Impact of Transit Fare Increase on Ridership and Revenue

On May 4, 2003, the Metropolitan Transportation Authority’s New YorkCity Transit subsidiary (NYCT) raised its subway and bus fares for thefirst time since discounted and unlimited-ride MetroCards (electronic farecards) were introduced between July 1997 and January 1999. Before the2003 fare increase, the agency did not have any experience with eitherthe direct ridership effects of price changes for unlimited-ride passes or theshift of customers when prices of different fare media increased at dif-ferent rates and customers could shift from one fare medium to another.Partly on the basis of work done by other transit agencies, NYCT devel-oped a spreadsheet model that used direct fare elasticities to estimateabsolute ridership loss and used trip diversion rates (similar to cross-elasticities) to estimate the likelihood that passengers would shift froma fare instrument with a larger percentage increase to one with a smallerincrease. The actual shift of customers between fare instruments after thefare increase was greater than projected, with the result of a lower thanexpected systemwide average fare. The negative revenue impact from thelower average fare was mostly offset by a lower than expected ridershipdecline. The estimated fare elasticities were below the average of historicalNYCT fare elasticities and suggest that customers using unlimited-ridepasses were less sensitive to fare changes.

On May 4, 2003, the Metropolitan Transit Authority’s New York CityTransit subsidiary (NYCT) raised its subway and bus fares for thefirst time since November 1995, when the base fare rose from $1.25to $1.50. In the period since the 1995 fare increase, NYCT completedsystemwide installation of automated fare collection (AFC) equip-ment and expanded the acceptance of MetroCard, an electronic farecard capable of storing time or value, to all subway stations and busroutes. Between July 1997 and January 1999, NYCT introduced thefollowing MetroCard fare incentives:

• Free transfers from bus to subway and from subway to bus start-ing July 1997 (previously, a full fare was charged for these transfers);

• A 10% bonus value added on all value-based MetroCard pur-chases of $15 or more starting January 1998 [e.g., a customer spend-ing $15 received a $16.50 MetroCard ($15 plus the 10% bonus of$1.50, equal to one free ride)];

• Starting July 1998, 7-day and 30-day unlimited-ride passes; and• Staring January 1999, 1-day fun pass.

The introduction of each fare incentive resulted in an increase inMetroCard market share, as well as an increase in the proportion ofcustomers paying fares with discounted MetroCards (1-day, 7-day,and 30-day passes and bonus MetroCard). Customers continued toshift to discounted fare options between 1999 and 2002, so that byOctober 2002, nearly three-quarters of all trips were made with dis-counted MetroCards, as shown in Figures 1a to 1d. As a result ofcustomers’ switching to lower-priced fare options, the nonstudentaverage fare declined from $1.38 in 1996 to $1.04 in 2002.

To make the purchase of MetroCards more convenient, the agencybegan to install MetroCard vending machines (MVMs) at stations in1999, and by September 2002, all stations in the subway system wereequipped with at least one MVM. The MVMs allow customers topurchase MetroCards either with cash or with credit or debit cards.A new single-ride ticket, available for $1.50 in cash, also becameavailable at all MVMs, but not at token booths.

The May 4, 2003, fare change was the first NYCT fare increaseimplemented with the multiple-fare structure made possible byMetroCard. For the first time, the agency had the ability to increase faremedia prices at different rates. Although that provided greater flex-ibility in developing a new fare structure, estimating the ridership andrevenue effects of different fare scenarios became more complex.

While there are considerable data available on the direct ridershipeffects of past NYCT fare increases, the agency had no previous expe-rience with either the direct ridership effects of unlimited-ride passprice changes or the shift of customers when prices of different faremedia increase at different rates and customers can shift from one faremedium to another.

RIDERSHIP AND REVENUE TRENDS BEFOREFARE INCREASE

The 2003 fare increase came amid a period of unsettled subwayand bus ridership resulting from a weak New York City economyand the impact of the September 11, 2001, World Trade Center attack.The ridership trend during the 2 years before the fare increase wasin sharp contrast to the significant ridership growth that occurredbetween 1996 and 2000, when average weekday nonstudent subwayridership increased by 24% and weekday nonstudent bus ridershipincreased by 50%. The 1996 to 2000 ridership growth resulted mainlyfrom the introduction of the MetroCard fare incentives, as well as arobust New York City economy, as measured by 10.5% NYC employ-ment growth over the same period. A NYCT paper on the impactof the AFC fare incentives was presented at the January 2000 TRBmeeting (1).

Impact of Transit Fare Increase on Ridership and RevenueMetropolitan Transportation Authority, New York City

Robert L. Hickey

Office of Management and Budget, New York City Transit, Metropolitan TransitAuthority, 2 Broadway, Room D17.81, New York, NY 10004.

239

Transportation Research Record: Journal of the Transportation Research Board, No. 1927, Transportation Research Board of the National Academies, Washington,D.C., 2005, pp. 239–248.

