Oversight Hearing on "Perspectives on America's Transit Needs."


Prepared Statement of Mr. Wendell Cox
Visiting Fellow
Heritage Foundation

10:00 a.m., Tuesday, October 8, 2002 - Dirksen 538

Thank you for inviting me to testify today.

My name is Wendell Cox. I am an independent consultant headquartered in Belleville, Illinois, in the St. Louis area. I am also a visiting fellow at The Heritage Foundation. I must stress, however, that the views I express are entirely my own, and should not be construed as representing the position of The Heritage Foundation.

I was appointed to three terms on the Los Angeles Country Transportation Commission by Mayor Tom Bradley and was appointed to the Amtrak Reform Council by Speaker Gingrich. Earlier this year I served an assignment as a visiting professor at the Conservatoire National des Arts ET Metiers (CNAM), a French national university in Paris, conducting seminars and research on urban planning and transport.

I will share perspectives that you may not have heard before --- about how transit has little or no potential to address traffic congestion and how so-called "smart growth" promises to worsen traffic congestion while making housing less affordable especially for the nation’s lower income households who are disproportionately minority. These views are held by other professionals and academics as well, and they challenge what is considered to be the conventional wisdom in both transport and urban planning. I will, of course, be pleased to supply the Committee with additional details as requested.

Increasing Traffic Congestion, Declining Transit Market Share

It is painfully obvious to commuters in virtually all US urban areas that traffic is getting worse. This has been going on for some time, but has become much more critical in recent years. For example, the US Census Bureau reports that average work trip travel time increased 3.1 minutes nationally from 1990 to 2000, a rate four times that of 1980 to 1990. And, things are likely to get much worse (Figure #1).


Figure 1

For some time there has been a widely held view that transit has the potential to reduce urban traffic congestion. Indeed, that sentiment was part of the rationale behind making highway user fees available to transit in the 1982 reauthorization.

Yet, despite spending nearly $500 billion in subsidies at the federal, state and local level since 1960, transit’s share of urban trips has continued to trend downward. This is confirmed by the 2000 Census, which shows that transit’s share of work trips has reached a new low --- 4.6 percent (Figure #2), down more than 10 percent from 1990 (Table #1). While employment was increasing 13.2 million, transit work trip use declined nearly 23,000. Only two metropolitan areas with more than 1,000,000 population maintain a transit work trip market share of more than 10 percent (Table A-1).

Figure 2




Table #1

Work Trip Market Share by Mode: 1990 & 2000

Mode

1990

Share

2000

Share

Change

Change in Share

Car, Truck or Van

99,592,932

86.5%

112,736,101

87.9%

13,143,169

1.5%

Drove Alone

84,215,298

73.2%

97,102,050

75.7%

12,886,752

3.4%

Car Pool

15,377,634

13.4%

15,634,051

12.2%

256,417

-8.8%

Public Transit

6,069,589

5.3%

6,067,703

4.7%

(1,886)

-10.3%

Public Transit

5,890,155

5.1%

5,867,559

4.6%

(22,596)

-10.6%

Taxicab

179,434

0.2%

200,144

0.2%

20,710

0.1%

Motorcycle

237,404

0.2%

142,424

0.1%

(94,980)

-46.2%

Bicycle

466,856

0.4%

488,497

0.4%

21,641

-6.1%

Walk only

4,488,886

3.9%

3,758,982

2.9%

(729,904)

-24.9%

Other

808,582

0.7%

901,298

0.7%

92,716

-0.0%

Work at Home

3,406,025

3.0%

4,184,223

3.3%

778,198

10.2%

Workers 16 Years & Over

115,070,274

100.0%

128,279,228

100.0%

13,208,954

0.0%

Source: Data from US Census Bureau




The Transit Dilemma: Little Auto Competitive Service

This is not to suggest that transit does not play an important role. Make no mistake about it --- where transit provides auto-competitive service, people use it. To be auto-competitive, transit must be, at a minimum, time competitive with the automobile. The 2000 Census data indicates that transit work trips take considerably longer than auto work trips. The average transit work trip was 43 minutes, which compares to other modes (mainly auto) at 24.8 minutes. Transit work trips take longer than auto trips in all metropolitan areas with more than 1,000,000 population (Table A-2).

