ALL STATES LINEAR CORRELATION MATRIX

VARIABLE __|DESCRIP VMT- VMT_CAP LM LM_CAP_| VYMT_LM_|LMCAPURB| PoP POPDEN PCI_98 JOBS GAS_PR98
MT Coefficient 1 0.08692} 082329 -0 39539} 0 8045] 019113 096957 0 4787 0 62362 072788} 0045)
Std Error 0 00001 0.0001 0.0001 0000! 0.0001 00001 0.0001 00001 0.0001 00265
IN 2420] 2420! 2420 2420) 2420) 2420} 2420] 2420) 2420 2420) 2420]
VMT_CAP [Coefficient -0 08692 ! 0.06522) 0 50663: -0.00042 0 16712) -0 2157 -6 12968} 005275 -0 13545] -0.0561
Std Error 0.0001 0 00013 0.0001 09835) 0.0001 0000! 00001 00095 0.0001 0.0058
IN 2420) 2420) 2420) 2420 2420} 2420) 2420) 2420 2420) 2420) 2420)
LM Coefficient 0 82329 -0 06522) 1 03359 050023) 0 29908} 0.77997 034915) 0.4396 0 66948) 0.05598)
Std Error 0.0001 00013 0} 00001 0.0001 0.0001 0.0001 0.0001 0.000! 00001 00012
IN 2420) 2420) 2420) 2420 2420) 2420) 2420) 2420, 2420} 2420) 2420)
ILM_CAP Coefficient -0 39539 0 50663 -0 3359 1 051941 -0 43812 -0 42876 -0 24353) -0 32282 -0 29362 OO
Std Error 0.0001 0.0001 0.0001 0 0.0001 0.0001 00001 00001 0.0001 0.0001 05848
IN 2420) 2420 2420) 2420! 2420) 2420) 2420! 2420 2420) 2420) 2420]
'VMT_LM Coefficient 0.8045 -0 00042! 0 50023) -051941 1 0 25851 0 78358) 049497, 0 70688) 051314 -0.02173
Std Error 0.0001 0 9835) 0.0001 00001 0 0.0001 0.0001 0.0001 0.0001 0.0001 0 2852
N 2420) 2420) 2420 2420) 2420) 2420) 2420) 2420) 2420) 2420) 2420!
LMCAPURB {Coefficient 0 19113) -0 167{2| 0 29908} -0 43812, 0 25851 { 0 1676 005496 0 24089 OITIST 0.00712!
Std Error 0.000! 00001 0.0001 0.0001 0.0001 oO} 00001 0 0068) 0.0001 00001 0.7263
IN 2420 2420) 2420) 2420) 2420) 2420) 2420 2420) 2420) 2420 2420
POP. Coefficient 096957 -0.2157 077997 +0 42876 078358! 0 1676 1 0 54264] 0 60104} 07633] 0 06766}
Std Error 0.0001 00001 0.000! 0.0001 0.0001 0 0001 0 00001 0.0001 0.0001 0.0009
IN 2420) 2420) 2420) 2420 2420) 2420) 2420 2420 2420) 2420 2420}
IPOPDEN Coefficient 0.4787] -0 12968 034915 -0 24353 0.49497 0.05496} 0 54264 1 0 49802 0 65324) 011776)
Std Error 00001 00001 00001 0.0001 0.000! 0.0068 00001 0 0.0001 0.0001 00001
N 2420} 2420! 2420 2420 2420) 2420] 2420! 2420) 2420 2420) 2420)
PCI_98 Coefficient 0 62362 0.05275 0.4396 -0 32282 0 70688) 0 24089} 0 60104) 0.49802! 1 0 51463 -0.01022
Std Error 00001 0 0095 0.0001 0.0001 00001 00001 0.0001 0 0001 0} 0.0001 06153
IN 2420! 2420] 2420) 2420 2420) 2420) 2420| 2420] 2420) 2420) 2420)
JOBS Coefficient 0 72788) -O 13545) 0 66948 -0 29362 051314 017151 0 7633 065324) 0 51463} 1 00795
Std Error 0.0001 00001 00001 00001 00001 0.0001 0.0001 00001 0.0001 ty) 0.0001
IN 2420) 2420 2420] 2420! 2420) 2420 2420 2420 2420 2420) 2420
IGAS_PR98 | Coefficient 00451 -0 0561 0 06598 OOLIII -002173) 0.00712) 0 06766 011776] -0.01022 0.0795 1
Std Error 0.0265] 0.0058) 00012 0 5848 0 2852) 07263) 0.0009 00001 06153 0.0001 0
IN 2420] 2420} 2420| 2420| 2420} 2420| 2420] 2420} 2420] 2420) 2420)



increase in travel. This could indicate that the cross-sectional variation in the data (actually,
cross-regional variation) has a steeper slope than the variation within each state. This may also
reflect the difference in the number of years used in this regression relative to Virginia or

Maryland regressions

Overall, the basic regression results indicate a generally strong relationship between the number
of Jane mules and the level of travel. However, this relatonship may well vary over time, due to
the possibility of a lag trme between an addition of lane-mile capacity and the different forms of
response Of travelers to this increase, which could include both rmmediate increases in travel
(especially to lane mile addiuons that reduce congestion), and Jonger term responses, such as
increased development along uncongested corridors, relocations of individuals that result in

Jonger tnps, etc.

Specifications also were tested that involve taking the first difference of all variables from year to
year. We conducted these as the differences of the logs of variables (year t minus year 1-1),
which captures percentage changes through tme A summary of the first difference results is

shown in Table 3-2

The results of these runs are somewhat more varied than the base runs, but sull significant for
lane mules in every study area (DC-Baltimore ss significant only at about the 90% confidence
interval). However, the coefficient for the change in population on the change in travel was
insignificant in most areas. This may reflect an errors-in-variables problem in the year to year
change in population, since population levels are not actually measured each year but estimated
for years between the census, Such an error would appear much more promunently in a first
difference regression than in a regression using variable “levels” (such as the base regressions).

The R-squared values in these runs are quite low, generally less than 0.2. However, this ts not

> Note that years before 1985 were dropped sn the “‘al) states” regressions since these years were not
available for North Carolina Keeping only the years available for all states preserves a balanced panel
data set. Analyses using all the years (not shown) does not alter the basic results

3-5


MEASURING INDUCED TRAVEL DEMAND
FROM ROADWAY CAPACITY EXPANSION:
AN EMPIRICAL ANALYSIS OF THE
U.S. MID-ATLANTIC REGION

FINAL REPORT

Prepared for:
U.S. ENVIRONMENTAL PROTECTION AGENCY
Office of Policy, Planning and Evaluation
401 M Street, S.W.
Washington, D.C. 20460

Prepared by:
ENERGY AND ENVIRONMENTAL ANALYSIS, INC.
1655 North Fort Myer Drive, Suite 600
Arlington, Virginia 22209

Under subcontract to:
SIERRA RESEARCH
1801 J Street
Sacramento, California 95814
Contract 68-D4-0102, Work Assignment 4-15