240 Transportation Research Record 1927

Cash/Token89%

MetroCard11%

Cash/Token/SRT19%

Regular MC13%

Bonus MC27%

1 Day Pass3%

7 Day Pass27%

30 Day Pass11%

Cash/Token/SRT15%

Regular MC11%

Bonus MC25%

1 Day Pass5%

7 Day Pass30%

30 Day Pass14%

Cash/Token/SRT28%

Regular MC14%

Bonus MC31%

7 Day Pass20%

30 Day Pass7%

(a) (b)

(c) (d)

FIGURE 1 Historical fare media market share (discounted fare media shaded): (a) October 1996, (b) October 1998, (c) October 2000, and(d) October 2002. (SRT � single-ride ticket; MC � MetroCard.)

Figures 2 and 3 show the ridership and NYC employment trendsfrom January 2001 to April 2003. In early 2001, New York Cityemployment growth began to slow, and starting in May 2001, employ-ment began to decline slightly from the same month in 2000. Subwayridership continued to increase, though not at the previous year’s rate,with January to August 2001 weekday subway ridership increasing4.9% from the previous year, compared with 7.2% growth during thesame period in 2000. Bus ridership remained strong between Januaryand August 2001, with weekday bus ridership increasing by 7.5%from the previous year.

In the 11 months following the September 11, 2001, World TradeCenter attack, New York City lost nearly 145,000 jobs (3.8% decline),and weekday subway ridership declined by 2.6% from the previous11-month period. The subway ridership loss was mainly at Manhattanstations (particularly lower Manhattan), with ridership still increasingin the boroughs outside Manhattan. Bus ridership continued to growduring that period but at a diminishing rate as the year went on. It ispossible that some subway riders were initially diverted to buses

because of subway line reroutings or unscheduled service disruptionsthat occurred after September 11, particularly in lower Manhattan.Some customers may have also shifted to the bus from fear of anotherattack.

While subway ridership was lower than for the same period of theprevious year throughout the 11 months following September 2001,most of the drop occurred in the months immediately after that time,and subway ridership stabilized and remained near the immediate post-September 11 level through most of 2002. Although it was expectedthat employment would increase in 2003, New York City jobs con-tinued to decline, and subway and bus ridership both were decliningslightly from the previous year through April 2003 (partially fromsevere weather in January and February).

Despite the drop-off in ridership after September 11, 2001, com-bined subway and bus ridership rose 35.8% between 1996 and 2002.However, passenger revenue rose by only 4.7% over the same periodbecause of the lower average fares resulting from the MetroCard fareincentives.

Hickey 241

% Change from Prior Year

-15%

-10%

-5%

0%

5%

10%

15%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr

NYC Employment Subway Avg. Wkdy. Ridership

2001 2002 2003

FIGURE 2 Average weekday subway ridership and NYC employment, 2001–2003.

% Change from Prior Year

-15%

-10%

-5%

0%

5%

10%

15%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr

NYC Employment Bus Avg. Wkdy. Ridership

2001 2002 2003

FIGURE 3 Average weekday bus ridership and NYC employment, 2001–2003.

NEW FARE STRUCTURE

While the impact from lower average fares was offset by the largeridership growth in previous years, projections of lower ridershipgrowth, coupled with slower growth in tax-based subsidies and rapidlyincreasing agency expenses, were expected to lead to budget deficitsin 2003 and 2004. Thus, the primary goal in setting the new fare struc-ture was to generate sufficient revenue to maintain a balanced budgetthrough 2004, as required by state charter.

A secondary goal was to develop a fare structure that would advancethe agency’s business strategy of enhancing the convenience of pur-chasing MetroCards while minimizing revenue support and mainte-nance expenses. To that end, the agency sought to structure the fareoptions to do the following:

• Encourage customers to purchase higher value cards to reducethe number of sales transactions, shorten lines at MVMs and booths,and reduce the amount of card stock that must be purchased;

• Encourage even-bill denomination purchases to shorten trans-action times and reduce the number of machine jams and servicecalls; and

• Encourage debit or credit purchases to reduce the amount of cashthat must be processed (Because a large proportion of 30-day pass saleswere debit or credit purchases, a fare structure that moved customersto the 30-day pass would likely increase debit or credit purchases).

Table 1 shows the fare structure that was approved by the MTAboard for implementation in May 2003. The fares for discounted highervalue cards (7-day and 30-day passes and bonus MetroCard) increasedat a lower rate than did the 33.3% increase ($1.50 to $2) for non-discounted lower-value cards (single-ride tickets and MetroCardswith no bonus value added) and for cash. The bonus value added toMetroCards was increased from 10% to 20%, for an effective bonusfare of $1.67, a 22.2% increase over the former effective bonus fareof $1.36. As a further inducement to attract customers to the bonusMetroCard, the minimum threshold for receiving the bonus was low-ered from $15 to $10, so that customers could get a bonus trip whenpurchasing only five trips.

Before the fare increase, the $4 price for the 1-day pass, with a break-even amount of only three trips (based on the effective bonus fare),was considered low compared with similar passes in other cities, andit was purchased by customers beyond the intended market of visitorsto the city. Therefore, the 1-day fun pass was the only fare categorythat increased at a higher rate than the base fare, rising by 75% from$4 to $7.