But where transit is auto-competitive, it is very successful. For example:


Figure 3

What all of this says is that transit is largely about downtown and to a lesser degree a small number of urban cores (Figure #4). Overall, only the New York and Chicago metropolitan areas maintain a transit work trip market share of more than 10 percent, with most of that concentrated in the core areas. In fact, more than one-half of the nations’ transit work trips are to locations within the central cities of New York, Chicago and the ten next largest central business districts. Thus, 3.1 million of the nation’s transit work trips are to a gross area of less than 600 square miles, while the balance of 2.8 million transit work trips are to the other more than 86,000 square miles of urbanized land. Thus, outside trips to downtown, transit is able to make little or no difference with respect to traffic congestion.

In most major metropolitan areas, auto-competitive transit service is limited to downtown service. This is illustrated by Portland (Oregon), which has the nation’s most aggressive smart growth policies and has nearly doubled transit service in the last decade. Transit competitive service is provided from approximately 70 percent of the urban area to downtown (where transit competitive is defined as 1.5 times auto travel times). Outside downtown employment locations are accessible to only five percent of the urban area by transit competitive service. People are not going to forsake their cars for transit service that takes too long, or transit service that doesn’t even exist.


Figure 4

It is not surprising, therefore, that people who use transit to non-downtown locations have much lower incomes than those able to access the auto-competitive services to downtown. In 1990, downtown transit commuters had an average household income within six percent of the national average. Non-downtown transit commuters had an average household income 40 percent below average. It would appear that transit is used for non-downtown work trips only by those who don’t have a choice (those who have no automobile available).

The key to getting people out of their cars is to provide automobile competitive service --- service that is competitive in travel time. But, as noted above, there is little auto-competitive service in the United States and little more planned to areas other than downtowns. And, despite their vertical impressiveness, downtowns represent a small and declining share of metropolitan employment in the United States. In 1990, the average downtown area accounted for barely 10 percent of metropolitan employment (Figure #5). Even Manhattan’s central business district, the second largest in the world, accounted for barely 20 percent of metropolitan New York’s employment.

Figure 5

The Union of International Public Transport is hardly the type of organization that would be expected to make critical comments about public transit. But this organization, the international equivalent of the American Public Transportation Association (APTA) put it this way:

In the United States, with the exception of New York, public transport is unable to compete with the automobile: its speed is half as fast, which means that door-to-door travel times, incorporating terminal distance times, waiting and transfer times, are 3 to 4 times longer on public transport."

Actually, this is something of an overstatement. Transit plays an indispensable role in providing auto-competitive service to a few much focused areas of the nation. But outside these areas, the potential for transit to attract people out of cars is nearly non-existent.

This is illustrated by the record of metropolitan areas that have built new rail systems...

Figure 6

The problem is further illustrated by the case of Minneapolis-St. Paul, which is currently building a light rail line (the "Hiawatha Line") and seeks to build a commuter rail line (the "Northstar Line"). There, the Texas Transportation Institute estimates that it would take an addition of 84,000 annual transit one-way peak period riders just to stop to growth of traffic congestion. The two lines would add fewer than 9,000 one-way transit riders over a period of 20 years or more. During the 1990s, transit work trip use increased approximately 200 annually, a small fraction of what would be required to materially impact traffic congestion (Figure #7).

Figure 7

Urban rail systems are exceedingly expensive. Often the annual cost per commuter attracted from the automobile exceeds the recurring lease cost for a new automobile.