September 30, 1999


WASHINGTON DC - BALTIMORE EXPANDED AREA LOG CORRELATION MATRIX

VARIABLE |DESCRIP_ | LOGVMT | LOGVCAP | LOGLM_[LOGLMCAP|LOGVMTLM|LOGLMURB| LOGPOP [LOGPOPDN] LOGPCI98 | _LOGIOBS | LOGGASOS
LOGVMT Coefficient 1 0.09649) 0 7674) -0 71429) 078461 -0 56761 095235 0 69985] 051532} 0815) -0 14989
Std Error i) 0045 0.0001 0.0001 00001 0.000! 00001 0.0001 00001 0 0001 0.0022}
IN 432 432 432) 432) 432) 432 432) 432| 432 432 416
LOGVCAP Coefficient -0.09649 1 -0 04554; 0 52294 -O 10328) 0 46992) -0 39547) -0 47049 0.00765 -0 26984 -0 23704
Std Error 0045 0 03451 00001 00319] 0000! 00001 0.0001 08741 0.0001 00001
N 432) 432] 432 432 432) 432 432| 432 432 432| 416
ILOGLM |Coefficrent 07574 -0.04554] ' -0:1951 0 20458 -0 28444) 07221 0 28384) 0 11688] 0 67326 000272
Std Error 00001 03451 0 0.0001 0.0001 0.0001 00001 0.0001 OOIsI 0 0001 09558)
N 432 432| 432 432) 432| 432 432 432) 432 432 416
ILOGLMCAP |Coefficrent -0 71429 0 52294) -01953 1 -0 90182 071103: -0 81938) -0 88478 -0 57401 ~0 62557 009498}
Std Error 0.0001 00001 0.0001 0 00001 00001 0.0001 00001 00001 0.0001 005291
N 432 432) 432 432 432 432 432 432 432 432 416}
ILOGVMTLM $|Coefficient 0 78461 -0 10328 0 20458] -0 90182) 1 -0 59149 075567 0 79396} 06737 0 5932 -0 23156
Std Error 00001 00319 0.0001 00001 it) 00001 0.0001 0.0001 00001 0 0001 0.0001
N 432 432| 432 432 432| 432 432| 432 432 432} 416
ILOGLMURB [Coefficient -0 56761 0 46992 -0 28444 0 71103) -0 591491 i -0 66778) -0 73844 -0 31984) -0 5307 0.00172,
Std Error 0.0001 0.000! 00001 0.000: 00001 0} 0.0001 00001 00001 0 0001 09721
N 432! 432 432 432! 432 432 432 432 432 432 Al6
|LOGPOP Coefficient 0.95235 -0 39547 07221 -0 81938) 075567 -0 66778 | 1 0 78998} 0.47319] 0 83476) -006554|
Std Error 0.0001 0.0001 0.0001 0.0001 0.0001 00001 0) 0.0001 0.0001 0.0001 0 1822]
IN 432 432] 432 432} 432 432| 432| 432 432 432! 416)
ILOGPOPDN = {Coefficient 069985: -0.47049 0 28384 -0 88478 0 79396 -0 73844 0 78998) 1 O60I9t 077707 -0 06806
Std Error 0.0001 0.0001 00001 0.0001 0 0001 0.0001 00001 0} 0.0001 0.0001 0 1659]
IN 432 432 432 432| 432 432 432) 432 432 432| 416)
[LOGPC198 Coefficient 051532} 000765 0 11688] -057401 06737 -0 31984 047319) 060191 | 0 42252] -030171
Std Error 00001 0874! 001s! 0.0001 00001 00001 0 0001 0.0001 0 0 0001 0.000!
IN 432 432! 432) 432 432 432 432 432 432 432 416)
ILOGJOBS Coefficient O8Is -0 26984 0 67326 -0 62557 0 5932 -0 5307 0 83476 077707 0.42252| 1 -0 06424
Std Error 0.0001 0.000! 00001 0.0001 00001 00001 0.0001 0.0001 0.0001 0} 0191
IN 432) 432) 432 432 432 432 432) 432 432 432| 416)
ILOGGAS98 =| Coefficient -0 14989) -0 23704] 0.00272; 0.09498) -0 23156 0.00172) -0.06554) -0 06806) -030171 -0 06424) i}
Std Error 0.0022 0 000! 09558) 0.0529 0 0001 09721 0 1822) 0 1659 0.0001 0191 0}
N 416) 416} 416) 416) 416 416 416] 416 416 416) 416)



APPENDIX C: DATA LISTING FOR DC AND
DESCRIPTION OF DC METRO AREA

This appendix provides a full listing of the data for the District of Columbia used in the analysis
It also provides a listing of the counties included in the DC/Baltimore extended metro area. The
tables in this appendix are as follows.

Table C-1. Listing of counties in DC / Baltimore extended area

Table C-2. DC database printout


fewer and/or shorter car trips, which may be due to proximity of destinations and/or greater

availability of alternative (non-auto) travel modes

The average number of Jane miles per capita 1s also greater in the areas with lower population
density, with a higher average in North Carolina and Virginia than in the DC/Balumore metro
area and Maryland. This may reflect the presence of underutilized interstates and major arterials
that have been put in place to provide mobility to the scattered populous of the rural counties in
states such as North Carolina. It also may help explain why VMT per capita in densely
populated areas 1s lower — the availability of roadway miles per person 1s much lower. If true,
this would imply that congested conditions limit the VMT of residents in such an area to levels
below areas with more roadway capacity available. This 1s conjecture, but these relationships are

examined more formally in the following section through techniques of multiple regression

Finally, the average daily trave] (VMT) per lane mile of available roadway 1s indeed much higher
in the more densely populated areas, again indicating that there 1s much less available road
capacity in the DC/Balumore area than in Virginia, with North Carolina and Maryland
intermediate. This information on the variation in roadway use intensity provides an opportunity
to study how the relationship between changes in roadway supply and travel vary by the iniual
conditions of roadway capacity (does adding capacity in an area of high roadway use affect travel

differently than adding capacity in an area of relatively low use?)

Table 2-2 provides average annual growth rates of key variables. The growth rates for several
key variables are significantly different across the different areas. While the growth rate in VMT
1s between 3% and 4% per year sn all areas, the growth rate in Jane miles varies significantly,
ranging from 0.38% in Maryland to 0.87% in the DC/Baltuumore area. The trends in North
Carolina, with VMT growing much faster than either population or lane miles, suggest that
average travel per person has increased significantly. However, the average VMT per lane mule

1m North Carolina counties in 1995 (shown in Table 2-1) was sull quite low compared to

2-3


NORTH CAROLINA LOG FIRST DIFFERENCES CORRELATION MATRIX

[VARIABLE DESCRIP_| L_VMTDFF | L_VCPDFF | L_LMDIFF | L_LMCDFF [| L_POPDFF | L_PCIDFF | L_JOBDFF
L_VMTDFF Coefficient i 0 98673 021371 0 15035 0 10502, -0 00717 0.0783}
Std Error 0 00001 0.000! 00001 0 0003 08123] 0.0094
IN 1200 1200} 1200} 1200] 1200 1100} 1190}
L_VCPDFF Coefficient 0 98673 1 0 20375 021418 -0 05782: 002477, 003768
Std Error 0.000! 0 0.0001 oooor 0.0452) 04118 02118
IN 1200) 1200} 1200} 1200} 1200 1100} 1100
|L_LLMDIFF Coefficient 021371 0 20375 1 0.89429 0.06606: 0.00634 0.02988
Std Error 0.0001 00001 0 0.0001 00221 0 8336] 03221
IN 1200) 1200 1200) 1200} 1200 1100} 1100}
ILLLMCDFF Coefficient 0 15035 021418 0.89429 1 -0 38742 0.09309) -0 08375
Std Error 0.0001 00001 0.0001 0 0.0001 0 002 00054
N 1200 1200) 1200} 1200) 1200 1100} 1100
L_POPDFF Coefficient 0 10502 -0 05782 0 06606] -0 38742 I -0 19989] 0 25564
Std Error 0.0003 0 0452 00221 00001 0 00001 0000!
IN 1200) 1200) 1200] 1200) 1200 1100} 1100
L_PCIDFF Coefficsent -0.00717 002477 0.00634 0 09309 -0 19989; 1 032727
Std Error 08123 04118) 0 8336] 0.002 00001 1) 0.0001
IN. 1100) 1100 1100} 1100; 1100) 100 1100)
L_JOBDFF Coefficrent 0 0783) 0 03768 0.02988} -0 08375 0 25564 0 .32727| !
Std Error 0 0094) 02118 03221 0.0054 00001 0.0001 0
IN 1100) 1100 1100 1100) 1100 1100} 1100)



Cle

TABLE 3-5

POPULATION DENSITY INTERACTION MODELS

All States model

Population Density

Population Density
interaction with

[Dependent vanable = LOG(VMT) interaction distributed lag
IN 2420 2200
R-Square o71t 0 785
Intercept est 2515 0.889

t @ 538) (1341)
Log (VMT) lagged | year est. 0.506

t (28 329)
Log (Lane Miles, low population density) est 0402 0.294

t (6.079) (5011)
Confidence interval (95%) 0272 -0.531 0 179-0 409
Log (Lane Miles, medium population density) est. 0,573 0.323

t (8.496) (5.447)
Confidence interval (95%) 0441 —-0 705 0 207 —0 440
Log (Lane Miles, high population density) est 0404 0.210

t (3 708) (2217)
Confidence interval (95%) 0.190 -0 617 0 024 — 0 395
Log (Population) est 0.571 0 229

t (14.260) (5.914)
‘Log (Income Per Capita} est 0.198 0157

1 (4 186) (3.744)
Long-Run Elasticity — Lane miles, low population density 0581
Long-Run Elasticity — Lane miles, medium population density 0 638
Long-Run Elasticity - Lane miles, high population density 0415

Note confidence interval provides the range of possible “actual” value for coefficient within 95% confidence interval



oRT! INF ‘IES EGRE (PR ROAL ”

Model- MODEL1
Dependent Variable: LOGVMT

Analysis of Variance

Sum of Mean
Source DP Squares Square P Value ProboF
Model 223 1058.73544 9 36934 2182.786 © 0001
Error 1086 4 66152 0.00429
¢ Total 1199 1063.39696

Root MSE 0.06552 R-square 0 9956

Dep Mean 13 46608 Adj R-sq 0.9952

cv 0.48653


TABLE A-la
DESCRIPTION OF VARIABLES

FOR ALL STATES
Variable. Description
VMT vehicle miles travelled
'VMT_CAP VMT per capita
LM lane miles
LM_CAP lane mules per capita
VMT_LM I'VMT per lane mile
LM_CAP*Urban
LMCAPURB (where Urban = | if population density 1s over 100 people/sq. mile)
POP populauon
POPDEN population density
PCI_98 income per capita in 1998$
JOBS lemployment by place of work
IGAS_PR98 statewide price of gasoline in 1998$
LOGVMT Log(VMT)
LOGVCAP Log(VMT_CAP)
LOGLM. Log(LM)
LOGLMCAP _|Log(LM_CAP)
ILOGVMTLM_|Log(VMT_LM)
LOGLMURB _|Log(LMCAPURB)
LOGPOP Log(POP)
LOGPOPDN _ {Log(POPDEN)
ILOGPCI98 Log(PCI_98)
LOGJOBS Log(JOBS)
ILOGGAS98 __|Log(GAS_PR98)



