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Predicted impact of Triangle Transit’s light rail project on Durham’s captive bus riders

by Bao Tran-Phu


  1. I.              Introduction


North Carolina’s Triangle Transit Authority’s (TTA) plan to construct a light rail network between Durham, Orange, and Wake Counties has been in the works since 2006, but insufficient funding has repeatedly stopped it in its tracks (Roberts, 2006).  The project finally got its first major green light in November 2012 when Orange County residents passed a half-cent sales tax to fund long-term transit investments, after Durham County residents had approved an equivalent tax hike a year before (Freemark, 2011; Grubb, 2013).


With the introduction of light rail, some may worry that its construction and operation may pull resources away from other transit forms, especially local bus systems that are typically disproportionately ridden by the poor (Garrett & Taylor, 1999).  The sales tax hike is expected to cover a large share of the future rail’s costs and even fund bus service expansions.  Nonetheless, the rail could still negatively affect bus funding in the long run, as subsequent investments and expansions might focus more on the publicly popular and environmentally friendly rail.  Triangle Transit has been expanding its bus fleet rapidly in the past years, increasing its size from 60 buses in 2011 to 64 in 2012 (TTA).  Even if the rail project does not force TTA to decommission any of its buses, its bus fleet could cease to grow as quickly in the future.


This paper examines the effect that the light rail’s introduction is expected to have on Durham’s poor.  While the actual effect will depend on legislative decisions made after the rail is constructed, data on other transit systems nationwide is used to demonstrate how increases in rail expenses have historically caused spending on bus transit to change.  Even after controlling for population and economic growth factors, a regression analysis finds no evidence suggesting that increased rail expenses lead to decreases in bus expenditures.  Instead, increased rail expenses have a significant positive effect on bus expenditures, possibly by triggering a positive spillover effect whereby the increase in overall transit ridership leads to bus service expansions that benefit captive riders as well.


  1. II.            Literature Survey


Studies on public transit consistently demonstrate a disproportionate reliance on local buses among the poor.  While the propensity to use public transit increases as income falls (Baum-Snow and Kahn, 2000), the slope of this trend is not uniform across transit modes.  Iseki and Taylor (2001) find that the median household income for transit bus passengers falls between $15,000-$19,999, compared to $30,000-$34,999 for urban rail and $40,000-$44,999 for commuter rail.  Additionally, this propensity to use public transit is not simply a preference for many bus riders.  By looking at data on the L.A. Metropolitan Transportation Authority (MTA), Iseki and Taylor then find that 69.2 percent of MTA bus riders have household incomes below $15,000, while only 20.3 percent of the L.A. County population falls into that category (see Table 1).


Table 1 – MTA Bus Rider Demographics


1995 Household Income

< $7.5k $7.5-15k $15-35k $35-50k $50-75k >$75k

% of MTA Bus Riders

40.2% 29.0% 20.6% 6.0% 2.8% 1.5%

Source: Iseki and Taylor (2001)

Properties inherent to the transit types are blamed for these demographic differences in ridership.  Transit policy-makers typically categorize users as either “choice” riders, who have access to private vehicles, or “captive” riders, who do not (Garrett & Taylor, 1999).  On average, captive riders are more likely to be poor, belong to a minority group, and live in inner cities, while choice riders are more likely to be white and live in suburbs (Garrett & Taylor).  As rail systems are often designed for longer-distance travel connecting the suburbs to the inner-city, they will have an inherent tendency to benefit wealthier populations.


While studies agree that the poor rely on buses, the link between funding for rail and funding for bus has been studied only indirectly.  One of the most direct approaches came from Iseki and Taylor (2001), who use a cost-allocation model to find the average per-trip subsidies of the various transit modes.  Using data on the L.A. MTA, they find average per-trip subsidies of $3.17 for buses, $6.28 for express buses, and $7.85 for light rail.  This provides evidence that the construction of a new rail line would indeed require a substantial re-allocation of subsidies from buses to rails in the absence of any new external sources of funding.  However, in the case of Triangle Transit’s rail project, the half-cent sales tax passed in Durham and Orange Counties and any grants or subsidies that it will receive serve as external sources of funding.  To predict the rail’s impact on bus service expenditure, an analysis must be done on how changes in spending on rail transit leads to changes in spending on bus transit in the short and long run.