Table #2

Work Trip Market Share in Dallas County: 1990-2000

Mode

1990

Market Share

2000

Market Share

Change

Change in Market Share

Drive Alone

718,709

76.2%

777,372

74.8%

58,663

-1.8%

Car Pool

135,776

14.4%

167,270

16.1%

31,494

11.9%

Transit

38,150

4.0%

35,261

3.4%

(2,889)

-16.1%

Walk

19,027

2.0%

17,390

1.7%

(1,637)

-17.0%

Other

11,004

1.2%

13,108

1.3%

2,104

8.2%

Work at Home

20,480

2.2%

28,378

2.7%

7,898

25.8%

Total

943,146

100.0%

1,038,779

100.0%

95,633

0.0%

Taxicab included in "Other"

Calculated from US Census Bureau data.




 

Table #3

Work Trip Market Share in Metropolitan St. Louis: 1990-2000l

Mode

1990

Market Share

2000

Market Share

Change

Change in Market Share

Drive Alone

912,509

79.7%

1,023,627

82.6%

111,118

3.6%

Car Pool

137,883

12.0%

122,219

9.9%

(15,664)

-18.1%

Transit

31,355

2.7%

28,675

2.3%

(2,680)

-15.5%

Walk

24,556

2.1%

20,131

1.6%

(4,425)

-24.3%

Other

10,881

1.0%

9,020

0.7%

(1,861)

-23.4%

Work at Home

27,152

2.4%

35,292

2.8%

8,140

20.1%

Total

1,144,336

100.0%

1,238,964

100.0%

94,628

0.0%

Taxicab included in "Other"

Calculated from US Census Bureau data.


Funding Imbalance

The modest returns from the nation’s new urban rail systems are evident when measured in terms of the cost per new passenger mile. From 1980 to 2000, incremental government expenditures (federal, state and local) on transit were $1.19 per incremental passenger mile, nearly 40 times that of streets and highways (Figure #8). And, while highway user fees and special imposts accounted for 75 percent or more of highway expenditures, transit user fees accounted for less than 30 percent of expenditures.

Figure 8

This spending imbalance is even more significant in some of the nation’s major urban areas. For example, through 2025, the Atlanta region will spend 55 percent of its transportation resources on public transit, while transit’s share of trips is expected to grow from only 2.6 percent to 3.4 percent (Figure #9).

Figure 9

The defining factor with respect to urban transport is that virtually all new travel demand is expected to be automobile related. Even Portland’s land use-transport planning agency, Metro, acknowledges this (Figure #10). The fundamental problem is that there is no transit system that can provide auto-competitive service to a significant share of destinations outside downtowns. This is true not only in the United States, and to a somewhat lesser degree even in Western Europe. Our research indicates that a transit system that provides auto-competitive service throughout the modern American urban area could cost as much as a metropolitan areas’ gross regional product.

Figure 10

But this does not mean that there is not a cost efficient role for strategies other than the single occupant automobile. The recent Census data indicates rays of hope. While overall transit work trip ridership was declining slightly, progress was made in much less costly modes, namely working at home (telecommuting) and car pooling. . Working at home increased 778,000 and car pooling increased 256,000, though registering a 3.4 percent market share loss (Figure #11).


Figure 11




Table #4

Telecommuting and Transit Work Trip Trend: 1990-2000

Metropolitan Area

Transit Change

Telecommuting Change

New York--Northern New Jersey--Long Island, NY--NJ--CT--PA CMSA

41,472

74,500

Los Angeles--Riverside--Orange County, CA CMSA

3,299

55,349

Chicago--Gary--Kenosha, IL--IN--WI CMSA

(42,131)

40,839

Atlanta, GA MSA

3,286

38,742

Boston--Worcester—Lawrence, MA--NH--ME--CT CMSA

29,223

33,125

Dallas--Fort Worth, TX CMSA

(567)

30,284

San Francisco--Oakland--San Jose, CA CMSA

27,049

27,915

Denver--Boulder--Greeley, CO CMSA

17,066

25,470

Total

78,697

326,224

Calculated from US Census data.