A new $33 7-day express bus plus pass, valid on express and localbuses and the subway, was introduced to replace the $120 30-dayexpress bus plus pass.

Finally, at the same time it approved the new fare structure, the MTAboard also approved a resolution to discontinue acceptance of thetoken, for more than 40 years the sole means of paying the subway fare.

NYCT FARE MODEL

Shortly after the introduction of the MetroCard fare incentives wascompleted in 1999, NYCT began to explore techniques to estimate theridership and revenue effects of future fare increases. Several transitagencies were contacted to obtain information on their experiencewith past fare increases and their methods for estimating ridershipand revenue changes caused by changes in fare structure. At the time,the Metropolitan Atlanta Rapid Transit Authority (MARTA) haddeveloped a model to identify shifts among fare payment methodsand to forecast revenue and ridership changes resulting from a farechange (2). The Southeastern Pennsylvania Transportation Authority(SEPTA) developed a similar approach that used factors to determinethe ridership shift between fare categories in response to a changein the fare ratio of two fare instruments.

NYCT staff developed a fare model that used the approach devel-oped by MARTA and SEPTA, and they modified it to reflect NYCT’sfare structure and ridership patterns. Base ridership and average fares,by fare category, are entered along with the proposed fares. The modeluses a two-stage spreadsheet that applies direct fare elasticities toestimate the ridership loss from the absolute fare change within eachfare category, and then it applies a ridership diversion rate (similarto a cross-elasticity) for each pair of fare instruments to estimate thelikelihood that passengers will shift from a fare instrument with alarger percentage increase to one with a smaller increase.

The direct elasticities used in the model are based in part on resultsfrom the nine NYCT fare increases that occurred between 1972 and1995. As shown in Table 2, subway average weekday ridership pointelasticities with respect to the fare (on the basis of average weekdayridership for a full year before and after the fare increase) averaged−0.09 between 1972 and 1995. Weekend subway ridership elastici-ties were higher (−0.12 for Saturdays and −0.21 for Sundays due tothe more discretionary nature of weekend subway ridership. Busaverage weekday ridership elasticities averaged −0.37 over the sameperiod (Table 2). Saturday and Sunday bus ridership elasticities wereclose to the weekday elasticities (−0.38 for Saturdays and −0.39),because bus ridership is generally more discretionary (i.e., less work

TABLE 1 Fare Structure Approved May 4, 2003

MetroCard Bonus Express Bus

Cash/SRT/ Regular Effective 7-Day 30-Day 1-Day Base 30-Day 7-DayTokena MetroCard Percent Threshold Fare Pass Pass Pass Fare Passb Passb

Old fare $1.50 $1.50 10 $15 $1.36 $17 $63 $4 $3 $120 NA

New fare $2.00 $2.00 20 $10 $1.67 $21 $70 $7 $4 NA $33

% change 33% 33% — — 22% 24% 11% 75% 33% NA NA

SRT � single-ride ticket.aToken was eliminated under new fare structure.bAlso valid on subway and local bus.

oriented) than subway ridership. The elasticities shown in Table 2do not account for changes in New York City employment.

The NYCT fare model is illustrated in Figure 4 for subway andFigure 5 for bus. Part 1 in each figure shows the model inputs, includ-ing base revenue and ridership, old and new fares, direct elasticities,and ridership diversion rates. A higher direct elasticity was used forlow-value fare instruments (regular MetroCard, cash, and single-rideticket), while a lower elasticity was used for high-value fare instru-ments (bonus MetroCard, 7-day and 30-day passes). From experi-ence at other agencies, it was assumed that customers who purchasefare media in smaller increments ride the system less frequently andare more discretionary in their trip making than are others.

The ridership diversion rates reflect estimates of the likelihoodof customers switching between pairs of fare instruments, accord-ing to experience in other cities and knowledge of NYCT customers.The highest trip diversion rates were set for the 7-day pass–bonusMetroCard at 0.40, and the bonus–regular fare MetroCard at 0.30.It was assumed that there would be no shift from MetroCards (includ-ing single-ride tickets) to tokens and cash or from MetroCards tosingle-ride tickets.

Part 2 of Figures 4 and 5 shows the model output. Section A ofPart 2 shows the ridership loss for each fare instrument resulting fromthe application of direct elasticities (ridership times percentage fareincrease times elasticity).

Sections B through E of Part 2 in Figures 4 and 5 show the calcu-lation of the ridership shift between fare instruments. As an example,the shift from 7-day pass to bonus MetroCard in the subway model,Figure 4, is highlighted. The model first calculates the relative changein average fare between each pair of fare instruments on the basis ofcurrent and proposed fares. Under the fare structure in effect beforethe May 2003 fare increase, the ratio of the 7-day pass average fare tothe bonus MetroCard average fare was 0.688 (Section B). Under theMay fare increase, with the 7-day pass price increasing by 23.5%and the bonus MetroCard price increasing by 22.2% (shown in Part 1),the ratio of the 7-day pass average fare to the bonus MetroCard aver-age fare becomes 0.696 (Section C), for a relative change of 1.1%(Section D) [i.e., (0.696 − 0.688)/0.688].

