tucker_smith_trafficcongestionpaper

Running Head: INVESTIGATING TRAFFIC CONGESTION IN LARGE MSA’s 1

Investigating Contributors and Combatants of Traffic Congestion in Large Metropolitan Areas

Tucker Smith

Furman University

INVESTIGATING TRAFFIC CONGESTION IN LARGE MSA’s 2

AbstractAs the United States as grown into an automobile-dependent country, congestion and

sprawl have grown into one of the most significant problems that cities face. A recent

resurgence in public transit investment brings about the need to investigate the relationships

between congestion, sprawl and public transportation. This study proposes that public

transportation usage alleviates annual per commuter traffic delay, while sprawling development

contributes to it. Using cross-sectional data from seventy-one of the one hundred largest

metropolitan statistical areas in 2010, Ordinary Least Squares regression results affirm this

study’s hypothesis regarding public transportation and add to the conflicting available evidence

regarding sprawl: while at low levels, sprawl is found to reduce congestion, past a certain

threshold it contributes to congestion, indicating that traffic delay levels are polarized at ultra-

dense and extremely sprawling areas.


I. Introduction

In 2014, the average American motorist spent forty-two hours idling in traffic delays,

wasting 3.1 billion gallons of gasoline in the aggregate. These measures have dramatically risen

from 18 hours and .6 billion gallons, respectively, in 1982–when gasoline was only ninety-one

cents, an average new car cost less than eight thousand dollars, and Time Magazine awarded

“The Computer” its Man of the Year distinction (Schrank, Eisele, Lomax, and Bak, 2015; “What

happened in 1982,” 2016). Due to this rapidly increasing burden of traffic congestion associated

with a growing population, federal, state, and local governments constantly invest in

transportation infrastructure. Recent emphasis on transportation investment has been met with a

broad coalition of support: between 2003 and 2014, over three-quarters of transit ballot measures

were approved by voters (Grisby, Neff, and Dickens, 2014) . However, these expansive

undertakings nationwide have accrued significant bills for the American people. Public spending

on transportation infrastructure investment, maintenance, and operations cost $279 billion in

2014 (Music and Petz, 2015). This is due in part to discretionary grant spending by local and

state governments on transportation and economic development surpassing all other categories

other than health care (“Policy basics: Non-defense discretionary programs”). Given the


budgetary prioritization on combatting sprawl and congestion, the systematic array of factors

contributing to traffic delays should be investigated. This research attempts to provide more

clarity on the subject by examining the relationship between congestion, public transportation

investment, and sprawl in metropolitan statistical areas (MSA’s). By offering commuters

alternative travel methods, and thus decreasing road traffic volume, public transportation options

should reduce congestion levels. However, the potential for reverse-causality in a congestion

model looms: a metropolitan area would invest in public transportation systems, and more

workers would commute via these systems, if the city had a significant congestion problem.

This study utilizes a mixture of control instruments, including a time-lag model for growth, in

order to account for this possible endogeneity. Furthermore, in many cases congestion is

worsened by severe urban sprawl that is characterized by low-density development and the

dilution of workers, jobs, and resources to outside of a city’s central business district. Suburban

residents making daily commutes to the central city clog highway lanes, potentially resulting in

long traffic delays and higher levels of congestion. Seeking to clarify the nature of the

relationships between congestion, sprawl, and public transportation, this study provides

empirical research on congestion levels in the United States’ one hundred largest metropolitan

statistical areas of 2010.

The nature of congestion in U.S. metropolitan areas can be traced back to the country’s

dependence on the automobile. While the invention of the automobile has been one of the most

important developments worldwide over the past century, the car has been especially prevalent

and impactful in the United States. Over 21% of automobile sales in 2015 occurred in the United

States–nearly five times its proportion of the world population, and while light-vehicle sales

declined worldwide in 2015, they reached an all-time high in the U.S. of 17.47 million (Phillips,


2016; Stoll, 2016). America’s love affair with the car has been highlighted by the rise of iconic

private-sector giants: Exxon Mobile and General Motors were the only two companies to top the

Fortune 500 from its creation in 1955 until 2002 (“Fortune 500 archive,” 2016). However, the

federal government has played an equally important role in the rise of the automobile. Between

1952 and 1970, the U.S. government spent eight times more on highways than rail systems

(Jovanovic, 2015). The resulting difference between U.S. and world automobile use was clear

even by 1980: in a study of 32 international cities, the nine U.S. cities examined all consumed

more fuel than any other city, while as a whole also having the lowest public transportation

usage and walking rates (Newman, 1996). The country’s dependence on the automobile has left

metropolitan areas susceptible to a significant degree of traffic congestion and associated

problems–ranging from environmental and health effects to productivity drags.