 

Table #5

Car Pooling and Transit Work Trip Trend: 1990-2000

Metropolitan Area

Transit Change

Car Pooling Change

Atlanta, GA MSA

3,286

92,022

Phoenix--Mesa, AZ MSA

8,116

81,827

Dallas--Fort Worth, TX CMSA

(567)

79,603

Seattle—Tacoma--Bremerton, WA CMSA

28,611

49,573

Las Vegas, NV--AZ MSA

20,940

48,561

Houston--Galveston--Brazoria, TX CMSA

1,643

40,049

Austin--San Marcos, TX MSA

3,313

32,889

San Francisco--Oakland--San Jose, CA CMSA

27,049

28,035

Denver--Boulder--Greeley, CO CMSA

17,066

27,499

Raleigh--Durham--Chapel Hill, NC MSA

1,985

26,728

Portland—Salem, OR--WA CMSA

22,152

25,947

Total

133,594

532,733

Calculated from US Census data.


One of the most promising developments has been the recognition by the Federal Transit Administration and some transit agencies of the much more cost effective options for rapid transit using buses. USDOT research has indicated that bus rapid transit can be five times as cost efficient per passenger mile.

Smart Growth: More Traffic Congestion, Less Housing Affordability

The "Smart Growth" movement seeks to stop or control urban sprawl. Proponents claim that it will reduce traffic congestion, reduce air pollution and reduce costs. As a result, there are proposals to impose land use regulations for controlling urban sprawl as in the federal transportation program. It is fundamental that smart growth and containing sprawl require higher densities. Smart growth’s goals simply are unattainable without much higher densities.

US urban areas tend to be less densely populated than those in Western Europe and Japan (Figure #12). But, contrary to the popular view, sprawl is not an American phenomenon. Sprawl occurs wherever there is population growth and rising affluence, and European urban areas have seen their urban densities decline at an even greater rate than in the United States (Figure #13).


Figure 12


Figure 13

I do not favor sprawl. I favor allowing people to live and work where and how they like. And, there is no reason not to allow it. Even today, nearly 400 years after Jamestown, urbanization accounts for less only 2.6 percent of the nation’s land area.

The claims of the smart growth movement simply do not hold up.

National and international data clearly indicates that traffic congestion rises with population density. The higher density European and Asia urban areas, with their much higher public transit market shares also have much worse traffic (Figure #14). Research commissioned by the United States Department of Transportation indicates that at current US urban densities, vehicle miles rise more than 80 percent when population density is doubled. Now, admittedly, that means that per capita driving declines marginally, but it means that there are more miles in a defined area --- traffic congestion is worse.


Figure 14

More driving per square mile means that traffic slows down and that people must spend more time in their cars. Not surprisingly, journey to work travel times tend to be longer where population densities are higher --- whether in the United States or internationally.

And, as traffic volumes in a particular area increase, there is also an increase in stop and go driving. Slower speeds and stop and go driving mean greater production of air pollution. So, not surprisingly, air pollution production tends to be higher where densities are higher. And, it is well to consider the great progress that has been made in air pollution abatement in the United States. In the last 30 years, driving has increased substantially, while criteria air pollution production has decreased --- not just per capita --- but overall.

So, smart growth increases traffic congestion, travel times and air pollution.

Some months ago research was published showing that transportation costs are higher in more sprawling areas. This is to be expected. But what may be surprising is that overall household expenditures tend to be lower where densities are lower. The big factor in this equation is housing costs. Housing costs are less where densities are less, and they tend to be less to such a great degree that the transportation cost disadvantage is more than canceled.

But, the worst impact of all is social. Home ownership is lower where densities are higher. Thus, smart growth works to make home ownership more difficult for lower income households. Recent decades shows than minority home ownership, (African-American and Hispanic), is rising faster than that of non-Hispanic whites (Figure #15). At the same time, minority home ownership levels still remain well below that of non-Hispanic whites, which is why the Bush Administration has undertaken steps to more greatly expand minority home ownership.