NORTH CAROLINA LOG CORRELATION MATRIX

VARIABLE _|DESCRIP. LOGVMT_| LOGVCAP LOGLM_|LOGLMCAP/|LOGVMTLM|LOGLMURB| LOGPOP_|LOGPOPDN] LOGPCI98 | LOGJOBS | LOGGAS98
ILOGVMT Coefficient 1 006178) 0 88381 -0 69707} 0 86048) -0 66181 094153 0 81843 0 62724} 0.9238 -0 07057
Std Error 0 0.0259) 0.0001 00001 0.0001 0.0001 00001 0.0001 00001 00001 0.0192
IN 1300) 1300} 1300} 1300] 1300] 1300] 1300) 1300) 1200) 1200} 1100}
LOGVCAP Coefficient 0 06178) 1 0.08775 052847, 0.01706} 0 35573 -0 27813) -0 36771 0.08913: -0 27607] -0 14462
Std Error 0.0259] ti) 00015 00001 0 5389 00001 00001 0.0001 0.002] 00001 00001
IN 1300} 1300] 1300 1300) 1300| 1300] 1300} 1300} 1200) 1200, 1100)
LOGLM Coefficient 0 88381 0.08775) 1 -0 3957 052215 -0 44104) 0.82094) 057315 0 40627 079421 -00198
Std Error 00001 00015 1) 0.0001 0.0001 0.0001 00001 0.0001 00001 00001 05119)
IN 1300} 1300} 1300 1300} 1300} 1300| 1300; 1300} 1200} 1200] 1100)
LOGLMCAP Coefficient -0 69707 0 52847] -0 3957] 1 -0 83981 0 80971 -0 84925 -0 93649 -0 54518 -0 84227 001279
Std Error 0.0001 00001 0.0001 0} 0.0001 00001 0.0001 0000! 00001 0.0001 0 6718]
IN 1300) 1300| 1300} 1300] 1300) 1300] 1300} 1300} 1200] 1200] 1100}
LOGVMTLM Coefficient 0 86048] 001706) 0.52215) -0 83981 i) -0 72615) 0 82235 0 86782) 0.70093) 0 81804} -0 1074
Std Error 0.0001 05389) 0.0001 00001 0} 00001 0.0001 0.0001 0000! 0.0001 0 0004
N 1300] 1300} 1300] 1300} 1300) 1300] 1300) 1300) 1200) 1200} 1100}
LOGLMURB {Coefficient -0 66181 0 35573} -0 44104) 080971 -0 72615: 1 -0 757 -0 82488 -0 49484) -0 76544 001676
Std Error 00001 00001 0.0001 00001 0 0001 0} 0.0001 0.0001 0.0001 00001 05787
IN 1300} 1300} 1300} 1300} 1300 1300} 1300) 1300] 1200| 1200) 1100)
LOGPOP Coefficient 094153 -0 27813 0 82094} -0 84925) 0 82235) -0757 1 091178) 0 57293] 098108 -001938
Std Error 00001 0 0001 0.0001 00001 0.0001 0.0001 0 0.0001 0.0001 00001 0 5208
IN 1300) 1300) 1300) 1300} 1300] 1300] 1300} 1300} 1200} 1200} 1100}
LOGPOPDN = {Coefficient 0 81843] -0 36771 057315) -0 93649) 0 86782! -0 82488} 091178 1 0 63334] 0.90817] -0.02172|
Std Error 0.0001 00001 0.0001 0.000! 00001 0.0001 0.0001 tv) 0.0001 0.0001 04717
IN 1300) 1300] 1300) 1300] 1300} 1300] 1300 1300} 1200] 1200] 1100)
LOGPCI98. —| Coefficient 062724] 0.08913) 0 40627 -0 54518 0 70093! -0 49484) 057293 0 63334] 1 0.60828) -0 22481
Std Error 00001 0 002) 0.0001 0.0001 0 0001 0.0001 0.0001 0.0001 0| 00001 0.0001
IN 1200] 1200] 1200] 1200] 1200| 1200} 1200) 1200] 1200) 1200) 1100)
LOGJOBS |Coefficrent 09238 -0 27607 0.79421 -0 84227 0 81804 -0 76544) 0 98108} 0 90817 0 60828) 1 -0 03237|
Std Error 0.0001 00001 0.0001 00001 0.0001 00001 00001 0 0001 0.0001 0} 0 2835
IN 1200] 1200) 1200) 1200) 1200} 1200} 1200) 1200} 1200] 1200} 1100)
LOGGAS98 = {Coefficient -0 07057 -0 14462} -0 0198] 0.01279) -0 1074 0 01676 -001938) -0. 02172 -0 22481 -0 03237 1
Std Error 00192 00001 05119 06718 0 0004 05787 0 5208 04717 0.000! 0 2835} 0
IN 1100) 1100} 1400} 1100) 1100, 1100} 1100} 1100} 1100) 1100) 1100)



4. SUMMARY AND CONCLUSIONS

The results of EEA’s analysis indicate that there is a significant relauonship between the level of
highway capacity, as measured by lane miles, and the level of travel, measured by daily VMT, in
the mid-Atlantic region of the U.S After accounting for other important determinants of travel,
such as population and income, the estimated elastcity between lane miles and VMT 1s in the
vicinity of 0.3 to 0.6, with an average value around 0 5. Breaking this into short run and Jong run
terms, the elasticity 1s in the range of 0.1 to 0.4 short run, with an average value around 0.25, and
0.5 to 1.0 Jong run, with an average value around 0.7 Although there 1s some variation across
study area and specification, there 1s a surprising degree of consistency in both the significance

and the value of the lane mile coefficient across all the regressions EEA conducted.

The results using the entire database (“all states” runs) are generally close to those for the
individual states, and there 1s relatively little difference between the results for each state,
especially in the base runs. However, a somewhat surprising result 1s that the relatronshp
between lane miles and travel appears to yield a lower elasticity for more urbanized areas
(namely the DC-Baltimore extended metropolitan area), than for the study areas that contain

many rural counties (all three of the states).

While the base specifications and distributed lag specifications provide stable and highly
significant coefficients, for the most part, EEA’s efforts to analyze the data on the basis of traffic
volume and population density have failed to produce conclusive inferences. These regressions
produced significant relauonships between travel and highway capacity for each of several areas
that vary by the level of traffic volume and population density, but little difference in the

relationships across the different area types

4-1


WASHINGTON DC - BALTIMORE EXPANDED AREA LINEAR CORRELATION MATRIX

VARIABLE

DESCRIP MT. VMT_CAP LM LM_CAP VMT_LM_|LMCAPURB. POP POPDEN PCI_98 JOBS GAS_PR98
MT Coefficient 1 -0 14647 0.7998) -0 60754 0 68558] 041412) 095753) 0 19561 0 46782! 079152 -0 13743
Std Error 0 0.0023 0.0001 0.0001 0.000! 0 0001 0.000! 00001 0 000! 0.0001 0005
N 432 432 432) 432 432 432) 432| 432 432 432 416
VMT_CAP = [Coefficient -0 14647 1 -0 11223 045937 -0 15497 0 1560! -0 33722) -0 33695 -001039 -0 23648) -0 21804]
Std Error 0 0023) 0 00196) 00001 00012 00011 00001 0.0001 0 8295 00001 0.0001
IN 432! 432) 432) 432 432 432 432 432 432 432) 416]
LM Coefficient 0 7998) -0 11223 1 -03123 0 17633 -0 10297 0 76424 -0 05002| 0 12226} 0 78872 -0 00424)
Std Error 00001 0.0196] i) 00001 0 0002 00324) 00001 0 2996 ool 00001 09313
N 432) 432) 432) 432 432 432 432 432| 432) 432 416
LM_CAP Coefficient -0 60754 0 45937] -03123 1 -0 73244, 06124) -0 67736 -0 51616 -0 52932| -0 56222 0 08767|
Std Error 00001 00001 0.0001 0 00001 0.0001 0.000! 0.0001 0 0001 0.0001 00741
IN 432 432 432 432 432 432 432 432 432 432 416)
VMT_LM Coefficient 0 68558 -0 15497 0 17633 -0 73244 1 -0 55057] 069815 0.49507, 0 64961 03551 -0 20835)
Std Error 00001 00012 0.0002, 0.0001 0] 0.0001 00001 0.0001 0.0001 00001 0.0001
N 432) 432| 432 432) 432 432 432 432 432 432, 416
ILMCAPURB = [Coefficient -0.41412 0 15601 -0 10297] 06124 -0 55057 1 -0 46833 -0 3778) +0 46366, -0 38071 0 11576]
Std Error 00001 00011 0.0324) 00001 0000) 0} 00001 0.0001 0.0001 0000! 00182
IN 432 432! 432 432 432 432, 432 432 432) 432| 416]
POP. Coefficient 095753 -0 33722 076424 -0 67736 069815 -0 46833 1 0 24757| 0.43146] 077097 -0 05667!
Std Error 0.0001 0.0001 0.0001 0.0001 00001 00001 0} 00001 0.0001 00001 0 2488]
N 432 432 432| 432 432 432 432, 432| 432 432! 416
POPDEN Coefficient 0 19561 -0 33695 -0.05002) -0 51616} 0.49507 -03778| 0 24757! 1 05747 0 35694! -0. 05136)
Std Error 00001 0.0001 0 2996) 00001 00001 00001 00001 0 00001 00001 0 296)
N 432, 432) 432 432 432! 432 432) 432 432 432 416
PCI_98 Coefficient 0 46782 -0 01039) 0 12226 -0 52932} 064961 -0 46366} 0 43146} 05747 1 045311 -0 28934)
Std Error 0.0001 0 8295 ool! 00001 00001 00001 0.0001 0.0001 t) 00001 0.0001
IN 432 432, 432 432 432| 432 432 432) 432 432) 416}
JOBS Coefficient 079152 0 23648} 0 78872| -0 56222 03551 -0 38071 077097 0 35694] 045311 1 -0 06843}
Std Error 00001 0.0001 00001 0.0001 00001 00001 0.0001 0.0001 00001 te) 0 1636]
IN 432 432) 432, 432 432] 432| 432] 432 432 432 416
GAS_PR98 | Coefficient -0 13743 -0 21804} -0 00424 0 08767) -0 20835) 011576 -0 05667 -0 05136 -0 28934] -0 06843) 1
Std Error 0.005 0.0001 09313 00741 00001 00182) 0 2488 0 296} 00001 0 1636) Oo
IN 416) 4N6) 416 416) 416 416} 416] 416] 416) 416) 416]