As the United State’s dependency on automobiles has grown, so has the level of traffic

congestion in large metropolitan areas. The average commuter spent 53 hours in traffic delay in

the 101 largest U.S. metropolitan areas in 2014, costing a mean of $1,190 in time and fuel

wasted. Signaling that the problem is still growing, 95 of the largest 100 MSAs suffered

increased congestion levels from 2014 to 2015 (Schrank et al., 2015). In addition to these

personal costs, congestion’s potential societal effects include increased greenhouse emissions,

greater risk of chronic disease, and a drag on productivity growth (Kaida and Kaida, 2014; Levy,

Buonocore, and von Stackelberg, 2010; Sweet, 2013). Simultaneous to the costs of congestion,

American dependence on automobiles has also been characterized by the phenomenon of urban

sprawl. Smart Growth America defines sprawl as “the process in which the spread of

development across the landscape far outpaces population growth” and identified four

dimensions of a landscape that has succombed to sprawl: a population that is widely dispersed in


low-density development; rigidly separated homes, shops, and workplaces; a network of roads

marked by huge blocks and poor access; and a lack of well-defined, thriving activity centers,

such as downtowns and town centers” (Ewing, Pendall, and Chen, 2002). Sprawl’s adverse

social costs mirror those of congestion: greater sprawl is correlated with more traffic fatalities, an

increase in chronic diseases, and worse air quality (Ewing et al., 2003; Sturm and Cohen, 2004).

In response to concerns over congestion and sprawl, cities across the United States have

invested in alternative transit options, with the aid of federal and state governments. In February

of 2009, the Department of Transportation’s Transportation Investment Generating Economic

Recovery (TIGER) grants gave $1.5 billion of funding to 51 transit projects nationwide, a mere

fraction of the requests totaling $60 billion in worth (Mortice, 2016). Pending congressional

approval, the Grow America Act would further invest $115 billion in transit systems over six

years, including light rail, streetcars, and rapid bus transit (“The Grow America Act,” 2015).

This immense pending investment would increase transit spending by 76% and at the surface

appears justified: 10.8 billion trips were taken on U.S. public transportation in 2014, the highest

amount in 58 years (“The Grow America Act,” 2015; Miller, 2015). Still, public transit is

unavailable to 45% of American households, and the American Society of Civil Engineers

assigned the country a D rating for transit infrastructure in 2013 (“2013 report card for

America’s infrastructure,” 2013). In order to determine if these are worthwhile investments, the

relationship between public transportation usage, sprawl, and congestion should be thoroughly

investigated.

II. Review of Previous Literature

Conventional wisdom dictates that public transportation usage reduces congestion, while

sprawl contributes to it. This belief is clearly acted on, as evident by the previously outlined


fiscal emphasis on public transit investment; however, empirical research on the relationships

between public transportation, sprawl, and congestion has yielded mixed results. Findings have

diverged based on the choice of measure for congestion, the potential for endogeneity, and the

inclusion of various control instruments. This section will briefly outline the nature of these

three problems and review previous literature regarding the effects of both public transportation

and sprawl on congestion at the metropolitan or urbanized area levels.

Although there is no consensus choice of measurement for analyzing congestion, three

metrics stand out for their popularity and validity. The simplest and easiest metric to

comprehend is mean commute time, “the average journey-to-work travel time,” which can be

obtained from the US Census Bureau (Sarzynski, Wolman, Galster, and Hanson, 2006). Another

prevalent measure, average daily traffic per lane (ADT/lane), measures congestion by traffic

volume rather than time and is presented in the Federal Highway Administration’s Highway

Performance Monitoring System (HPMS). Developed and calculated by the Texas

Transportation Institute (TTI), the per computer number of hours annually spent in traffic delay

has caught on as an effective indicator of the tangible effects of congestion on commuters