  1. III.         Data


To study how the introduction and expansion of rail transit systems in other U.S. cities have affected funding and support for bus systems, I use National Transit Database (NTD) data on total operating expenses by transit system from 1991-2011, separated by transit mode (TS2.1, 2013).  The NTD collects data on all transit systems that receive grants from the Federal Transit Authority.  I also use data from the World Bank on economic indicators such as GDP and per capita GDP.


Of the 896 urbanized areas (UZAs) listed in the NTD, 39 simultaneously operated bus and rail systems at some point between 1991 and 2011.  Of those, 14 have rail systems that opened during that period, thereby providing data on bus expenditure patterns before and after the rail systems’ openings (see Appendix Table A1).  With the exception of Phoenix’s Valley Metro, the opening of a rail system was never coincident with any substantial decline in bus expenditures.  Further, rail openings do not appear to stall the gradual increase in bus expenditures over time that most AZUs exhibit (see Appendix Figure A2).


III.I. Measuring changes in operating expenditures over time


Operating expenditures were studied rather than capital or total expenditures, as they provide a more direct indicator of service expansions: as rail infrastructure investments are being made, its capital expenses may span unpredictably across many years in advance, but operating expenses will only increase once the new lines open.  Operating expenditures also lack the volatile and cyclic fluctuations characteristic of capital expenditures: Triangle Transit’s operating expenditures changed by only 11 percent from 2011 to 2012, while its capital expenditures changed by 48 percent (TTA; see Appendix Figure A1).


Using this NTD data, I calculate each year’s growth in operating expenses for bus and rail (ΔOpexBus and ΔOpexRail) for each AZU.  Given year i and AZU j,


ΔOpexBusij = OpexBusijOpexBusi[j-1]


ΔOpex is preferred over the given year’s actual operating expenses, as using the data to study the relationship between current bus and rail operating expenses primarily would capture between-city differences rather than within-city changes.  Further, it is insufficient to find whether increasing rail expenditures causes decreases in bus expenditures, as a rail system might stall the bus system’s growth without actually causing it to contract.  Using annual change-over-time values for bus and rail operating expenses addresses both of these limitations, as the ΔOpex captures only within-city changes and can itself be compared between cities.


Since ΔOpexRail is usually highest when an AZU has just opened or expanded its rail system, a negative relationship between ΔOpexRail and ΔOpexBus would suggest that Triangle Transit’s rail system will hurt the bus system by at least stalling its growth.  A neutral or positive relationship would suggest that Triangle Transit’s bus system would not only continue to grow after the introduction of the rail line, but its rate of growth would stay the same or even increase.


III.II. Three time intervals to measure short-term and long-term effects


The effect of rail expenditures may not be instantaneous.  A transit system may choose to construct or expand its rail network without making any cuts to its bus services, only to find its newfound deficit unsustainable.  Furthermore, the new rail network may consume future investment resources that the transit system would have otherwise channeled into its bus network.  In either case, the growth of a rail system may have no immediate impact on buses, but it may still have a lagged effect.  Therefore, the empirical analysis is repeated three times, each using a different time interval (Δt):

Δt = 1 year:     ΔOpexj = Opexj – Opex[j-1]

Δt = 2 years:    ΔOpexj = Opexj – Opex[j-2]

Δt = 3 years:    ΔOpexj = Opexj – Opex[j-3]


Each AZU contributes one observation for each data-year where both OpexBusij > 0 and OpexRailij > 0.  This method records all incidents where an AZU ended the one-, two-, or three-year time interval with both bus and rail systems running, regardless of whether the bus or rail systems existed at the beginning of the interval.  Meanwhile, it excludes all incidents where an AZU started and ended the interval with no rail system or no bus system.  In all, there were NΔt=1 = 780 observations for Δt = 1, NΔt=2 = 741 for Δt = 2, and NΔt=3 = 702 for Δt = 3.