NORTH CAROLINA COUNTIES LOG REGRESSION (PRIMARY ROADS ONLY!

Parameter Estimates

Parameter Standard TT for HO-
Variable DF Estimate Error Parameter: Prob > |T|
1 4 242007 = 1.:03268853 4 108 0 0001
1 0 434538 =o :05420386 8 017 0 0001
1 0 585011 0. 06231359 9 388 0 0001
L 0 056924 = -0.-: 05940959 0 958 0.3382
1 0 023705 0 00974034 2 434 0 o1s2
ry 9 013958 0 :01014483 1376 0 1692
2 0 070336 = -o:01132153 6 213 0 0002
1 0 124368 © 0 01231221 20 101 0 0002
1 0 129553 0 01244942 210.406 90 0001
a 0 161899 0 01248450 12,968 0 0002
rf 0 198603 0 01371256 14 483 0 0003
1 © 209684 =o: 01509294 23.893 © 0001
1 0 241577 0 01599959 15 099 © 9003
1 0 279203 9 01746163 15 990 © 0002
1 0 311663 0 01894133 16 454 0.0001
1 -0 480652 0: 10724600 4 482 0 9002
1 -0 737722 0 15529357 “4751 0 0001
1 +0 235851 —-0.10142226 +2325 0 0202
1 -0 528601 0 10464426 +S 051 0 0001
AVERY 2 -0 036317 0: 12897693 -0 282 0 7783
BEAUFORT 1 -0 374608 © 0 06917426 “8 415 © 9001
BERTIE 1 -0 262281 © 0:11336522 -2 314 0 0209
BLADEN 1 -0 293319 0 09896047 -2 964 © 0031
BRUNSWIC 1 0 176436 © 0 06017137 2 932 0 0034
BUNCOMBE 1 0 138348 = 0 04706161 2 940 0 0034
BURKE 1 0.028816 = 0: 03672596 0 512 0 6085
CABARRUS 1 0 064857 —-g-: 02813524 2 305 0 0213
CALDWELL «1 -0 308292 =o: 04066045 -7.581 0 001
CAMDEN 1 0.008680 0 18581902 0 047 0 9627
CARTERET 2 0 145859 =) 95928035 2 687 © 0073
CASWELL =1 -0 548266 © 0.11091861 -4 943 0 0001
CATAWBA 1 -0,038458 0 02937679 -1 309 0 1908
CHATHAM 1 -0 210197 0: 06752182 +3213 © 0019
CHEROKEE 1 -0.325803 011352492 -2 870 © 0042
CHOWAN 1 +0 509207 0 13951184 -2 650 0.0003
CLAY 1 -0 422686 =o: 18070145 -2 339 0 0195
CLEVELAN 1 -0.149140 003475667 -4 292 0 0001
COLUMBUS = 1 -0 322477 0 06881165 +4 686 © 0001
CRAVEN 1 -0 160997 0 03434432 -4 688 © 0001
CUMBERLA 1 -0 293959 0 06375880 -4 610 0.0001
CURRITUC 1 0 186653 0 13019194 1 434 © 1520
DARE a 0 400691 0, 10188082 3933 © 0001
DAVIDSON 1 -0 097033 «=. a-:03348358 -2 898 0 0038
DAVIE 1 0.211979 =a 08S66064 2.478 0 0135
DUPLIN 1 -0 228323 0 07547469 -3 025 0 0025
DURHAM 1 0.172240 © 0 04291114 4 014 0 0002
EDGECOMR 1 +0 460266 = 0:05542280 -8 305 0.0001
FORSYTH 1 0 037456 =o: 06597858 0 568 0 5704
FRANKLIN 1 +0 319608 0 :07589196 -42aL 0.0001
GASTON py +0 00825¢ = 0 03982488 -0 207 0 6359
GATES 2 -0 316852 0: 15745420 -2.012 0 04a
GRAHAM 2 -0 902848 = 0:1 7886854 <5 048 0 0002


TABLE A-3b

FIRST DIFFERENCE RUNS RESULTS - NORTH CAROLINA

Dependent Variable VMT/CAP FIRST DIFF LOG(VMT/CAP) FIRST DIFF
Bs of Independent Vanables? 70 no nO. no 20 nO yes yes. yes “yes “yes yes yes yes
1200 [1200 [1200 | 1100 [1100 | 100_[ 1200 [1200 [1200 [1100 [1100 | 1100_| 1100 | _1100 |
dyusted R-Square oo20 | 0052 _[ 0053 | o061 | 0062 | 0061 { 0025 [ 0071 | 0070 | 0070 | 0069 | 0069 | 0070 | 0070
INTERCEPT est [-0.486 [0495 | -0.407 | -0638_[-0535 | -0529 | -0020 | -o021 [ -0020 | -0027 | 0027 | -0027 | -0025 | -0028
[tog [112-0909 | -ra7_ [3 | 112-0869 [| -0933 | -o874 [ria [rn | -113 | -108 | -118
M_CAP DIFF et] x 6s | 850 | 839 | 826 | 825 x oes [0609 [or | 0612 | 0613 | 0624 | 0617
q x ois [600 | 583 | 573 | 572 x 739 [695 | 705 | 677 | 678 [718 | 708
POP DIFF est |x x [7656-05 x__|-8 17E-05|-7 808-05] __x x | ono [x | 006 | 0056 | x x
t x x 125 x 1268 | -1198 [x x | 03m [x | oo [ors | x x
PCL_98 DIFF est |x x x__| 1386-04] 1 236-04[132E-04[ x x x 0096 [ 0095 | 0068 x 0072
U x x x 134 ig [123 x x x 10s [102 [| 0689 x 0753
OBS DIFF est x x x x x -1 25E-05} x x x x x 0.089 0110 0 086
t x K x x x__| -0367 | x x x x x os32_ [110 | 0816
Dependent Variable MT FIRST DIFF LOG(VMT) FIRST DIFF
Logs of Independent Variables” no 0 no 0 no. no yes yes yes yes yes yes yes yes
IN 1200 1200 1200 1100 1100 1100 1200 1200 1200 1100 1100. 1100 1100 1100
[Adjusted R-Square 376 _| 0422 | 0425 [ 0430 [ 0431 | 0430 | 0037 | 0077 | 0077 | 0075 | 0075 | 0075 | 0075 | 0075
INTERCEPT est | 34756 | 22734 [ 13029 | 22560 | 17838 _| 17928 | -oo10 | -o017 | -0020 | -0022 | -0027 | -0027 | -0022 | -0023
«| 139 [0942 [0535 | 0973 | 0758 | 0757 | -0431 [| -0755 | -0874 | -0934 | -111 | 113 | -0927 | 0979
CM DIFF et [x 2507 [2470 [2275 | 2260_|_ 2260 x 0609 [0609 | 0613 | 0612 | 0613 | 0615 | 0614
t x 929 [| 917 | 898 [ 891 | 890 x 695 [ 695 | 678 | 677 | 678 | 681 | 679
POP DIFF est |x x 862 x 382_ | 381 x x 0281 x 0372 [0331 x x
q x x 259 x 120 | 118 x x 0989 x 1i7_|_ 103 x x
PCI_98 DIFF est |x x x =192_[-114 | -116 x x x don [0095 1 0068 x 0041
t x x x__| -0374 | -0221 [0217 [x x x 0777 [102 | 0689 x 0.436
OBS DIFF est |x x x x x 0.020 x x x x x 089 | O19 | 0105
n x x x x x 0012 x x x x x os32 | 119 | 100



how WUNTI oo uve REbawooi0N (secu ING mnonatGTin ny

Variable

VAFAIRFA
VAFAUQUI
VAFLOYD
VAFLUVAN
VAFRANKL
VAFREDER
VAGILES
VAGLOUCE
VAGOOCHL
VAGRAYSO
\VAGREENE
VAGREENS
VAHALIFA
VAHANOVE
VAHENRIC
VAHENRY
VAHIGELA
VAISLEWT
VAJAMESC
VAKINGQU
VAKINGGE
VAKINGWI
VALANCAS
VALEE
VALOUDON
VALOUISA
VALUNENB
VAMADISO
VAMATHEW
VAMECKLE
VAMIDDLE
VAMONTGO
VANELSON
VANEWKEN
VANORTHA
VANORTHU
VANOTTOW
\VAORANGE
VAPAGE
VAPATRIC
VAPITTSY
‘VAPOWHAT
VAPRNCED
VAPRNCGE
VAPRNCWI
VAPULASK
VARAPPAH
VARICHMO
VAROANOK
VAROCKBR
VAROCKIN
VARUSSEL
VASCOTT
VASHENAN
VASMYTH