(Sarynski et al., 2006). However, all three methods have been criticized for significant

shortcomings, since they are all averaged across both time and space, which may result in

underestimating congestion at the busiest hours and roads. In particular, ADT/lane fails to

capture peak congestion in that it measures total traffic volume throughout the day, which

Sarynski et al. present this as a strength of ADT/lane (2006). This is a poor interpretation of the

metric and congestion: ADT/lane portrays a uniform travel distribution as equal to a distribution

largely polarized at peak hours, which would have a far greater impact on commuters. Commute

times and the TTI’s traffic delay metric both portray congestion’s presence at the busiest hours,


and thus, its real effect on commuters. Since commute times are self-reported, and therefore,

potentially less reliable, this study favors traffic delay as the best available measure of

congestion. More importantly, since the mean commute time for U.S. public transit users is

double that of solo drivers (Zou, 2015), a model using commute times as an exogenous variable

would reflect this divide rather than public transportation usage’s effect on congestion itself.

This decision is critical, because it can affect the sign, significance, and interpretation of

estimated coefficients (Sarynski et al., 2006).

Another primary reason for the disparate nature of empirical analysis on public

transportation, sprawl, and congestion is the steep degree of potential for endogeneity and

inclusion of diverse instruments to attempt to counteract this. Although sprawl–according to

prevailing theory–contributes to congestion by yielding longer commutes along a limited supply

of highways, the presence of congestion could also incentivize residents to move farther away

from clogged streets to less dense areas; similarly, while transportation usage combats

congestion by reducing the volume of traffic road networks, in a cross-sectional model one

would also expect public transport usage to be higher in the presence of severe congestion.

Various instruments are included in congestion models in attempt to control for reverse-causality

in either of these cases, including economic growth and housing costs. Introducing a time-

lagged measure of economic growth controls for any congestion that could be caused by a rising

supply of commuters outpacing the growth of available roadways and other accommodating

transportation systems. This phenomenon is likely to occur in areas that experience relatively

high economic growth, as both more initial residents are likely to acquire employment and other

potential commuters will migrate to the MSA due to the greater employment opportunities.

Furthermore, a measurement of housing costs would internalize the effect of higher rents near


the central business district (CBD) pushing residents further out into the suburbs, which could

augment sprawl, limit proximity to public transportation, and conceivably increase congestion

levels. If endogeneity is not properly accounted for in a model, coefficients can be significantly

biased and misrepresent the true effect of public transportation and sprawl on congestion.

Dominant theory regarding sprawl and congestion asserts that low-density development

induces longer trips and dependency on automobiles (Ewing, 2003; Downs, 1992); hence,

empirical research should find positive correlations between sprawl and congestion. Due to the

difficulty in measuring sprawl, insufficient literature is available on the subject. Ewing et al.

developed the original version of the sprawl index employed in this study and investigated 83 of

the largest U.S. MSA’s (2013). The authors found that sprawl was positively related to average

vehicle ownership and daily vehicular miles traveled per capita, adding empirical evidence to the

mechanism through which sprawl could contribute to congestion (Ewing et al., 2003). Using a

sprawl index consisting of land use mix, centrality, and compactness to capture the multifaceted

nature of sprawl, McCann found that household transportation expenditures were $1300 higher

in more sprawling metropolitan areas (2000). Performing similar analysis, Zolnik found little

evidence of any relationship between sprawl and transportation expenditures (2012). In either

case, although household transportation expenditure is not a valid measure of congestion, it

captures part of the consequences of congestion on commuters. Finally, Sultana and Webertook

a unique approach to examining sprawl by measuring the commute times in newly developed

versus older areas, with data for metropolitan areas from the 1980, 1990 and 2000 census (2013).

The author’s conclusion shed new light on how sprawling development ages: while commute

times were initially higher for residents in newly developed areas, they diminished as the areas

aged (Sultana and Weber, 2013).