  1. IV.          Empirical model


To study rail transit’s effect on buses, I construct an ordinary least-squares regression model:


ΔOpexBusij = β0 + β1ΔOpexRailij + β2Xij + β3Yi + β4YEARi + β3STATEj + uij


            Xij is a set of population factors, including UZA population, population growth rate, area, and population density.  Yi is a set of economic factors, including national per capita GDP and per capita GDP growth rate.  YEARi and STATEj are sets of dummy variables for each year and state, where Yr_11ij and State_WIij are their respective omitted reference variables.  (See Appendix Table A2 for a complete list of variables).

  1. V.             Results


The coefficient for ΔOpexRailij is both positive and significant at the 0.01 level for all three time intervals.  Thus, the rise in bus expenditures illustrated in Figure A2 (Appendix) does not merely reflect the expansion in both rail and bus services in response to some confounding factor, such as population or economic growth.  Even after controlling for population and economic factors, bigger increases in rail operating expenses predict bigger increases in bus operating expenses.  Furthermore, the coefficient increases as the time interval widens, meaning rail expenses’ acceleratory effect on bus expenditures strengthens over time (see Table 2; see Appendix Table A3 for unabridged regression results).


Table 2 – Selected regression results



Coefficient (Standard error)

  Δt = 1 Δt = 2 Δt = 3
ΔOpexRailij 0.66385




1.200903 (0.05365)*
UZA_Popij 6.589327




18.14565 (3.195444)*
UZA_Areaij -18828.6




-72415.86 (13212.49)*
UZA_Densityij 7452.779




23619.98 (6145.469)*
UZA_Growthij 1.02 × 109

(2.58 × 108)*

1.90 × 109

(3.68 × 108)*

2.60 × 109

(4.59 × 108)*

GDPi -682.802




-1537.338 (537.6729)*
GDP_Growthi 1.72 × 108

(6.64 × 107)*

2.67 × 108

(9.29 × 107)*

4.35 × 108

(1.82 × 108)**

Constant -7626747

(1.73 × 107)

-4.17 × 107

(2.48 × 107)***

-3.31 × 107

(2.99 × 107)

* Statistically significant at the 0.01 level

** Statistically significant at the 0.05 level

*** Statistically significant at the 0.10 level


The coefficients for UZA_Popij, UZA_Densityij, and UZA_Growthij (population growth rate) are all also positive and significant at either the 0.01 or 0.05 levels for all three time intervals.  Given that bus expenditures generally increase over time in this data set, this finding means that bus expenditures rise at a faster rate in more populous, dense, and faster-growing UZAs.  In contrast, the coefficient for UZA_Areaij is negative and significant at the 0.01 level for all three time intervals.  Interestingly, bus expenditures rise faster for smaller urbanized areas.


The impact of gross domestic product on ΔOpexBusij varies by time interval.  The coefficient for GDPi­ is significant at the 0.05 level under the single-year time interval and at the 0.01 level under the three-year time interval, but it is not significant at any level under the two-year time interval.  It is curiously negative, suggesting that bus expenditures rise more quickly when GDP is lower.  In a seemingly contradictory manner, the coefficient for GDP_Growthij is positive and significant at the 0.01 level for all three time intervals.  It is possible that GDP_Growthij has captured the effects of acute economic expansions and contractions, while GDPij reflects a longer-term relationship between GDP and public bus usage.

  1. VI.          Discussion


While bus transit remains a public service on which many urban poor rely on, the results from this study’s regression predict that Triangle Transit’s planned rail project will not harm Durham’s poor by triggering any bus service reductions.  Instead, the results suggest that the project will provide a net benefit for the bus system.  One plausible explanation for this is that various transit modes may act as complements to one another.  As rail networks develop, public transit in general becomes a more viable and convenient option for choice riders.  Overall transit ridership subsequently increases in the area, and the heightened bus ridership allows the AZU to expand its bus services.  Through this mechanism, rail and bus networks expand together as they work to foster a stronger transit network and community.  Thus, while a rail project may be targeted at choice riders originally, it could trigger a positive spillover effect that ultimately benefits captive riders as well.


This view is consistent with the finding that bus expenditures rise more quickly when per capita GDP is lower.  During times of economic distress, choice ridership is expected to grow more easily, as private vehicle owners respond more readily to the financial incentives offered by public transit.  It is also consistent with the finding that bus expenditures rise more quickly in denser regions.  Increased population density offers additional incentives for choice ridership, as amenities would be closer to households on average and as traffic would be more congested.