The model calculates the number of shifted trips as a percentageof ridership for the fare instrument that is becoming relatively moreexpensive. Thus, on the basis of a 7-day pass–bonus MetroCard diver-sion rate of 0.40, the 1.1% increase in the 7-day pass to bonus Metro-

Hickey 243

Card ratio would decrease 7-day pass ridership by 0.43% (0.40 �1.1%) or 1,624,636 trips, and it would increase bonus MetroCardridership by the same amount (Section E).

When passengers switch to using passes, it was assumed that theywould increase their trip making by 20%. The estimated percentage ofgenerated pass trips is based in part on NYCT’s ridership change afterthe introduction of passes in 1998. The 20% figure is also in line withresults in other cities after the introduction of passes. Pass users inAtlanta increased their trip making by 14% after a monthly pass wasintroduced in 1979 (3), and 7-day pass users in Chicago increased theirtransit travel 31% after a 7-day pass was introduced in late 1998 (4).

Using the ridership loss from Section A and the trip diversions inSection E (including the 20% factor for passes), ridership after thefare change is calculated (Section F). Revenue is calculated by multi-plying the new average fares for each fare medium (current averagefare increased by the percent increase in fare and pass price) by theridership after the fare change.

The trip diversion rates used in the model were conservative in thatthey result in small shifts from fare instruments with larger increasesto those with smaller increases. Therefore, there was a risk that rev-enue would be lower than projected if more customers switched tolower priced fare media. There was also the risk of lower revenue if theactual ridership loss were higher than projected or if the average passfares were lower than projected. Because of the risk that deviations ina series of factors (such as lower than expected average fare accom-panied by higher than expected ridership loss) could compound therevenue variance from forecast, many of the fare options that wereexamined were tested with alternate scenarios that measured the effectof changing one or more of those factors. The various scenarios pro-vided a range of revenue projections to help identify the risks inmodeling a given fare structure.

Aside from the inherent risks in projecting ridership loss and diver-sions, the model also has limitations that, in some cases, requiredadjustments to the model results. The main limitation of the NYCTmodel is that it cannot accommodate introduction of new fare media(e.g., the 7-day express bus plus pass) or elimination of existing faremedia (e.g., tokens).

The potential market share of the new 7-day express bus pass wasestimated outside the model by obtaining records of all MetroCardtrips made by express bus users over a typical week and by using

TABLE 2 NYCT Fare Increases: Average Weekday Ridership Changeand Point Elasticities

Subway Bus

Date of Change Ridership Point Ridership PointFare Change in Fare Change Elasticity Change Elasticity

1/5/72 17% −4% −0.24 −6% −0.38

9/1/75 43% −5% −0.12 −17% −0.40

6/28/80 20% −3% −0.13 −5% −0.26

7/3/81 25% −3% −0.11 −11% −0.42

1/2/84 20% −1% −0.07 −7% −0.35

1/1/86 11% 1% 0.12 −3% −0.30

1/1/90 15% −4% −0.29 −6% −0.37

1/1/92 9% 0% −0.04 −4% −0.41

11/12/95 20% 1% 0.04 −8% −0.41

Average 20% −2% −0.09 −7% −0.37


Part 1: Model InputsBase Token Single_Ride Regular MC Bonus MC 7-Day 30-Day 1-DayRidership 146,188,044 13,585,671 163,969,100 356,686,140 379,758,664 216,735,332 81,644,141Revenue $212,197,407 $20,378,506 $222,046,456 $439,059,786 $321,750,952 $197,098,645 $77,859,619Avg Fare $1.45 $1.50 $1.35 $1.23 $0.85 $0.91 $0.95

Old Fare $1.50 $1.50 $1.50 $1.36 $17 $63 $4New Fare $2.00 $2.00 $2.00 $1.67 $21.00 $70.00 $7.00% Change 33.3% 33.3% 33.3% 22.2% 23.5% 11.1% 75.0%New Avg Fare $1.94 $2.00 $1.81 $1.50 $1.05 $1.01 $1.67

Token Single_Ride Regular MC Bonus MC 7-Day 30-Day 1-DayDirect Elasticities -0.15 -0.15 -0.15 -0.10 -0.10 -0.10 -0.15

Ridership Diversion RatesToken Single_Ride Regular MC Bonus MC 7-Day 30-Day 1-Day

Token --- 0.00 0.00 0.00 0.00 0.00Single-Ride 0.25 --- 0.00 0.00 0.00 0.00Reg MC 0.25 0.25 --- 0.30 0.25 0.10

0.000.000.05

Bonus MC 0.25 0.20 0.25 --- 0.40 0.15 0.057-day 0.20 0.05 0.05 0.30 --- 0.20 0.0530-day 0.10 0.05 0.05 0.20 0.20 --- 0.051-day 0.05 0.05 0.05 0.10 0.10 0.05 ---

Part 2: Model OutputsA. Ridership Loss (from application of direct elasticities)

Token Single_Ride Regular MC Bonus MC 7-Day 30-Day 1-DayRidership Loss -7,309,402 -679,284 -8,198,455 -7,926,359 -8,935,498 -2,408,170 -9,184,966