Straying from conventional theory on land use’s relationship with congestion, Sarzynski

et al. proposed that dense urban structure was actually positively correlated with congestion, and

outlined seven factors of sprawl that may affect congestion: density, continuity, concentration,

centrality, proximity, mixed use, and nuclearity (2006). Varying depending on the measure of

congestion used, results did not convincingly strengthen the author’s counter-argument. Only

density and continuity were significantly positively related to ADT/lane and housing centrality

was significantly positively correlated with traffic delay per commuter, supporting that sprawl

actually combats congestion. On the other hand, housing-job-proximity was found to negatively

affect commute times (Sarzynski et al., 2006). Although three of the four significant coefficients

bolstered the author’s claim, the majority of coefficients for the identified factors of sprawl ere

insignificant and varied depending on the measure of congestion. Thus, Sarynzki et al. mainly

adds to the already inconclusive pile of evidence regarding sprawl’s relationship with

congestion, rather than asserting a well-supported alternative theory. Furthermore, the overall

presence of sprawl should be included rather than individual factors. Due to the extremely

interrelated nature of the factors, cities would have extreme difficulty implementing policy for

individual factors versus sprawl as a whole.

Unlike with sprawl, an abundance of literature exists examining public transportation’s

effect on congestion. One would expect that public transportation would be a direct substitute

for automobile usage and reduce congestion by replacing would-be street traffic volume with

public transportation usage. Many studies at the metropolitan or urban level uphold this theory,

depending on type of transit. Investigating the extensive effect of public transit by comparing

congestion levels with and without public transit options, Anderson utilized a quasi-natural

experiment in Los Angeles that originated from a transit labor dispute in 2003 and identified a


47% increase in average highway delay in absence of the public transit service (2014). In their

2006 study Winston and Langer analyzed the effects of public transportation expenditures on the

cost of congestion in 72 large urban areas from 1982 to 1996. The authors found that rail transit

was effective at reducing congestion costs, but that bus service expansions actually increased

congestion costs (Winston and Langer, 2006). Also focusing on rail transit, Baum-Snow and

Kahn studied 16 rapid rail transit systems in large cities that were either constructed or expanded

between 1970 and 2000, finding that the rail investments resulted in decreased commute times

but did not affect congestion levels (2005).

Empirical studies have also presented evidence opposing public transportation’s

congestion relief, complicating the understanding of the relationship between public

transportation and congestion. One theory that greatly threatens the notion that public

transportation can effectively combat congestion is the “fundamental law of traffic congestion,”

which proposes that through induced demand, in the long run transit investment will be

ineffective without an efficient congestion tax on auto travel (Beaudoin, Farzin, and Lawell,

2015; David and Foucart, 2014). In essence, many potential commuters are discouraged from

traveling during peak hours because of congestion itself, and while transportation investment

may reduce traffic volume in the short run, volume will increase in the long run until reaching

equilibrium congestion levels again (Hau, 1997). Durant and Turner used data from 228

metropolitan areas to inspect the validity of the fundamental law, yielding significant evidence of

induced auto demand: increases in road capacity were followed by increases in auto travel, and

the availability of public transit services did not affect auto travel volume (2011). The lack of

consensus in previous literature regarding both public transportation and sprawl’s effect on

congestion signifies that further research is necessary.


III. Data and Methodology

Figure 1: Variable Definition

Sprawl2010 The Smart Growth America Sprawl Index score for an MSA

(Metropolitan Research Center)

POP2010 An MSA's population in 2010 (U.S. Census Bureau)

PubTran2010 The % of commuters in an MSA that traveled to work via public

transportation (Brookings Institute)

TrafficDelay2010 The per capita annual hours spent in traffic delay in an MSA in 2010

(Texas Transportation Institute)

MedHCforRenters201

0

The medain housing costs for renters in an MSA (U.S. Census Bureau)

Growth____ The % change in real per capita growth in an MSA in the indicated years

(Bureau of Economic Analysis)

TrafficDelay2009 The per capita annual hours spent in traffic delay in an MSA in 2009

(Texas Transportation Institute)

Sprawl2 The value of Sprawl2010 squared for an MSA (Metropolitan Research

Center)

Figure 2: Summary Statistics


The dataset composed for this study consists of the one hundred largest U.S.

Metropolitan Statistical Areas for the year 2010. Metropolitan areas are chosen rather than

urbanized areas because sprawl’s effect can extend over large and even polycentric regions. Of

all MSA’s, the one hundred largest are included because congestion is a more relevant issue in

larger cities. However, due to lack of availability across all variables for some cities, the final

number of MSA’s included in the Ordinary Least Squares regression is 71. A variety of sources

are utilized for this study’s data, including the Census Bureau, the Brookings Institute, the Texas

Transportation Institute, the Metropolitan Research Center, and the Bureau of Economic

Analysis.