Figure 15

By raising the price of housing, smart growth promotes social inequity. Smart growth rations land and development. It is a fundamental principle of economics that when valuable goods are rationed, their prices rise. When prices rise, it is the lower end of the income spectrum that is driven away from the market. The lower income spectrum has a disproportionate representation of minorities. As a result, smart growth reduces home ownership opportunities for lower income households, especially African-Americans and Hispanics. There is a raging debate between supporters and opponents of smart growth about the extent to which home ownership is reduced by smart growth. We often hear from smart growth supporters that they way to compensate for smart growths reduction of home ownership is to provide greater amounts of affordable housing. Such proposals are no more than empty platitudes in view of the fact that, by some reports, current public resources are sufficient to provide housing assistance to barely one third of eligible recipients. In fact, recent research by Matthew Kahn of Tufts University indicates that African-American home-ownership tends to be higher in more sprawling urban areas (Figure #16). Further, research by Edward L. Glaeser and Joseph Gyourko, published by Harvard University found that much of the difference in housing affordability around the nation can be attributed to land regulation. It is not surprising that Oregon, with the nation’s most comprehensive smart growth regulations, experienced by far the greatest increase in housing values between 1990 and 2000 (Figure #17).

 


Figure 16


Figure 17

Thus, smart growth is promises to produce a more traffic impacted urban area and one that is less economically inclusive. It would be a mistake for the federal government to encourage such measures through the transportation program.

RECOMMENDATIONS

Three conclusions and three recommendations are suggested by the current situation and recent trends in urban transport.


 

Table A-1

Transit Journey to Work Market Share: Major Metropolitan Areas over 1,000,000: 1990-2000