Additional research could be done to test for this rail-initiated spillover of benefits from choice riders to captive riders.  A fundamental component in that investigation would be to see whether rail projects can effectively attract choice riders, and whether the choice riders cause a significant increase in bus ridership.


Ultimately, the potential for Triangle Transit’s rail project to have a positive effect on Durham’s poor will depend on several factors.  First, the rail network’s construction and operation must be fully funded by new sources of revenue, so as to not pull resources away from TTA’s existing bus services.  In the long run, the rail must be able to draw in more choice riders from Wake and Orange Counties who would then utilize Durham’s bus system for shorter point-to-point transportation.  This increase in Durham bus ridership could lead to bus service expansions that benefit choice and captive riders alike.







Figure A1 – TTA total annual operating and capital expenses from 2004-2012



Table A1 – UZAs with rail lines opened after 1991

UZA Name Transit System Name Rail Opened
Baltimore, MD Maryland Transit Administration 1992
Memphis, TN-MS-AR Memphis Area Transit Authority 1993
St. Louis, MO-IL Bi-State Development Agency 1993
Denver-Aurora, CO Denver Regional Transportation District 1994
Dallas-Fort Worth-Arlington, TX Dallas Area Rapid Transit 1996
Salt Lake City-West Valley City, UT Utah Transit Authority 1999
Kenosha, WI-IL Kenosha Transit 2000
Seattle, WA Central Puget Sound Regional Transit Authority 2003
Houston, TX Metropolitan Transit Authority of Harris County, Texas 2004
Minneapolis-St. Paul, MN-WI Metro Transit 2004
Little Rock, AR Central Arkansas Transit Authority 2004
Charlotte, NC-SC Charlotte Area Transit System 2007
San Diego, CA North County Transit District 2008
Phoenix-Mesa, AZ Valley Metro 2008

Source: National Transit Database (TS2.1, 2013)


Figure A2 – Bus transit operating expenses before and after rail opening



Table A2 – List of regression variables


Variable name




Change in annual operating expenses of bus transit system, from year j – {1,2,3} to year j

ΔOpexRailij Change in annual operating expenses of rail transit system, from year j – {1,2,3} to year j
UZA_Popij AZU population
UZA_Areaij AZU area, in square miles
UZA_Growthij AZU population growth rate
UZA_Densityij AZU population density, in people per square mile
GDPi U.S. per capita gross domestic product
GDP_Growthi U.S. per capita GDP growth rate
YEARi Dummy variables for each year (e.g. Yr_92­i = {1 if i = 1992; 0 otherwise})
STATEi Dummy variables for each state (e.g. State_NCj = {1 if j is in North Carolina; 0 otherwise})

Note: Not all states have an AZU with both bus and rail transit systems, and STATEi dummy variables exist only for those that do.


Table A3 – Unabridged regression results



Coefficient (Standard error)

  Δt = 1 Δt = 2 Δt = 3
ΔOpexRailij 0.6638503




1.200903 (0.05365)*
UZA_Popij 6.589327




18.14565 (3.195444)*
UZA_Areaij -18828.55




-72415.86 (13212.49)*
UZA_Densityij 7452.779




23619.98 (6145.469)*
UZA_Growthij 1020000000


1900000000 (368000000)*


2600000000 (459000000)*
GDPi -682.8017




-1537.338 (537.6729)*
GDP_Growthi 172000000


267000000 (92900000)*


435000000 (182000000)**


State_AZj -1103388




13100000 (15900000)