B. Old Fare RatiosToken Single_Ride Regular MC Bonus MC 7-Day 30-Day 1-Day

Token --- 1.033 0.933 0.848 0.584 0.627 0.657Single-Ride 0.968 --- 0.903 0.821 0.565 0.606 0.636Reg MC 1.072 1.108 --- 0.909 0.626 0.672 0.704Bonus MC 1.179 1.219 1.100 --- 0.688 0.739 0.7757-day 1.713 1.770 1.598 1.453 --- 1.073 1.12630-day 1.596 1.649 1.489 1.354 0.932 --- 1.0491-day 1.522 1.573 1.420 1.291 0.888 0.954 ---

C. New Fare RatiosToken Single_Ride Regular MC Bonus MC 7-Day 30-Day 1-Day


D. Change in Ratios (positive means fare is relatively more expensive)Token Single_Ride Regular MC Bonus MC 7-Day 30-Day 1-Day

Token --- 0.0% 0.0% -8.3% -7.4% -16.7% 31.3%Single-Ride 0.0% --- 0.0% -8.3% -7.4% -16.7% 31.3%Reg MC 0.0% 0.0% --- -8.3% -7.4% -16.7% 31.3%Bonus MC 9.1% 9.1% 9.1% --- 1.1% -9.1% 43.2%7-day 7.9% 7.9% 7.9% -1.1% --- -10.1% 41.7%30-day 20.0% 20.0% 20.0% 10.0% 11.2% --- 57.5%1-day -23.8% -23.8% -23.8% -30.2% -29.4% -36.5% ---

E. Trip Diversions (includes increase in trips by new pass users)Token Single_Ride Regular MC Bonus MC 7-Day 30-Day 1-Day

Token --- 0 0 3,322,456 2,784,534 3,508,513 0Single-Ride 0 --- 0 247,012 64,694 163,028 0Reg MC 0 0 --- 3,726,570 780,805 1,967,629 -1,275,690Bonus MC -3,322,456 -247,012 -3,726,570 --- -1,624,636 8,560,467 -1,762,7717-day -2,320,445 -53,911 -650,671 1,624,636 --- 8,488,723 -1,700,92030-day -2,923,761 -135,857 -1,639,691 -7,133,723 -8,488,723 --- -2,347,2691-day 0 0 1,275,690 1,762,771 1,700,920 2,347,269 --- Total Shift -8,566,662 -436,780 -4,741,243 3,549,723 -4,782,406 25,035,630 -7,086,650

F. Post Fare Increase Ridership and RevenueToken Single_Ride Regular MC Bonus MC 7-Day 30-Day 1-Day

New Ridership 130,311,980 12,469,607 151,029,402 352,309,504 366,040,759 239,362,792 65,372,525New Revenue $252,203,610 $24,939,214 $272,698,075 $530,044,048 $383,099,840 $241,862,232 $109,098,948

FIGURE 4 Fare models for subway.

Hickey 245

Part 1: Model InputsBase Cash/Token Single Regular MC Bonus MC 7-Day 30-Day 1-DayRidership 139,915,660 683,311 68,076,572 148,016,662 211,485,159 74,589,086 40,544,403Revenue $132,856,632 $1,024,924 $69,648,221 $137,641,488 $178,459,174 $65,966,281 $38,664,932Avg Fare $0.950 $1.500 $1.023 $0.930 $0.844 $0.884 $0.954

Old Fare $1.50 $1.50 $1.50 $1.36 $17.00 $63.00 $4.00New Fare $2.00 $2.00 $2.00 $1.67 $21.00 $70.00 $7.00% Change 33.3% 33.3% 33.3% 22.2% 23.5% 11.1% 75.0%New Avg Fare $1.27 $2.00 $1.36 $1.14 $1.04 $0.98 $1.67

Cash/Token Single Regular MC Bonus MC 7-Day 30-Day 1-DayDirect Elasticities -0.30 -0.30 -0.30 -0.20 -0.20 -0.20 -0.30

E. Ridership Diversion RatesCash/Token Single Regular MC Bonus MC 7-Day 30-Day 1-Day


Part 2: Model OutputsA. Ridership Loss (from application of direct elasticities)

Cash/Token Single Regular MC Bonus MC 7-Day 30-Day 1-DayRidership Loss -13,991,566 -68,331 -6,807,657 -6,578,518 -9,952,243 -1,657,535 -9,122,491

B. Old Fare RatiosCash/Token Single Regular MC Bonus MC 7-Day 30-Day 1-Day


C. New Fare RatiosCash/Token Single Regular MC Bonus MC 7-Day 30-Day 1-Day


D. Change in Ratios (positive means fare is relatively more expensive)Cash/Token Single Regular MC Bonus MC 7-Day 30-Day 1-Day