DF

eee et eee eee ena een ee ee Rene ee ee eee eee teen ant ed

Parameter
Estimate

ecbococeedc0e

0
0

801702
412666
264809
228937
095995
435250
028914
367983
$87213
578012
285423
873455
195152
587588
227994

002495

577207
390586
427046
005020
359851
085449
155962
173554
244444
300475
479232
188790
318546
043806
490134
268208
049606
997190
126622
022263
199292
003375
373774
312096
243763
037064
032782
587072
486253
161800
216539
280975
519495
649032
118617
o16ase
020492
434369
100207

Standard
Error

0 09667513
0 03918922
0 10206859
0 10223993
0 05291128
0 03981419
0 07372688
0 06693261
0 07487080
0 07479579
0 11788301
0 09921389
0 04670268
0 03489967
© 05232206
0 04243985
0 13911055
0.06398077
0 06232127
0 11284663
0 08312938
0 10298644
© 10070598
0.06340447
0.04144365
0 06226578
0 09794486
0 09149449
0.12433403
0 04857008
0 10539168
0 04302540
0 07868044
0 08492909
0 08815797
0 10665125
0,07621011
9 07079780
0 08037895
0 07652515
0 03566235
0 09152330
0 07283953
0 06732203
0 06031362
0 05580810
0 10226038
© 12740408
0 04634778
0 06257040
0 03481585
0 05519387
0 06049744
0 04914304
0 05318617

T for Ho.
Parameters0

20.

-2

-2.
1.

10
-0
s
7
7
2
8
-4
216
4
0
“4
6
6
-0
4
°
“1
-2
s
4
“4
2
2
-0
4
6
-0
a
1
o
“2
-0
“4
-4
“6
-0
-0
a
a
2
+2
2

293
530
620
239
a4
932
257
498
943
728
424
804
179
837
358
059
149
105
as2
044
329
830
549
737
898
826
893
063
538
902
651
234
630
m1
436
118
615
048
650
078
a35
405
450
275
062
agg
118
393
209
373
407
298
339
839
24

Prob > |T|

0 0002
© 0002
0.0089
0 0252
0 0698
0 0002
0 7976
0 0001
0.0001
0 0002
0 0155
0 0003
0 0002
0 0002
0 0001
0 9531
0 0001
0 002
© 0001
0 9645
© 0002
0.4068
@ 1216
0 0062
© 0001
0 0001
0 0002
0 0392
0 0112
0 3672
0 0002
0 0002
0 5284
0 0002
0 1522
0 308s
0 0090
0 9620
0 0001
0 0001
0 0001
0 6855
0 6527
0 0001
0 0001
0 0038
0 0343
0 0168
© 0002
9 0001
0 0007
0 7656
0 7349
0 9002
0 0897


analysis do include (see discussion of fixed effects models, below.) Finally, transit data was not
available for many counties so it 1s not included in the analysis It has been noted by other
analysts (e.g., Hansen and Huang) that the availability of transit itself may be influenced by
roadway supply and may represent a joint product with highway travel, in which case controlling

for it would be inappropriate.

EEA has conducted the econometric analysis of induced demand in a sequential fashion, first
developing basic econometric models relating travel to the extent of lane miles and other
variables for each study area separately, then for all areas together, and then testing alternative
specifications to explore the underlying relationships in more detail. These alternative
specifications include first difference models, urban/rural models, and lagged dependent variable
models. Although both linear and log-linear (linear in logarithms) models were tested, most of
the discussion 1s focused on the log-linear models due to the ease of interpretation of the

coefficients in such models as elasticity estimates.

In all estimated models, a “fixed effects” specification approach has been used. Fixed effects
models use cross sectional and/or time series intercepts for each unit of observation in order to
capture residual variation that 1s not accounted for in the set of explanatory variables entered in

the model.

Econometrically, a “fixed effects” model acknowledges the researcher’s lack of information
about the unique characteristics of each unit in the data It can also reduce or eliminate bias
associated with correlations across units that would normally be captured in the error term. The
closer the error term 1s to a random distribution of residual (unexplained) variation, the less bias
will be present in the model estimates - in this case the estumates of the relationship between lane
miles and VMT. Since the data base used here 1s a panel data base (i.e., the same counties
included repeatedly over a period of years) our fixed effects models also account for variations
across time that might be correlated in the error term for individual counties. Our fixed effects

models are thus specified with a separate intercept term for each county and each year of data.


Figure 1-1

LIST OF FIGURES

Schematic Representation of the Impact of Roadway
Expansion on Travel

iii

E
iD

1-3


TABLE A-2b
BASE RUN RESULTS - NORTH CAROLINA

[Pxvensen Wanable VMT/CAP LOG(VMT/CAP) |
[Logs of Independent Variables? no no no no no no yes yes yes. yes “yes yes yes. yes
IN 1300 1300 1300 1200 1200 1200 1300 1300 1300 1200 1200 1200 1200 1200
Adjusted R-Square. 0944 0945 0945 0944 0945 0945 0957 0961 0961 0961 0961 0961 0961 0961
INTERCEPT est [EI 18 141 109 129 131 260 SAT 485 445 424 495 339 331
- t 288 218 179 721 818 691 128 239 780 643 4it a77 6 82 440
|LM_CAP_ est x 357 328 306 261 260 x 0459 0475 0426 0435 0 438 0495 0.493
t x 447 413 332 283 281 x Hg 979 949 802 815 log 102
Pp est x x 2 10E-05| x -2 49E-05| -2 87E-05| x x 0035 x 0020 ~0 204 x x
1 x x 401 x 412 -1 50 x x 0537 x 0 266 -229 x x
1_98 est x x x 5 82E-05 | 9 87E-05 | 9 41E-05 x x x 0055, 0057 -0.037 x 0 008
t x x x 0760 129 118 x x x 0929 0.958 -0 592 x 0.134
OBS est x x x x x 4 40E-06 x x x x x 0.257 0180 0179
t x x x x x O212 x x x x x 435 382 370
[Perenden Variable VMT LOG(VMT) |
[Logs of Independent Variables? no 110 10 no no 70 yes yes. yes yes yes yes yes yes
IN 1300 1300 1300 1200 1200 1200 1300 1300 1300 1200 1200
|Adjusted R-Square 0958 0969 0991 0973 0992 0993 0994 0995 0995 0995 0995
INTERCEPT est_ | 1505208 | -1370819| -1824418| -1053630) -1695994) -936079 142 108 485 120 424
L 208 903 -22 08 490 -1478 -747 67) 361 780 187 4il
ILM est x 7358 2251 6277 2006 2157 x 0567 0475 0542 0.435
t x 208 105 173 960 uo x ng 979 984 802
POP est x x 234 x 230 923 x x 0560 x 0585
t x x 535 x 526 754 x x 107 x 939
|PCI_98 est x x x 537 4208 -182 x x x ON 0057
t x x x 0558 0041 3.62 x x x 189 0958
JOBS est x x x x x 157 x x x x x
L x ee ee: x x ng x x x x



