declines happen, do you think people are mostly accomplishing their trips through other means or not making the trip at all? I ask this question from the context of Washington DC. If I have a non-essential trip (for example, hanging out with friends), I will do one of five things depending on where my origin and destination points are:

1. Ride my bike (my favorite choice, but tough in rain and snow, and unsafe on the return if I’m going out drinking)
2. Bring a book to read and suck it up and wait for Metro
3. Take a bus (for example, the Green Line is mostly paralleled by the 70 bus, which is more frequent than rail on weekends)
4. Take an Uber/Lyft
5. Cancel my plans (most often, this happens when I am meeting my friends in Virginia, which often is nearly impossible to get to on weekends)
6. Drive a personal car (I don’t own a car, but for many people, that would be a go-to option)

I think that the answer to which of these is happening is important. Obviously, if people are mostly taking the bus, they are still paying a fare, usually to the same transit agency. Therefore, would you consider those diversions less of a problem than people switching to Uber/Lyft?

And then from a broader urbanist perspective, shifting trips to bikes is obviously a lot better than shifting them to cars.

Finally, I think the worst of them all is cancellations, since it demonstrates that bad frequency actually shrinks the civic connectedness of the metropolitan area.

10. Ryan Kennedy

Sorry not exactly on topic but it’s news on subway costs, which nobody does better than Aron and this blog:

https://la.curbed.com/2019/2/28/18244970/purple-line-subway-budget-westwood

The Purple Line is the latest project affected by swiftly escalating construction costs, which have inflated the prices of some of Metro’s signature projects.

Metro had previously budgeted slightly less than \$1.4 billion for the third phase of the project, though when Los Angeles County voters approved the Measure M sales tax initiative in 2016, the agency predicted it would cost closer to \$2 billion. On Thursday, the board agreed to spend \$3.2 billion on the project.

Metro staff told the board Thursday that the entire nine-mile project, which will bring the line to Westwood, is now expected to cost roughly \$1 billion for every new mile of track.

Like another paroxysm of how Altamont is so much better, world peace will descend, all want be eliminated and make the chewing gum save it’s flavor on the bedpost overnight.?

Novelty song that made the phrase “but will it make your chewing gum lose it’s flavor on the bedpost overnight?” common for quite some time.

It’s a terrible thing, if it does.

11. Ross Bleakney

>> In contrast, bus costs are dominated by driver wages, which are not fixed.

No, but the same sort of peak versus non-peak wage issues you mentioned exist (https://humantransit.org/2017/08/basics-the-high-cost-of-peak-only-transit.html). While the numbers may not be quite as strong for all day frequent bus service as it is for all day frequent rail service, pretty much all the same arguments apply. Service cuts are not that cost effective and lead to weaker fare box recovery, which means that you get little savings. (The opposite is true as well — improved all day service isn’t that expensive).

As a resident of Seattle, it is interesting to see how we have managed to buck the trend, and have increased transit ridership the last couple years. There are number of reasons for this, and they all work together:

1) Finally (mostly) building the most important segment of the light rail subway system, between the University of Washington and downtown.
2) Small speed improvements for the buses.
3) Large frequency improvements for the buses.
4) Increased urbanization.

My money is on the last two. Seattle has been going through a massive population boom and growth is happening much faster (especially in absolute terms) in the city than in the suburbs. It is also centered around various “urban villages” meaning that communities with fairly good transit have seen the biggest growth. At the same time, frequency has increased substantially as funding has increased. The light rail line also caused a restructure, which in turn lead to more frequency. It is tempting to chalk up the speed improvement of the new extension of the subway as being responsible for a lot of the increased use, but I think this is exaggerated. The stop at the UW (the northern terminus of the line) is awkward, and requires a long walk for many riders. It is also not far enough north (the next stop they are building will be). For many riders, there is no speed improvement over taking the old bus — but there is a frequency improvement (both for getting to the UW as well as making the transfer). If you just look at the numbers, you can see that the new subway extension — while successful — still represents a small portion of the overall transit growth. Finally, while the city (and county) have made some speed improvements (off board payment here and there, some bus lanes) nothing really big has been built in the last couple years (again, this will change in a few years). It is mostly urbanization and better all day service that has lead to the increased transit use.

I expect this to continue, as the light rail system moves further north. Right now the Seattle transit network is still fairly awkward, with trips focused on the more popular destinations, as opposed to something close to a grid. As frequency increases, though, transfers become less of an issue, and this should enable the county (which runs the bus system) to create a real grid. This should in turn lead to higher ridership. Trips that used to require a very time consuming, out of the way transfer will instead be straight forward. Moving towards a grid — or an “anywhere to anywhere” — system is another example of how service improvements can pay for themselves (or come close to it). Transit scales.

12. Dan

What would happen if a government started to subsidize tickets during a recession, for example? Would you have to increase service or should the service stay the same?

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