Token --- 0.0% 0.0% -8.3% -7.4% -16.7% 31.3%Single-Ride 0.0% --- 0.0% -8.3% -7.4% -16.7% 31.3%Reg MC 0.0% 0.0% --- -8.3% -7.4% -16.7% 31.3%Bonus MC 9.1% 9.1% 9.1% --- 1.1% -9.1% 43.2%7-day 7.9% 7.9% 7.9% -1.1% --- -10.1% 41.7%30-day 20.0% 20.0% 20.0% 10.0% 11.2% --- 57.5%1-day -23.8% -23.8% -23.8% -30.2% -29.4% -36.5% ---

E. Trip Diversions (includes increase in trips by new pass users)Cash/Token Single Regular MC Bonus MC 7-Day 30-Day 1-Day

Token --- 0 0 3,179,901 2,665,060 3,357,976 0Single-Ride 0 --- 0 18,636 6,508 8,200 0Reg MC 0 0 --- 1,547,195 324,174 816,919 -633,506Bonus MC -3,179,901 -18,636 -1,547,195 --- -904,749 3,552,400 -875,3917-day -2,220,883 -5,423 -270,145 904,749 --- 4,727,315 -844,67530-day -2,798,313 -6,833 -680,766 -2,960,333 -4,727,315 --- -1,165,6521-day 0 0 633,506 875,391 844,675 1,165,652 --- Total Shift -8,199,098 -30,892 -1,864,599 3,565,539 -1,791,648 13,628,461 -3,519,223

F. Post Fare Increase Ridership and RevenueCash/Token Single Regular MC Bonus MC 7-Day 30-Day 1-Day

New Ridership 117,724,996 584,088 59,404,315 145,003,682 199,741,269 86,560,012 27,902,689New Revenue $149,047,376 $1,168,127 $81,034,337 $164,804,080 $208,207,879 $85,059,243 $46,566,162

FIGURE 5 Fare models for bus.

those data to create a trip frequency matrix of express bus users. Allusers making enough weekly express bus, local bus, and subway tripsto break even or save money (based on the difference between the$33 pass price and the cost of the trips at the bonus fare) were assumedto shift to the new pass.

The new fare media choice of token users was based on surveysconducted at selected token booths that measured the percentage oftoken purchase transactions by denomination (e.g., single-token pur-chases accounted for 54% of token sales transactions). It was assumedthat 80% of the single-token purchasers would switch to the single-rideticket and all other token users would switch to the regular MetroCard[no shift from token to cash (on buses), bonus MetroCard, or any ofthe unlimited-ride passes].

Another limitation of the model is that, while it calculates the pas-senger shift between the bonus MetroCard and other fare instrumentsin response to a change in the bonus percentage (and the resultingchange in average fare), it cannot estimate the effect of lowering orraising the purchase threshold for receiving additional bonus value.The effect of changing the bonus threshold was calculated indepen-dently, on the basis of data on MetroCard sales transactions by pur-chase amount as well as the token purchase survey. It was assumed thatcustomers purchasing amounts near or above the threshold amountwould take advantage of the bonus.

Finally, because the model uses aggregate data, average fares areused within each fare category. As a result, the model assumes thatcustomers who shift between fare categories previously paid the sameaverage fare, as did all customers in the old fare category, and theywill pay the same average fare as existing customers do in the new farecategory. In reality, the number of trips made per month or week bycustomers who shift from value-based cards to passes, for example,is likely closer to the break-even amount than with the number of tripsmade by the average pass user. Therefore, there is a risk of under-stating revenue for customers who shift from value-based fare cardsto lower-fare passes, because they likely make fewer trips and wouldtherefore have a higher average fare than did existing pass users.Conversely, there is a risk of overstating revenue when a pass has anextremely large increase (e.g., 1-day pass), because the average num-ber of trips made by the remaining pass users would likely increase,which would result in a lower average fare.

MODEL RESULTS

Fare Media Market Share

Table 3 compares projected fare media market share from the base2003 forecast (excluding fare increase) with the market share yieldedby the model for the approved fare structure, which is adjusted for theelimination of the token, introduction of the 7-day express bus pass,and change in bonus threshold. The fare categories with the lowest


percentage fare increase (bonus MetroCard and 30-day pass) wereprojected to have the largest percentage market share increase. Thelargest percentage decrease was in the category of token, cash, andsingle-ride ticket, owing mainly to the elimination of tokens and theassumption that most token customers would shift to regular Metro-Card. As a result of the shift of token users, the regular MetroCardmarket share was projected to increase, despite having a 33.3% fareincrease. The 7-day pass market share was essentially unchanged dueto trip shifting from cash and token and regular MetroCard, offsetby customers’ shifting to the 30-day pass.

Average Fares

On the basis of rate of increase in each fare category and the newmarket shares resulting from the shift of customers among fare cat-egories, the model projected that the combined subway and bus non-student average fare would rise from $1.04 to $1.30, a 25.3% increase.The subway average fare was projected to increase by 24.6%, and thebus average fare was projected to rise by 25.8%.

Ridership Loss

The model projected a 4.1% total nonstudent ridership loss, or a−0.16 ridership elasticity based on a 25.3% fare increase. The projectedsubway nonstudent ridership loss was 2.9% (−0.12 elasticity basedon a 24.6% increase), and bus ridership was projected to decline by6.5% (−0.25 elasticity based on a 25.8% increase). The ridershiploss resulting from application of direct elasticities was partially off-set by the assumed 20% increase in trip making by customers’ shiftingto 7-day or 30-day unlimited-ride passes.