Metropolitan Area

2000

1990

Change

Atlanta, GA MSA

3.5%

4.6%

-24.6%

Austin--San Marcos, TX MSA

2.5%

3.2%

-21.9%

Boston--Worcester--Lawrence, MA--NH--ME--CT CMSA

8.8%

9.7%

-8.8%

Buffalo--Niagara Falls, NY MSA

3.3%

4.5%

-24.8%

Charlotte--Gastonia--Rock Hill, NC--SC MSA

1.3%

1.7%

-24.8%

Chicago--Gary--Kenosha, IL--IN--WI CMSA

11.2%

13.4%

-16.4%

Cincinnati--Hamilton, OH--KY--IN CMSA

2.8%

3.6%

-20.3%

Cleveland--Akron, OH CMSA

3.3%

4.5%

-26.0%

Columbus, OH MSA

2.2%

2.7%

-17.1%

Dallas--Fort Worth, TX CMSA

1.7%

2.3%

-22.8%

Denver--Boulder--Greeley, CO CMSA

4.3%

4.0%

8.3%

Detroit--Ann Arbor--Flint, MI CMSA

1.7%

2.2%

-22.8%

Grand Rapids--Muskegon--Holland, MI MSA

0.8%

1.0%

-26.4%

Greensboro--Winston-Salem--High Point, NC MSA

0.8%

1.0%

-27.7%

Hartford, CT MSA

2.8%

3.6%

-23.3%

Houston--Galveston--Brazoria, TX CMSA

3.2%

3.7%

-13.3%

Indianapolis, IN MSA

1.3%

2.0%

-35.7%

Jacksonville, FL MSA

1.3%

2.0%

-32.5%

Kansas City, MO--KS MSA

1.2%

2.0%

-40.9%

Las Vegas, NV--AZ MSA

4.0%

1.9%

111.9%

Los Angeles--Riverside--Orange County, CA CMSA

4.6%

4.5%

1.7%

Louisville, KY--IN MSA

2.2%

3.1%

-30.9%

Memphis, TN—AR--MS MSA

1.7%

2.8%

-39.9%

Miami--Fort Lauderdale, FL CMSA

3.8%

4.2%

-10.8%

Milwaukee--Racine, WI CMSA

3.9%

4.8%

-18.8%

Minneapolis--St. Paul, MN--WI MSA

4.4%

5.2%

-15.6%

Nashville, TN MSA

0.9%

1.6%

-45.2%

New Orleans, LA MSA

5.3%

7.0%

-23.9%

New York--Northern New Jersey--Long Island, NY--NJ--CT--PA CMSA

24.1%

25.8%

-6.5%

Norfolk--Virginia Beach--Newport News, VA--NC MSA

1.8%

2.1%

-15.4%

Oklahoma City, OK MSA

0.5%

0.6%

-10.8%

Orlando, FL MSA

1.6%

1.4%

10.6%

Philadelphia--Wilmington--Atlantic City, PA--NJ--DE--MD CMSA

8.6%

10.1%

-14.6%

Phoenix--Mesa, AZ MSA

1.9%

2.0%

-4.6%

Pittsburgh, PA MSA

6.1%

7.9%

-22.4%

Portland--Salem, OR--WA CMSA

5.7%

4.8%

18.2%

Providence--Fall River--Warwick, RI--MA MSA

2.4%

2.5%

-5.2%

Raleigh—Durham--Chapel Hill, NC MSA

1.5%

1.8%

-17.9%

Rochester, NY MSA

1.9%

3.1%

-38.4%

Sacramento--Yolo, CA CMSA

2.7%

2.4%

13.0%

Salt Lake City--Ogden, UT MSA

3.0%

3.0%

0.1%

San Antonio, TX MSA

2.8%

3.6%

-21.7%

San Diego, CA MSA

3.3%

3.2%

3.2%

San Francisco--Oakland--San Jose, CA CMSA

9.4%

9.2%

1.8%

Seattle--Tacoma--Bremerton, WA CMSA

6.7%

6.1%

9.2%

St. Louis, MO--IL MSA

2.3%

2.8%

-18.4%

Tampa--St. Petersburg--Clearwater, FL MSA

1.3%

1.3%

-5.3%

Washington--Baltimore, DC--MD--VA--WV CMSA

9.2%

11.3%

-18.7%

West Palm Beach--Boca Raton, FL MSA

1.2%

1.1%

7.9%

Average

3.8%

4.3%

-12.3%

Taxicabs excluded

Minor geographical differences between 1990 and 2000

Calculated from US Census Bureau data.




Table A-2

Work Trip Travel Times: Major Metropolitan Areas Over 1,000,000: 2000 by Mode

Metropolitan Area

Mean Travel Time

(Minutes)

Travel Time: Not Public Transit (Mainly auto)