NORTH CAROLINA LINEAR FIRST DIFFERENCES CORRELATION MATRIX

VARIABLE DESCRIP VMTDIFF_ | VCAPDIFF LMDIFF LCAPDIFF POPDIFF PCIDIFF JOBDIFF
'VMTDIFF Coefficient 1 0.48262) 0 33826 0.03168 0 56975 0 03382 0.49054
Std Error 0 00001 00001 02728) 00001 0 2625 00001
IN 1200 1200 1200} 1200 1200, 1100) 1100)
VCAPDIFF Coefficient 0 48262! | 0 16071 0 16877 -0 02249) 0.03286! -0 01695
Std Error 00001 0 00001 00001 0 4363 0 2762 05744
IN 1200 1200) 1200 1200} 1200} 1100] 1100
|LMDIFF Coefficient 0 33826) 0 16071 1 05237 023022 00341 0 17283
Std Error 0.0001 0.0001 Ql 0 0001 0.0001 0 2585) 0000!
N 1200) 1200) 1200) 1200} 1200 1100) 1100)
ILCAPDIFF Coefficient 0 03168} 0 16877 05237) 1 -0 06802 007177 -0 02242}
‘Std Error 0 2728 00001 00001 0} 00184 00173 0 4576]
IN 1200} 1200| 1200} 1200 1200; 1100) 1100}
POPDIFF Coefficient 0 56975 -0.02249 0 23022 -0 06802! 1 0 0066] 077536
Std Error 00001 04363 0.0001 00184, 0 0 827; 6.0001
IN 1200 1200} 1200} 1200 1200) 1100) 1100}
PCIDIFF Coefficient 0 03382) 0.03286) 00341 007177 0 .0066| t 0 22412,
Std Error 0 2625) 02762 0 2585] 00173) 0 827 0 0.000!
N 1100} 1100 1100} 1100, 1100} 100 1100)
JOBDIFF Coefficient 0.49054 -001695 0 17283) -0 02242 077536] 0.22412) 1
Std. Error 00001 05744 00001 0 4576 0.0001 00001 0
IN 1100} 1100} 1100} 1100 1100) 1100} 1100)



APPENDIX D: COMPLETE LISTING FOR BASE RUNS

The following run listings relate to the runs presented in Table 3-1, page 3-2 (all five study

areas). They include the intercept coefficients for these models.


MEASURING INDUCED TRAVEL DEMAND

FROM ROADWAY CAPACITY EXPANSION:

AN EMPIRICAL ANALYSIS OF THE

U.S. MID-ATLANTIC REGION

FINAL REPORT



VIRGINIA COUNTIES LOG REGRESSION

Parameter Estimates

Parameter Standard T for HO

Variable DP Estimate Error Parameter=0
INTERCEP a1 4.903998 0 24459659 20 049
LOGLM 1 © 506314 0 03269729 15 485
LOGPOP 1 0 507106 0 01975764 25 666
‘YR_1972 1 © 021943 © 01503921 1 459
YR_1972 1 0 078041 0 01507051 5178
YR_1973 1 0 120630 © 01812123 7 978
‘YR_1974 1 © 077302 © 01519424 5 088
YR_197S 1 0 105404 0 01526148 6 B98
YR_1976 1 0.147884 © 01538101 9 634
YR_1977 1 0 178758 0 01543777 11°579
YR_1978 1 0 219655 © 01555828 14118
YR_1979 1 0 219111 0.01564082 14 009
YR_1980 1 0 192615 Oo 91566276 12.295
YR_1981 1 0 198945 0 01572768 12 649
YR_1982 1 0 219414 0 01577647 13 908
YR_1983 1 0 251050 oO 01580464 15 885
‘YR_1984 1 0.306306 0 01584359 19 333
YR1985 1 0 352296 = 0-015 88993 22.171
YR_1986 1 0 410374 0 01594160 25 742
YR_1987 1 0 457387 0 01604286 28 510
YR_1988 1 0 506266 0 01610099 ‘31 443
YR_1989 1 © 525995 © 01618535 32.498
YR_1990 1 0.529351 © 01626658 +32 542
YR1991 1 0 514269 0 01639924 31 359
YR_1992 1 0.561919 0 01654889 33 955
YR_1993 1 0.598735 0 01665276 35 954
YR_1994 1 0 612382 0 01677854 36 498
YR_1995 1 0 627221 0.01689761 37.119
YR_1996 1 0 649412 © 01698008 38 246
ALBEMARL 1 0 150782 0 03409587 4 422
ALLEGHAN 1 -0.106481 0 03287207 73239
AMELIA 1 -0 O6seass 0.04551381 -1 447
AMHERST 1 -0 098310 0 02929621 -3 356
APPOMATT 1 0 124334 © 03951847 -3:146
ARLINGTO 1 © 422198 0 04623780 9 131
‘AUGUSTA 1 O 286944 @ 03535339 8.116
BATH 1 -0 603252 0 04719268 12-783
BEDFORD 1 -O 153523 0 02960387 5.186
BLAND 1 oO 153885 0 04211595 3 654
BOTETOUR 1 0 391678 0 02962423 13 222
BRUNSWIC 1 -O 008014 0 03189232 -0 252
BUCHANAN 1 -0 210668 0 03247370 -6 487
BUCKINGH 1 -0 247424 © 03486052 -7,098
CAMPBELL, 1 0 012720 0 02932857 0.434
CAROLINE 1 0 638889 © 03146182 20 307
‘CARROLL 2 -0 118877 0 02854657 -4 164
‘CHARLES 1 -0 3504671 0 05597644 -6 261
CHARLOTT 1 -0 383392 0 03376046 -11 356
‘CHESTERP 1 0 185549 0 04211999 4 405
(CLARKE 1 0 092252 0.04063267 2,270
CRAIG 1 -0 962138 0 05303677 -18 141
CULPEPER 1 -0 108313 © 03121287 -3 470
CUMBERLA 1 -0 457245 —0.04839308 -9.4a9

Prob > |T|

© 0001
0.0001
0 0001
0 1447
0 0002
0 0001
0 0001
0 0002
© 0001
0.0002
0 0003
0.0003
0.0002
0.0002
0 0002
© 0002
0 0001
0 0002
0 002
0 a001
0 0002
0.0001
0 0001
0 0001
0 0001
0 0001
0.0001
0 0001
9 0001
0 001
0 co1z
0 1480
0 0008
0 0017
0 0001
0 0001
0 0001
0 0001
0 0003
© 0001
© 8016
0 0001
0.0002
0 6645
© 0001
0 0002
0 0001
© 0001
0 0001
0.0233
0.0001
0.0005
0 0001


Table 2-1

Table 2-2

Table 2-3
Table 2-4
Table 3-1
Table 3-2
Table 3-3
Table 3-4

Table 3-5

LIST OF TABLES

Average County Values of Key Variables in 1995
(by State and Area) a . see

Average Annual! Growth Rates of Key Variables (by State and

Area and First Year to Last Year of Available Data}... we

Data Distributions (Number of Counues in Each Category in 1995) ...... ...

All States Logarithmic Correlation Matrix. .....

Summary of Base Run Results... 0.00. 2 ee wee we saeeeae wo

Summary of First Difference Run Results .. 0... 2 ee ce eee 2 eee

Summary of Short/Long Run Elasticity Results .... 0.0 ces cesses cee oe

Traffic Volume Interaction Models... ic cee cesses ce taeessee sates seeseeneeanens

Population Density Interaction Models seen aeeaneaee

3-2

3-6

3-9


TA ( LIS RES:
Parameter Standard T for HO

Variable DP Estimate Error Parameters0 Prob > |T|
DICKENSO 1 -0 479202 0 :03456219 0 0002
DINWIDDI 1 0 103462 0: 02966179 © 0905
ESSEX 1 0 062416 0 04101660 0 1282
FAIRFAX 1 0756385 0. 05607248 9 0001
FAUQUIER 1. 0 251002 © -02897124 © 0001
FLOYD 1 -0 520774 0 04410608 0 oc.
FLUVANNA 1 -0.435221 0 -04603077 “9 455 0 0002
FRANKLIN 1 ~0.091032 0 02903257 -3 136 0 0017
FREDERIC 1 0 267018 =o: 03010738 a 869 0.0002
GILES 1 -0 196230 0 03268285 -6 194 0 0002
GLOUCEST 1 0 100082 0 :03289075 3043 0.0024
GOOCHLAN 1 0.331881 003336188 9 948 9 0002
GRAYSON 1. -0 672942 0 03186803 -21 117 0.0003
GREENE 1 0 049417 0 05584599 0 885 0 3763
GREENSVI 1 0 575399 0 -:04178496 13 770 9 0002
HALIFAX = 1. 0 283947 0 03006740 -9 444 0 0002
HANOVER 1 0.422235 © 9 :03112330 13 567 0 0002
HENRICO 1 0119102 0: 04697394 2 535 0 0113
HENRY 1 -0 107632 003072868 -3 503 0.0005
HIGHLAND 1. -0 701376 0 -05747350 12 203 0 0001
ISLEWIGH 1 © 220181 =o 03058546 7199 9 0002
SAMESCTY 1 0 175013 0 03202314 5 641 0 0001
KINGQUEER 1 -0 276252 0.04664787 5.922 0.0002
KINGGEOR 2 0.145245 0 -03600938 4.034 © 0002
KINGHILL 2 -0,272034 0 -04508354 6 034 0.0002
LANCASTE 1 -0 312202 0 04345271 -7:185 9 0001
LEE 1 -0 319424 =o 02957219 -10 802 0 0002
LOUDON 1 © 020630 © © :03182479 0 648 0 5169
LOUISA 1 0.057002 © -03021803 1 686 0.0594
LUNENBUR 1. -0 724187 © 6,.04113459 17 605 0 0003
MADISON = 1. -0.047370 © 03833848 1.236 0 2167
MATHEWS = 1 -0 142893 005723165 -2 S01 0 0124
MECKLENB 1 -0 209084 = -0.03122573 6.636 0 0003
MIDDLESE 1 0 151295 0: 04585450 3 299 © 0010
MONTGOME 2 0.083718 0 031692112 2.642 0 0083
NELSON 1 -O 239219 0: 03340672 7-161 0 0001
NEWKENT =. 0 782966 0: 03753853 20 958 © 0002
NORTHAMP 1. -0,123872 0 03603723 3437 0 0006
NORTHUMB 1 -0.218390 0 04791172 -4 558 0 0002
NOTTOWAY 1 -0,397718 0 -:03239285 -12 278 9 0001
‘ORANGE 2 -0.163149 0 03193223 5.109 9.0001
PAGE 1 -0.582768 0 :03686181 15 810 © 9001
PATRICK 3 -0 512657  0.03250935 -15 770 0 g001
PITTSYLV 1 -0,346190 —-0:03314163 -10 446 © 0002
POWHATAN 1 -0 293153 004106649 -7:138 0 0003
PRINCEED 1 -0 228481 — 0:0317810¢ -7.189 0 0002
PRINCEGE 1 0.324370 0 03177059 10 210 © 0002
PRINCEWI 1 0 360030 0 04338184 8 299 0 0002
PULASKI = 1 -0 049555 — ¢-02951896 -1 679 0 0933
RAPPAHAN 1. -0.406489 004264531 9.532 0 0001
RICHMOND 1 0.042385 0 05165076 0.822 0.4119
ROANOKE = 1 0 338432 0 03349956 ao 104 © 0002
ROCKBRID 1 0 400324 =o: 03501542 qn 433 0 0002
ROCKINGH 1 9 023925 0: 03383805 0 707 0 4796
RUSSELL 1 -0 101938 0 :02844604 -3.584 0 0003