Revenue Gain

As a net result of the average fare increase partially offset by the rid-ership loss, total revenue was projected to increase by 20.0%. The fullyear impact (2004) was projected at $430 million, and the 2003 impact(May through December) was projected at $286 million.

ACTUAL RESULTS

The three key components in comparing projected and actual resultsfrom the May 4 fare increase are the following:

1. Average fare, by fare medium;2. Actual market shares and the resulting systemwide average

fare; and3. Actual ridership loss.

TABLE 3 Fare Media Market Share and Average Fare: Base and Projected Fares

Market ShareNonstudent

Cash/SRT/ Regular Bonus 7-Day AverageToken MetroCard MetroCard 7-Day 30-Day 1-Day Express Fare

Increase in fare 33.3% 33.3% 22.2% 23.5% 11.1% 75.0% NA —

Base 14.7% 11.4% 24.7% 29.0% 14.3% 6.0% NA $1.04

Projected 7.5% 13.1% 28.4% 29.0% 16.6% 4.8% 0.5% $1.30

Together, the three components determine how close the actualrevenue increase is to the model projection.

Average Fares by Fare Medium

As mentioned earlier, there was a risk that the actual average fares byfare medium could be lower than projected. However, except for the1-day pass, the average fares by fare medium were close to, and inmany cases higher than, the projected average fares. The 1-day passaverage fare was 6% lower than projected. With a 75% increase in the1-day pass price, the number of trips required to break even increasedfrom 3 to 5. As a result, the average number of trips per 1-day passincreased from 4.2 to about 4.5 per pass. Thus, the increase in the1-day pass average fare was less than it would have been had thenumber of trips per pass remained constant. Despite the lower 1-daypass average fare, the average fares by fare medium had little effect onthe systemwide average fare.

Fare Media Market Share and Systemwide Average Fare

As shown in Table 4, the shift of customers to the 30-day pass and theshift away from the 1-day pass were greater than expected. In addi-tion, although the overall pay-per-ride MetroCard market share wasclose to the projection, the proportion of pay-per-ride trips made withbonus MetroCard was higher than expected. The underestimate ofmarket share for the fare media with the lowest average fares (30-daypass, bonus) yielded a $1.26 systemwide average fare, 2.5% lowerthan the projected $1.30 average fare. The finding would suggest that,if the expected ridership loss occurred, total revenue would be 2.5%,or about $64 million, lower than projected.

Ridership

While it is a fairly simple task to calculate the ridership change inrelation to budget (or previous year) after the fare increase, it is not sosimple to separate the impact of the fare increase from that of otherfactors, such as the economy or the underlying ridership trend. Theanalysis was further complicated by lower ridership in January andFebruary 2003 from unusually severe winter weather. As a result,March and April 2003 were used to estimate the ridership trend beforethe fare increase.

Table 5 shows the results from two methods used to isolate the fareincrease impact on ridership. The first method compares the rider-ship change from the 2003 base ridership budget (i.e., excluding thefare increase impact) in the 2 months before the fare increase with

Hickey 247

the ridership change after the fare increase, with the difference in thetwo variances assumed to reflect the fare increase impact. That methodyields a 0.5% subway ridership increase (−1.9% May through Decem-ber 2003 subway ridership variance minus the −2.4% March and Aprilvariance) and a 4.2% bus ridership decrease (−4.1% May throughDecember bus ridership variance minus the 0.1% positive Marchand April variance).

The second method compares the ridership change from 2002 to2003 in the 2 months before the fare increase with the change after thefare increase. That method yields a 0.9% decline in subway ridershipand a 4.7% bus ridership loss.

Taken together, the results of these methods suggest that the rid-ership loss due to the fare increase was lower than expected. While itis unlikely that there was actually a positive impact on subway rider-ship as inferred by comparing ridership to budget, the previous yearcomparison (supported by other estimates) yields a subway ridershiploss of approximately 1%, well below the projected 2.9% loss. The busridership loss was somewhere in the range of 4% to 5%, comparedwith the projected 6.5% loss. The resulting combined subway and busridership loss is approximately 2% compared with the projected 4.1%ridership loss.

Revenue

It appears that most of the negative revenue impact from the lower thanexpected average fare was offset by the positive impact from thelower than expected ridership loss. Depending on the methodologyused, the revenue impact from the fare increase was between $6 and$20 million below the projected impact.

With a similar approach to the method as shown comparing rider-ship to budget, a comparison of the revenue variance from the bud-get before the fare increase in the periods before and after the fareincrease yields a 20.2% revenue increase. Applying that percentageto the revenue budget before the fare increase for May 4 throughDecember 31 (adjusted for the March and April budget variance) yieldsa revenue increase of approximately $280 million, about 2% lowerthan the projected $286 million increase.

Alternatively, a comparison of the revenue change from 2002 to2003 in the periods before and after the fare increase yields a revenueincrease of approximately $266 million, nearly 7% (or $20 million)below the projected increase.