Travel Time: Public Transit

Transit Time Compared to Other

Atlanta, GA MSA

31.2

30.5

50.3

1.65

Austin--San Marcos, TX MSA

25.5

25.2

37.9

1.50

Boston--Worcester--Lawrence, MA--NH--ME--CT CMSA

27.8

26.1

43.8

1.68

Buffalo--Niagara Falls, NY MSA

21.1

20.5

36.2

1.76

Charlotte--Gastonia--Rock Hill, NC--SC MSA

26.1

25.9

44.1

1.71

Chicago--Gary--Kenosha, IL--IN--WI CMSA

31.0

28.5

49.7

1.75

Cincinnati--Hamilton, OH--KY—IN CMSA

24.3

23.9

38.4

1.61

Cleveland--Akron, OH CMSA

24.0

23.3

42.9

1.84

Columbus, OH MSA

23.2

22.9

35.6

1.56

Dallas--Fort Worth, TX CMSA

27.5

27.1

48.7

1.80

Denver--Boulder--Greeley, CO CMSA

25.9

25.1

42.7

1.70

Detroit--Ann Arbor--Flint, MI CMSA

26.1

25.7

46.0

1.79

Grand Rapids--Muskegon--Holland, MI MSA

20.7

20.6

32.2

1.56

Greensboro--Winston-Salem--High Point, NC MSA

22.4

22.2

36.8

1.66

Hartford, CT MSA

22.9

22.5

37.7

1.67

Houston--Galveston--Brazoria, TX CMSA

28.8

28.0

50.4

1.80

Indianapolis, IN MSA

23.8

23.6

40.6

1.72

Jacksonville, FL MSA

26.6

26.3

47.2

1.80

Kansas City, MO--KS MSA

22.9

22.7

38.6

1.70

Las Vegas, NV--AZ MSA

24.1

22.9

51.3

2.24

Los Angeles--Riverside--Orange County, CA CMSA

29.1

28.0

50.0

1.79

Louisville, KY--IN MSA

22.7

22.4

37.4

1.67

Memphis, TN--AR--MS MSA

24.5

24.2

44.9

1.86

Miami--Fort Lauderdale, FL CMSA

28.9

28.0

50.2

1.79

Milwaukee--Racine, WI CMSA

22.1

21.3

39.9

1.87

Minneapolis--St. Paul, MN--WI MSA

23.7

23.0

36.2

1.57

Nashville, TN MSA

25.8

25.6

41.2

1.61

New Orleans, LA MSA

26.7

25.7

43.6

1.70

New York--Northern New Jersey--Long Island, NY--NJ--CT--PA CMSA

34.0

27.8

52.2

1.88

Norfolk--Virginia Beach--Newport News, VA--NC MSA

24.1

23.8

43.5

1.83

Oklahoma City, OK MSA

22.0

21.9

31.4

1.43

Orlando, FL MSA

27.0

26.6

48.2

1.82

Philadelphia--Wilmington--Atlantic City, PA--NJ--DE--MD CMSA

27.9

25.9

47.4

1.83

Phoenix--Mesa, AZ MSA

26.1

25.7

45.3

1.76

Pittsburgh, PA MSA

25.3

24.4

38.8

1.59

Portland--Salem, OR--WA CMSA

24.4

23.3

40.7

1.75

Providence--Fall River--Warwick, RI--MA MSA

23.2

22.6

47.0

2.08

Raleigh--Durham--Chapel Hill, NC MSA

24.9

24.7

33.0

1.34

Rochester, NY MSA

21.1

20.8

37.0

1.78

Sacramento--Yolo, CA CMSA

25.6

25.1

42.5

1.69

Salt Lake City--Ogden, UT MSA

22.4

21.7

42.4

1.95

San Antonio, TX MSA

24.5

23.9

44.3

1.85

San Diego, CA MSA

25.3

24.4

50.5

2.07

San Francisco--Oakland--San Jose, CA CMSA

29.3

27.5

46.0

1.67

Seattle--Tacoma--Bremerton, WA CMSA

27.7

26.4

44.8

1.70

St. Louis, MO--IL MSA

25.5

25.0

44.3

1.77

Tampa--St. Petersburg--Clearwater, FL MSA

25.6

25.4

41.1

1.62

Washington--Baltimore, DC--MD--VA--WV CMSA

31.7

30.0

47.1

1.57

West Palm Beach--Boca Raton, FL MSA

25.7

25.4

45.6

1.79

Average

25.6

24.8

43.0

1.74

Minor geographical differences between 1990 and 2000

Calculated from US Census Bureau data.