one would expect fewer long run impacts from Jand use changes. To test these hypotheses, we
disaggregated the data based upon relative traffic levels (calculated as average daily VMT per
Jane mile) as a proxy for congestion and also on different levels of population density Obviously
this 1s a simplification as congestion effects can be very ume of day specific, but in general, the

greater the level of traffic or the greater the population density, the larger the level of congestion

The data was divided into three groups, defined as “low”, “medium” and “high” levels of travel
per lane mile The data was divided with “low” being defined as below 2000 daily VMT per lane
mule, “medium” being between 2000 and 5000 daily VMT per lane mile, and “hgh” being above
5000 daily VMT per lane mile The year 1990 was chosen as the base year for dividing counties
into groups The problem with using earlier years as a basis for making the allocation is that (a),
several areas’ data extends back only to the mid 1980s and (b) most counties in Virginia and
North Carolina fall into the low density category before 1980 The overall split was 20 high

congestion counties, 120 low congestron counties, and 80 medium congestion counties.

The population density estimates were split by areas with less than 100 persons per square mile
defined as “low”, 100 ~ 500 persons per square mile was “medium”, and greater than 500
persons per square mile was “high” A level of 500 persons per square mile might not be
considered a high population density, but the vast majority of the counties in the analysis have
very low population densities With this split of the levels there were 16 counties in the “high”
category, 66 counties 1n the “medium” category, and 138 counties in the “low” category.

These estimates were done only for the “all states” model since some of the sub-categories for
individual states did not have enough counties within the cross-section. Interaction terms were
entered for the lane mile variables Results are shown in Tables 3-4 and 3-5 for models using

VMT/Lane mile and VMT/capita respectively.
In each table a base mode! and a distributed lag model 1s presented. The population density base

model] suggests that areas with medium population density have the highest lane mile elasticities.

The congestion level model suggests slightly lower elasucities for the least congested areas, as

3-10


<M (_. ----ING

Model MODELL
Dependent Variable LOGVMT

Analysis of Variance

Sum of Mean

Source DF Squares Square P value Prob>P
Model 232 ©2476 18627 10 67322 1779 935 0 0002
Error 2187 13 11415 © 00600
c¢ Total 2419 (2489 30041

Root MSE 0 07744 R-square 0 9947

Dep Mean 13 45850 Adj R-sq 0 9942

cv 0 $7537


ALL COUNTIES LOG REGRESSION (INCLUDING WASHINGTON DC]

Variable

INTERCEP
LOGLM
Locpop
Y¥R_1986
YR_1987
yR_1988
YR_1989
¥R_1990
¥R_1991,
Y¥R_1992
YR_1993
YR_1994
¥R_1995
MDANNEAR
MDBALTIM
MDCALVER
MDCAROLI
MDCARROL
MDCECIL
MDCHARLE,
MDDORCHE,
MDFREDER
MDGARRET
MDHARFOR
MDHOWARD

DF

ferereenen Ser een ete ee ee ee net ee

Parameter Estimates

Parameter
Estimate

4 507597
0 587263
0 519973
© 020195
© 062028
© 112856
0 146675
© 152479
0 161847
0 198955
0 218829
0 237532
@ 262312
0.422808
0.421512
0 183778
-0 078243
0 087738
-0 033707
0 086591
-0 081911
0.329766
-0 064957
-0 011691
0 611405
-0 152430
0 361121
0 465586
© 362219
-0 048321
9.097008
0 441113
0.266961
0 060447
0 235318
0.128838
-0 240257
~0 659206
-0.186396
+0 450838
9 077077
-0 327488
~O 244214
-0 330234
0.226192
@ 207703
0 113359
0 204312
-0 174021
0 120017
© 275739
-0.482002
0 121530

Standard
Error

0 48831683
0 04729827
0 03818200
0 00741931
0 00746200
© 00752326
© 00760849
© 00770586
0 00789412
0 00810659
0 00832352
0 oog6oss2
0 00886717
0 07731033
0 09591121
0 04225013
0 05479928
0 03885937
0 03437522
0 03497409
0 05241145
0 04851943
0 04977609
0 04711814
0 04742235
0 06464479
0 09747666
0 09861418
0 04528363
0 03379238
0 06421800
0 05204627
9 03975223
0 03421068
0 04333126
0 03933654
0 09278981
0 09575930
0 05836345
0 06378146
0 08249318
0 04065115
0.06105171,
0 04930441
9 03619152
9 04732901
0.03604609
0.04133466
0 04249783
0 11878401
0 ossosoas
9 osso1123
0 04368233

T for HO
Parameters0

9 231
12 416
13 618
2.722
8 313
14 068
19 278
19 787
20 502
24 542
26 290
27 602
29.582
5 469
4395
4 350
1 428
2 258
-0 981
2 476
-1 563
6 797
1 305
-0 248
12 893
-2 358
3-705
4.721
7999
71 430
1 $21
8.475
6 716
1.767
$432
3278
=2.589
-6 884
-3 194
+7068
0 934
-8 056
-4 000
-6 698
6 250
4.388
2145
4.943
-4 095
1010
6 122
+7414
2 769

Prob > |T]

0 oo02
0 0001
0 0001
0 0065
0 0001
© 0001
© 0001
© 0001
0 0001
0 0001
0 0001
0 0001
0.0001
9.0002
© 0002
0 0001
0 1535
0 0241
0 3269
0 0134
0 1182
0 0001
0.1920
0.8042
0 0001
0 0185
0 0002
@ coor
0 c001
0 1529
0 1310
@ 0001
0.0002
0 0774
0.0001
© 0011
0 0097
0 0001
0 0014
0 9001
0 3502
0 001
Q 0001
0 0001
0 001
0 0002
0 0017
0 coor
© 0001
0 3124
0.0001
0 0001
0 0087


This 1s not believed to represent a problem since the main issue 1s that the data for VMT match

the data for lane miles 1n terms of road coverage, which it does.

For counties in Maryland, VMT data was only available as a total for all state-maintained roads,
including interstate, primary, and some secondary roads. Lane mile data was available in more
disaggregate form, but EEA used the lane mule total for all state-maintained roads in order to
match it with the WMT data.

Since the DC data was obtained during the current project, a full listing of data for DC is
provided in Appendix C. This Appendix also lists the counties included in the study area added
to this project, the DC/Baltimore metro area. This area uses the county data for the Maryland and

Virginia counties that EEA had obtained previously.