State_ARj 4812837


8414082 (16800000)
State_CAj -18200000


-40300000 (10400000)* -61300000 (12900000)*
State_COj -7794084




-26400000 (15800000)***
State_CTj 17000000




42700000 (16300000)*
State_FLj -9615875




-15200000 (15700000)
State_LAj 23400000




36700000 (17800000)**
State_MDj 10300000




14400000 (14900000)
State_MAj 2476362




-10100000 (12700000)
State_MIj 20600000




69400000 (16300000)*
State_MNj 12600000




40900000 (14800000)*
State_NJj 27400000




84100000 (16100000)*
State_NMj -10400000


-24600000 (13100000)*** -38700000 (16300000)**
State_NYj 36100000




69400000 (14100000)*
State_NCj -21600000


-36800000 (18100000)** -47700000 (22500000)**
State_OHj 20900000




58900000 (16200000)*
State_ORj -4859887




-25400000 (15700000)
State_PAj 34700000




96700000 (17000000)*
State_RIj 21600000




56200000 (16300000)*
State_TNj 5679868




15200000 (12800000)
State_TXj -8233170




-15900000 (11700000)
State_UTj -8053100


-22100000 (13000000)*** -37400000 (16100000)**
State_WAj 20900000




63000000 (14700000)*
State_WIj (omitted: reference state)
Yr_92i 17200000


Yr_93i -11600000




Yr_94i -18300000




28600000 (11200000)**
Yr_95i -27200000




-11600000 (12300000)
Yr_96i -21100000




-18300000 (12100000)
Yr_97i -12400000




-27200000 (11800000)**
Yr_98i -9379006




-21100000 (11600000)***
Yr_99i 1298334




-12400000 (11400000)
Yr_00i 7351299




-9379006 (11200000)
Yr_01i 15800000




1298334 (11000000)
Yr_02i 11400000




7351299 (10900000)
Yr_03i 16500000




15800000 (10900000)
Yr_04i 20000000




11400000 (10900000)
Yr_05i 36000000




16500000 (10900000)
Yr_06i 33400000




20000000 (11000000)***
Yr_07i 10800000




36000000 (11200000)*
Yr_08i 742124.7




33400000 (11600000)*
Yr_09i 28600000




10800000 (12100000)
Yr_10i -11600000




742124.7 (12500000)
Yr_11i (omitted: reference year)
Constant -7626747







Asterisks denote statistical significance at the 0.01 (*), 0.05 (**) and 0.10 (***) levels.


Works Cited


Baum-Snow, Nathaniel, and Matthew E. Kahn. “The Effects of New Public Projects to Expand Urban Rail Transit.” Journal of Public Economics 77.2 (2000): 241-63.

Freemark, Yonah. “In North Carolina’s Triangle, the Passage of a Sales Tax Increase in Durham Is Just the First Step.” The Transport Politic. The Transport Politic and Yonah Freemark, 9 Nov. 2011. <http://www.thetransportpolitic.com/2011/11/09/in-north-carolinas-triangle-the-passage-of-a-sales-tax-increase-in-durham-is-just-the-first-step>.

Garrett, Mark, and Brian Taylor. “Reconsidering Social Equity in Public Transit.” Berkeley Planning Journal 13 (1999): 6-27.

“GDP per Capita (current US$).” The World Bank | Data. The World Bank Group, n.d. <http://data.worldbank.org/indicator/NY.GDP.PCAP.CD>.

Grubb, Tammy. “Sales Tax Rises a Half-cent Monday.” The Chapel Hill News. The News & Observer Publishing Company, 31 Mar. 2013. <http://www.chapelhillnews.com/2013/03/31/75668/sales-tax-rises-a-half-cent-monday.html>.

Iseki, Hiroyuki, and Brian D. Taylor. “The Demographics of Public Transit Subsidies: A Case Study of Los Angeles.” Presented at the TRB 81st Annual Meeting (2001).

Mildwurf, Bruce. “Triangle Transit Unveils Plans for Durham-Orange Light Rail.” WRAL.com. Capitol Broadcasting Company, Inc., 3 May 2012. <http://www.wral.com/news/local/story/11059619>.

Roberts, Mark. “Triangle Rail Project Won’t Receive Federal Funding.” WRAL.com. Capitol Broadcasting Company, Inc., 6 Feb. 2006. <http://www.wral.com/news/local/story/148290>.

TTA. “Triangle Transit Publications.” Triangletransit.org. GoTriangle, n.d. Web. <http://www.triangletransit.org/news/publications>.

“TS2.1 – Service Data and Operating Expenses Time-Series by Mode.” National Transit Database. Federal Transit Administration, 27 Jan. 2013. <http://www.ntdprogram.gov/ntdprogram/data.htm>

“What Is the National Transit Database.” National Transit Database. Federal Transit Administration, 26 Jan. 2013. <http://www.ntdprogram.gov/ntdprogram/ntd.htm>

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