Elasticities

The $1.26 systemwide (subway and bus combined) average fare afterthe fare increase represented a 21.2% increase over the full year 2002average fare of $1.04. Under the assumption that total subway and

TABLE 4 Fare Media Market Share and Average Fare: Projected and Actual Fares

Market ShareNonstudent

Cash/SRT/ Regular Bonus 7-Day AverageToken MetroCard MetroCard 7-Day 30-Day 1-Day Express Fare

Increase in fare 33.3% 33.3% 22.2% 23.5% 11.1 % 75.0% NA —

Projected 7.5% 13.1% 28.4% 29.0% 16.6% 4.8% 0.5% $1.30

Actuala 7.5% 10.8% 32.0% 27.8% 20.0% 1.7% 0.2% $1.26

aJuly–December 2003 results.

bus ridership declined 2%, the resulting systemwide fare elasticityis −0.10, compared with the −0.16 elasticity yielded by the model.

The subway average fare increased 20.5% from $1.11 in 2002 to$1.34 after the fare increase. Assuming a subway ridership loss of 1%yields an elasticity of −0.05, compared with −0.12 from the model.The bus average fare increased 22.4% from $0.92 in 2002 to $1.12after the fare increase. On the basis of a bus ridership loss of 4% to5%, the bus elasticity is between −0.18 and −0.22, compared with theprojected −0.25 elasticity.

CONCLUSIONS

Absent available market research data on customer preferences orhistorical data on fare media shifts after past fare increases, the faremodel provided a reasonable estimate of the revenue impact from thefare increase. From the actual results, there are several modificationsthat will likely improve the model’s ability to forecast the impact offuture NYCT fare changes.

Direct Elasticities

The actual elasticities estimated above are well below the average ofthe historical elasticities shown in Table 2 and the direct elasticitiesused in the model. It is likely that customers using unlimited-ridepasses are less sensitive to fare changes because, once they have pur-chased a pass, there is no incentive to reduce the number of trips made.In fact, the cost per trip declines with each additional trip made witha pass. In future versions of the model, lower direct elasticities willbe used, particularly for unlimited-ride passes.

Ridership Diversion Rates

From the greater than expected shift among fare media, it is clearthat the ridership diversion rates used in the model were too low. Ananalysis of the actual results revealed that if the diversion rates used inthe model were doubled, the resulting market share projection wouldhave closely matched the actual shift between fare media.


Outliers

As a result of fare increase of 75% in the 1-day pass from $4 to $7,the market share for the 1-day pass dropped to 1.7% compared withthe projected 4.8% market share, as shown in Table 4. It is clear fromthat large market share decrease that, when the rate of increase for aparticular fare medium is disproportionate to all other fare categories,the standard trip diversion rates, while valid for smaller increases, maynot be sufficient to project the shift away from that fare instrument.In such cases, it may be prudent to estimate the market share shiftindependently, on the basis of available data or general knowledgeof customer behavior.

ACKNOWLEDGMENTS

The author thanks Larry Hirsch of the revenue analysis unit of NYCTOffice of Management and Budget for his constructive suggestions.Special thanks go to David Jordan (retired from the revenue analysisunit) for his suggestions and for his efforts in developing the earlierversions of the NYCT model.

REFERENCES

1. Hirsch, L. R., J. D. Jordan, R. L. Hickey, and V. Cravo. Effects of FareIncentives on New York City Transit Ridership. In Transportation ResearchRecord: Journal of the Transportation Research Board, No. 1735, TRB,National Research Council, Washington, D.C., 2000, pp. 147–157.

2. Harris, A. E., R. Thomas, and D. Boyle. Metropolitan Atlanta RapidTransit Authority Fare Elasticity Model. In Transportation ResearchRecord: Journal of the Transportation Research Board, No. 1669, TRB,National Research Council, Washington, D.C., 1999, pp. 123–128.

3. McCollum, B. E., and R. H. Pratt. TCRP Report 95: Traveler Response toTransportation System Changes. Chapter 12—Transit Pricing and Fares.Transportation Research Board of the National Academies, Washington,D.C., 2004.

4. Foote, P. J., and D. G. Stuart. Impacts of Tansit Fare Policy InitiativeUnder an Automated Fare System. Transportation Quarterly, Vol. 54,No. 3, 2000, pp. 51–66.

The Public Transportation Marketing and Fare Policy Committee sponsored publication of this paper.

TABLE 5 Ridership Comparisons

Ridership Change Ridership Change from Budget from 2002 to 2003

Subway Bus Total Subway Bus Total

March–April −2.4% 0.1% −1.5% −1.3% 0.3% −0.7%

May–Dec.a −1.9% −4.1% −2.6% −2.1% −4.4% −2.9%

Difference 0.5% −4.2% −1.1% −0.9% −4.7% −2.2%

Expected −2.9% −6.5% −4.1% −2.9% −6.5% −4.1%

aExcludes August 15–18, 2002 & August 14–17, 2003, because of blackout on August 14, 2003.

Documents

Impact of Transit Fare Increase on Ridership and Revenue