Applying Ordinary Least Squares regression analysis to this data set, this study seeks to

add more evidence illuminating the nature of the relationships between congestion, sprawl, and

public transportation. The following model is used:

TrafficDelay2010i=β0+β1Sprawli +β2PubTrani + β3 TrafficDelay2009 +β4Populationi

+β5MedianRenti+β6GMPGrowthit-3 +β7Sprawl2i+εi

Congestion is primarily measured as the Texas Transportation Institute’s annual per

commuter hours spent in traffic delay. The institute’s travel time index estimates speed of travel

during the busiest traffic and compares this to that of free-flow status. Utilizing this metric, the

number of hours of delay per year in an MSA is calculated and then divided by the number of

commuters to yield the per commuter annual traffic delay (Schrank et al., 2015). As previously

discussed, this study prefers traffic delay as the best available measure for congestion because it

portrays the effects of the worst congestion realized by commuters. In addition to the primary

variables of interest–sprawl and public transportation usage–this studies model includes the

previous year’s congestion levels, population, median rent, and a time-lagged growth in gross

metropolitan product as control variables.

Public transportation usage is measured as the percentage of daily commuters that travel

to work via public transportation, using data from the Brookings Institute’s Metropolitan

Indicator Map dataset. Public transportation demand–illustrated by usage rates–is evaluated

rather than transportation supply–represented by transit investment, because public transportation

demand is more likely to directly relate to congestion relief. Although policymakers are faced

with the decision to increase supply through transit investment, their overall goal is to capitalize

on transit demand. Including a measure of public transportation supply rather than demand

would downwardly bias public transit’s effect on congestion levels by not separating


underutilized types of public transportation from effective systems. Instead, by including usage

rates, this model will demonstrate the potential congestion relief, or lack thereof, of public

transportation that is actually utilized by commuters. In other words, this model could be applied

to portray the goal of calculated public transportation investment, rather than the investment

itself. A rise in public transportation usage signifies that commuters are traveling via public

transit, rather than other options–mainly via automobile. Thus, congestion should directly

decline through diminishing road traffic volume and this study expects β2 to be negative.

Figure 3: The Sprawl Index and Congestion

This study’s other variable of interest, sprawl, is quantified by Smart Growth America’s

sprawl index. Smart Growth America identifies four major factors of sprawl to incorporate into

their index: residential density; neighborhood mix of homes, jobs, and services; strength of

activity centers and downtowns; and accessibility of the street network . The index is configured

so that the mean value for all metropolitan areas is 100 and the standard deviation is 25. Given

that each of the four factors is indicative of compact development, low values of the overall


sprawl index portray low-density, sprawling development. Contrary to the perception of sprawl,

the index does not merely bias towards larger cities: the simple correlation coefficient between

the index and MSA population was .575 and significant with 99.9% confidence (Ewing and

Hamidi, 2014). This indicates that based on these four factors, larger cities actually

demonstrated lower degrees of sprawl. Sprawl2 is also included in this study’s model to account

for potentially quadratic nature of sprawl and congestion’s relationship–that past a certain

threshold of sprawl, its effect on congestion changes direction. Although this is clearly present

in the 2010 data set, as seen in Figure 2 the model should be applied to other years to test the

robustness of this result. However, data is only currently available for the sprawl index in 2010,

so such a test of robustness is outside the scope of this paper. Sprawl should contribute to

congestion in two ways: as residents move further out from the central business district, cities do

not have the resources to invest in a plethora far-reaching public transit options and roadways in

low-density areas. Hence, their transportation options are largely limited to the automobile,

which results in increased vehicle ownership and vehicular miles traveled (Ewing et al., 2003).

The combination of greater traffic volume and more limited transportation routes could result in

clogged roadways and increased traffic delay.