 


Table A-3

Change in Transit, Car Pools and Work at Home: Metropolitan Areas Over 1,000,000: 1990-2000

Metropolitan Area

New Transit Trips

New Carpool Trips

New Work at Home

Atlanta, GA MSA

3,286

92,022

38,742

Austin--San Marcos, TX MSA

3,313

32,889

11,443

Boston--Worcester--Lawrence, MA--NH--ME--CT CMSA

29,223

9,556

33,125

Buffalo--Niagara Falls, NY MSA

(6,228)

(10,484)

1,085

Charlotte--Gastonia--Rock Hill, NC--SC MSA

(675)

9,108

9,592

Chicago--Gary--Kenosha, IL--IN--WI CMSA

(42,131)

3,668

40,839

Cincinnati--Hamilton, OH--KY--IN CMSA

(1,923)

2,514

8,941

Cleveland--Akron, OH CMSA

(10,089)

(7,408)

12,217

Columbus, OH MSA

(880)

(2,501)

7,417

Dallas--Fort Worth, TX CMSA

(567)

79,603

30,284

Denver--Boulder--Greeley, CO CMSA

17,066

27,499

25,470

Detroit--Ann Arbor--Flint, MI CMSA

(7,424)

2,309

17,330

Grand Rapids--Muskegon--Holland, MI MSA

(102)

8,555

5,517

Greensboro--Winston-Salem--High Point, NC MSA

(487)

9,415

4,555

Hartford, CT MSA

(4,425)

(11,830)

3,463

Houston--Galveston--Brazoria, TX CMSA

1,643

40,049

15,304

Indianapolis, IN MSA

(2,220)

2,898

8,424

Jacksonville, FL MSA

(1,731)

2,875

546

Kansas City, MO--KS MSA

(5,097)

(4,538)

8,724

Las Vegas, NV--AZ MSA

20,940

48,561

10,980

Los Angeles--Riverside--Orange County, CA CMSA

3,299

(23,904)

55,349

Louisville, KY--IN MSA

(3,344)

(3,225)

2,884

Memphis, TN--AR--MS MSA

(3,876)

5,834

4,489

Miami--Fort Lauderdale, FL CMSA

(468)

7,019

16,509

Milwaukee--Racine, WI CMSA

(5,279)

(3,788)

3,473

Minneapolis--St. Paul, MN--WI MSA

1,993

13,106

16,186

Nashville, TN MSA

(2,500)

10,959

7,245

New Orleans, LA MSA

(5,622)

4,829

4,874

New York--Northern New Jersey--Long Island, NY--NJ--CT--PA CMSA

41,472

(5,455)

74,500

Norfolk--Virginia Beach--Newport News, VA--NC MSA

(1,150)

(6,768)

(16,959)

Oklahoma City, OK MSA

22

1,470

3,183

Orlando, FL MSA

4,521

21,112

11,624

Philadelphia--Wilmington--Atlantic City, PA--NJ--DE--MD CMSA

(39,509)

(49,806)

16,727

Phoenix--Mesa, AZ MSA

8,116

81,827

24,390

Pittsburgh, PA MSA

(10,708)

(19,347)

5,934

Portland--Salem, OR--WA CMSA

22,152

25,947

18,518

Providence--Fall River--Warwick, RI--MA MSA

(452)

(8,296)

2,082

Raleigh--Durham--Chapel Hill, NC MSA

1,985

26,728

12,180

Rochester, NY MSA

(5,092)

(8,774)

3,204

Sacramento--Yolo, CA CMSA

5,140

14,426

10,941

Salt Lake City--Ogden, UT MSA

4,842

17,392

9,399

San Antonio, TX MSA

(809)

18,708

4,831

San Diego, CA MSA

3,535

14

(4,103)

San Francisco--Oakland--San Jose, CA CMSA

27,049

28,035

27,915

Seattle--Tacoma--Bremerton, WA CMSA

28,611

49,573

22,138

St. Louis, MO--IL MSA

(3,763)

(15,664)

8,140

Tampa--St. Petersburg--Clearwater, FL MSA

1,233

10,207

12,576

Washington--Baltimore, DC--MD--VA--WV CMSA

(32,046)

(28,430)

(39,622)

West Palm Beach--Boca Raton, FL MSA

1,528

8,134

9,284

Total

32,372

506,623

621,889

Compared to Transit

16

19

Minor geographical differences between 1990 and 2000

Calculated from US Census Bureau data.

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