Four control instruments are employed to isolate the effects of sprawl and public

transportation usage on congestion. First, the previous year’s congestion level is incorporated

into the model, as leaving it out would be a clear source of bias. The previous year’s congestion

levels could encourage a resident to alter their transportation behaviors, thus affecting public

transportation usage. If congestion levels were high in the past, a commuter would be more

likely to travel via public transit in the future; on the other hand, low previous congestion levels

would make a worker more likely to commute via automobile. Meanwhile, the previous year’s


congestion levels would clearly be positively related to those of the current year. Through this

mechanism, reverse-causality and positive bias on the public transportation coefficient are

counteracted. The MSA’s population, taken from the 2010 Census, is included because

transportation infrastructure growth might not be able to keep up with traffic volume at high

population levels, and a large MSA could be more likely to sprawling residential development.

In order to account for the pricing differential of housing in the central business district and

suburbs of an MSA, the average housing costs for renters is added into the model, since renters

are more likely to be located in the central city core than the lower-density suburbs. The median

rent, using survey data from the Census Bureau, also internalizes the value of a specific city’s

amenities. In both cases, median rent could affect the likelihood for a resident to live closer to or

farther from the CBD, which would influence the degree of both sprawl and congestion, along

with the likelihood of access to public transportation. Finally, an MSA’s gross metropolitan

product from a previous period according to the Bureau of Economic Analysis–time lags of one,

three, and five years are used–is included to account for economic growth encouraging greater

immigration into a city and potentially higher levels of sprawl and congestion. Additionally, a

growing city would produce greater tax revenue and, therefore, have a greater ability to invest in

public transportation options for the future. If these investments proved effective, they would

result in greater public transportation usage; thus, including a lagged economic growth variable

should prevent negative bias on public transportation positive bias on β2.

IV. Discussion of Results

Figure 4: OLS Regression Results

(1) (2) (3)VARIABLES TrafficDelay2010 TrafficDelay2010 TrafficDelay2010


SPRAWL2010 -0.245* -0.254* -0.246*(0.185) (0.187) (0.181)

PubTran2010 -1.355** -1.335** -1.334**(0.788) (0.785) (0.773)

TrafficDelay2009 0.732*** 0.742*** 0.742***(0.153) (0.159) (0.161)

POP2010 1.04e-06* 1.02e-06* 1.02e-06*(6.50e-07) (6.56e-07) (6.54e-07)

MedHCforRenters2010 -0.00247 -0.00327 -0.00331(0.00885) (0.00742) (0.00723)

Growth0708 0.168(0.412)

SPRAWL2 0.00169* 0.00172* 0.00170*(0.00111) (0.00111) (0.00109)

Growth0506 0.0646(0.110)

Growth0910 -0.0264(0.441)

Constant 22.07 22.49 22.15(16.29) (16.40) (16.12)

Observations 71 71 71Adjusted R-squared 0.618 0.617 0.617

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

The results of this study add to the complicated assortment of empirical evidence

concerning the remarkably complex relationships between congestion, sprawl, and public

transportation. The model’s R2 of .6563 is significantly lower Sazrynski et al.’s .7837, which is

due to Szyrynski’s inclusion of five more explanatory variables than this study (2006).

Sazrynski fails to provide adjusted R2, as this study does, so proper comparison of fit is

impossible. Despite warranted concerns over endogeneity, public transportation usage was

negatively correlated with congestion at a five percent probability of type I error in all three

regressions. These results allows for the rejection of the null hypothesis and provides evidence

to the theoretical underpinnings of public transportation relieving congestion, extending the

dynamic in Anderson’s findings to a nation-wide data set (2014). The implication from the

coefficients themselves speaks to the power of effective public transit investment as a combatant


of congestion: a one-percentage point increase in public transit usage would reduce annual traffic

delay by over 80 minutes per commuter, a 3.3% decline from the average delay in the largest one

hundred MSA’s. Another critical takeaway is that there appears to be ample room for more

public transit investment, even in large metropolitan areas. If this were not the case, one would

expect public transportation usage to have a minimal marginal effect on congestion, as higher

usage rates would signify oversaturated public transit markets and prove ineffective at relieving

congestion.

At low levels, the sprawl-index was significantly negatively correlated with congestion at

with a p-value below .1 in every regression; however, Sprawl2’s significantly positive coefficient

indicates that past a certain threshold, sprawl actually reduced congestion. The positive

statistical significance of β2 at a 10% level of significance supports this study’s theory that

sprawl can raise congestion levels through increasing traffic volume on insufficient roadway

capacity; however, the positive β6 at a 10% level of significance conforms to Sarzynski et al.’s

mechanism of sprawl clearing up roads and diminishing congestion, rather than supplying to it

(2006). A high sprawl index score beyond the threshold at which the sign switches could be

indicative of a very dense area, where by Sarzynski et al.’s theory roadways cannot

accommodate the immense population of commuters (2006). The combination of these two

mechanisms, supported by this study’s empirical evidence, would suggest that congestion’s

relationship with sprawl is polarized: traffic delays are highest in both extremely compact central

cities and low-density poly-centric regions. Still, this study’s model needs to be applied to data

sets for other years in order to determine the validity of the inclusion of Sprawl2, since it was

chosen based on the preliminary nature of this data set.


Of the four other included variables, TrafficDelay2009 and POP2010 yielded statistically

significant positive coefficients in each model, helping to provide more information on what

affects congestion levels. GMP growth was not significant at any of the three time-lags, but still

was important to include due to its role in controlling for endogeneity and positive bias on

congestion. Once again, the model should be applied to other data sets in order to ascertain if the

three-year time lag’s slightly superior fit is simply an anomaly or a general rule. Median rent’s

insignificance further complicates the interpretation of sprawl and congestion’s relationship,

since relatively high housing costs in the central business district of an MSA compared to its

suburbs would theoretically result in sprawling development, but in this case is not correlated

with higher congestion levels.

Figure 5: Simple Correlation Coefficients

Empirical tests were run to test for heteroscedasticity and severe multicollinearity in the

model, which could be problems due to the cross-sectional nature of the dataset and complex

interrelations between explanatory variables. A White test initially resulted in rejecting the null

hypothesis of homoscedasticity, so each regression was re-run with heteroscedasticity-corrected

error terms. Figure 4 displays these results, which were used for hypothesis tests so that

incorrect conclusions were not made. A simple correlation coefficient matrix, presented in

Figure 5, portrays that severe collinearity did not exist between any two variables, except for


Sprawl and Sprawl2. This was expected and is not a problem, since the two variables are

exponentially related to one another and OLS still yielded significant coefficient estimates for

both variables even with possible high standard errors. Analysis of more complex

multicollinearity is performed using variance inflation factors (VIF’s) for each variable in all

three regressions. Once again, only VIF’s for Sprawl and Sprawl2 surpassed the threshold of 5.

Overall, the statistical significance of this study’s variables of interest indicate that

multicollinearity is not a severe problem.

V. Conclusion

This study examines the relationships between traffic congestion, public transportation,

and sprawl in large metropolitan areas and finds that while public transportation usage clearly

alleviates traffic delay through replacing vehicular traffic volume with transit ridership, sprawl’s

effect on congestion is not as easily deciphered. At lower levels of sprawl, a greater degree of

sprawl is associated with decreased congestion; however, past a certain threshold, sprawl’s effect

reverses. This supplements the results of Sarzynski et al., rather than overturning them, and

indicates that congestion, as measured by traffic delay, may be polarized in both ultra-dense and

highly sprawling areas (2006).

Policy implications based on both of these results should be taken with caution. When

considering public transportation, local or state governments should not approve an investment

without confidence that the transit system will be substantially utilized, especially given the

immense cost associated with constructing or expanding a public transit system. This study

shows that if this is the case, then congestion can be alleviated, but makes no conclusions

regarding public transportation investment as a whole. Cities must also carefully choose

development policies that do not result in extreme density or sprawl if they wish to minimize


congestion. Such a task is much easier said than done, and the overall agglomeration benefits

associated with density most likely cancel out the costs of congestion in ultra-dense regions.

Other potential policies for combatting congestion include an efficiency tax on clogged roads,

which may be effective at achieving the socially optimal level of congestion when paired with

public transportation (Pawlak, 2012).

Further research on congestion should be undertaken in order to create more evidence

clarifying its contributing and alleviating factors. This study’s model should be applied to

additional years when data for the sprawl index is made available. Moreover, public

transportation should be specified by type, in order to shed light on which systems are the most

effective at combatting congestion and deserving of public investment. Finally, to determine if

combatting congestion is a worthwhile goal for cities, congestion’s economic, environmental,

and public health effects should continue to be investigated.


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