TABLE A-6b
TRAFFIC VOLUME / ELASTICITY RUNS - NORTH CAROLINA

ALL LOW VOLUME MID VOLUME | HIGH VOLUME
Dependent Variable LOG(VMTICAP) | LOG(VMT/CAP) | LOG(VMT/CAP) | LOG(VMTICAP)
N 1200 1100 452 427 652 391 96 82 Traffic Volume Classifications
[Adjusted R-Square 0976 | 0977 | 0963 | 0965 | 0984 | 0984 | 0980 | 0983 Low Volume-  VMT/LM < 2000
INTERCEPT est | 455 393 431 116 408 678 495 363 Mid Volume-  2000< VMT/LM < 5000

tl 213 668 150 135 123 760 687 125 High Volume-  VMT/LM > 5000
|Log (VMT/CAP) est | 0186 | 0162 | 0047 | 0031 | 0301 0268 | 0226 | 0224
lagged 1 year t [797 683 137 0894 824 114 207 193
Log (LM_CAP) est { 0438 | 0424 | 0442 | 0321 | 0406 | 0449 | 0528 | 0527

t 129 0 750, 466 174 800 537 358
Log (POP) est x x x x x x x x

t x x x x x x x x
(ree (PCI_98) est x 0061 x 0284 x -0240 x 0152

t x 123 x 377 x “311 x 0549
[Short-Run Elasticity 0.438 | 0.424 | 0.442 | 0321 | 0406 | 0.449 | 0528 | 0.527
[Long-Run Elasticity 0.538 | 0.506 | 0.464 | 0331 _| 0582 | 0.614 | 0.683 | 0.680

ALL LOW VOLUME MID VOLUME HIGH VOLUME

Dependent Vanable LOG(VMT) LOG(VMT) LOG(VMT) LOG(VMT)
IN 1200 1200 1100 452 452 427 652 652 391 96 96
Adjusted R-Square o997 | 0997 | 0997 | 0993 | 0994 | 0994 | 0995 | 0995 | 0995 | 0996 | 0996
INTERCEPT est | 806 470 461 8.46 137 0.467 690. 468 930 5.46 342

tl] 220 828 509 153 135 0328 132 3.46 589 417 232
LAG VMT est | 0238 | 0187 | 0164 | 0095 | 0045 | 0029 | 0340 | O31 0275_ | 0356 | 0226

t 104 80 69 269 131 0847 954 854 736 351 208
LM est | 0455 | 0426 | 0394 | 0491 | 0550 | 0352 | 0408 | 0373 | 0392 | 0612 | 0610

tl] 1066 1021 8.60 641 751 406 701 635 6.45 301 521
POP est x 0367_| 0373 x 0745 | 0710 x 0246 | 0138 K 0337

t x 761 654 x 664 5.80 x 325 151 x 264
PCI_98 est x x 0055 x x 0.280 x x -0 300 x x

t x x 1107 x x 369 x x “362 x x

hort-Run Elasticity 045s | 0.426 | 0.394 | oan | 0550 | 0.352 | 0.408 | 0373 | 0.392 | 0612 | 0.610

feeoceun Best |_| 05s 0s oan | 0543 | 0577 | 0.362 | 0618 | 0541 | 0540 | 0.951 | 0.789



MARYLAND COUNTIES LOG REGRESSION

Parameter Estimates

Parameter Standard _T for HO
Variable DF Estimate Error Parameters0
INTERCEP 1 3.384472 0 43564161 7 769
LOGLM 2 0 451239 0 05630867 a 014
LOGPOP 1 0 659392 0: 02722223 24 223
¥R_197000 1 0 059339 =o 02155613 2753
YR1971 1 © 107554 002163005 4972
YR397200 1 0 140056 0: 02166655 6 464
YR19730 1 0 192819 = 0 02273125 8.873
YRIS74 0173953 0 02183087 7 968
YRI1S7S 1 0.242054 0 02197282 11 016
YRII976 1 0 272086 «= 0 02211410 12 304
yR71977, 1 © 268203 «0: 02222316 12 069
YRI1978 1 0 336757 0 022340: 15 074
YR2979° 1 9 324160 © -0.02243413 14 450
yR1980 1 0 342304 0 02251620 1S 203
yR19820 2 0 359171 0 02260521 15 889
yRuge2 1 0 357255 0: 02264867 15774
YR1983 1 0 416821 0 02273142 18 337
yR 198401 0 433596 0 02286570 18 963
yRiges 1 © 474027 0 02302536 20 587
1 0.514443 0 02328247 22 096
1 © 552630 0 02360608 23 410
1 0 570250 0: 02383452 23: 925
1 0 580128 =o: 02408274 24 089
1 0 601950 =O 02434215 24 729
2 0 614654 002459094 24 995
a 0 605767 002480347 24 423
1 0 587581 0 02495715 23 S44
1 0 573494 © 0 02514664 22 806
1 0578664 0 02533073 22 844
2 9.590786 0: 02552950 23.141
1 0 259995 0 05688917 4570
1 0 200230 007695523 2 602
1 © 061385 0 03988692 2539
2 -0 014360 © 0 04399737 -0 326
2 -0 038871 0 02081856 -1 867
2 -0 004837 0 02111509 -0 229
1 0 045465 0: 02002806 2271
1 -0 038058 = 9.:04056104 -0 938
1 0 272858 «= 0 04052517 6 733
1 0 066637 0 :03575891 1 863
1 -0 099580 0: 02837145 -3 510
1 0 498216 © 0: 02489466 20 013
1 -0 049903 004758552 +1049
2 0 185748 © 0:07324793 2 536
2 0 231797 0 -:07911833 2 930
2 0 466322 0 :03542492 23 163
1 -0 129395 002115792 -6.116
1 0 222002 0: 05464919 4 062
2 0.510174 = «0 04152211 12 287
2 0 183256 0: 03057296 5 994
WICOMICO 1 0 052422 © 002179146 2 406
WORCESTE 1 0.302824 03157484 9591

Prob > |T

© 0001
0.0001
© 9002
0.0061
0 0001
0 0001
9 0001
0 0001
0 0002
© 0001
0 0001
0.0001
90,0001
© 0001
© 0001
0 0002
9 aoc1
9 G001
0 ao01
9 0001
0.0001
9.0001
0 9001
0 001
© 001
0 coo1
0.0001
0.0001
0 ocor
0 0001
0.0001
0 0095
0 1243
0.7443
0 0624
0 8189
0.0235
0 3485
© 0001
0 0629
0.0005
@ 0003
0.2947
0 0115
0 0035
© 0003
0 001
0 0001
0.0001
0 0001
0 0165
0 0001


VIRGINIA LINEAR FIRST DIFFERENCES CORRELATION MATRIX

VARIABLE _|DESCRIP VMTDIFF_| VCAPDIFF LMDIFF LCAPDIFF POPDIFF PCIDIFF JOBDIFF
VMTDIFF Coefficient 1 0 36432 0 17613) -0 00962 0 62506) 0 11954) 012971
Std. Error 0 0.000! 00001 0 6309] 0.0001 00001 00001
N 2496] 2496) 2496 2496 2496 2496) 2496)
VCAPDIFF =| Coefficient 0 36432) 1 0.02205) 011746] -0 09857 021239 001547
Std. Error 00001 0 0.2708 00001 00001 00001 0.4399
N 2496) 2496) 2496) 2496) 2496 2496 2496]
LMDIFF Coefficient 0 17613] 0 02205. 1 047251 0 15345 0.01582) 0.02024!
Std Error 00001 0 2708: 0 00001 0.0001 0 4294 03121
N 2496) 2496) 2496) 2496) 2496 2496) 2496]
LCAPDIFF Coefficient -0.00962| 011746 047251 1 -0 10848 0 07083) -0 03677]
Std Error 0 6309 0.0001 0 0001 i) 0.000! 0 0004 | 0.0663
N 2496) 2496) 2496) 2496) 2496 2496 2496)
POPDIFF Coefficient 0 62506 -0.09857 0.15345 -0 10848} 1 0 03617) 0 11388
Std Error 00001 0.0001 0.0001 0000! 0 0.0708 00001
IN 2496 2496, 2496 2496) 2592, 2496 2496
PCIDIFF Coefficient 0 11954] 0 21239) 001582 0.07083) 003617 1 0 13472
Std Error 0000! 0000! 0 4294 0.0004 00708 0 00001
N 2496 2496, 2496} 2496) 2496 2496) 2496)
JOBDIFF Coefficient 012971 001547 0.02024) -0 03677 0 11388 0 13472) 1
Std Error 0.0001 0.4399) 03121 00663 00001 00001 0
N 2496) 2496 2496) 2496) 2496) 2496) 2496]



In summary, the results of EEA’s analysis suggest that there 1s a strong relationship between the
level of lane miles and leveis of travel, although this does not prove causality. Even with the
significant results for lagged models, suggesting that increases in lane miles tend to come before
increases in travel, 1t could be argued that planners are simply doing their jobs and properly
anucipaung future growth in travel by providing the new roadway capacity ahead of ume More
research into travel response in congested v uncongested conditions, and long term growth in

areas with roadway expansions, may shed more light on this very difficult quesuion

This research indicates a number of different avenues for potential additional research.

Certainly, the study of additional states and metropolitan areas wil] add to the body of literature
and may help improve the understanding of how the relationship between lane miles and travel 1s
affected by characteristics that can be isolated in each area. However, perhaps more interesung
would be a deeper analysis of the potential variations in the VMT/lane mile relationship in the
current study areas (or other areas), including an analysis of what kind of lane miles are being
added in different counties, and how this relates to the types of responses in these counties
General growth patterns and land use patterns would also be interesting correlates. However, the
major problem in conducting such an analysis 1s that more detatled data would be needed. It may
be difficult to obtain most of this data from many state agencies, but may be available in some
cases. In any case, if 1s an area of research that 1s worth investigating in the future An even
more disaggregate analysis, such as within a metropolstan area with data at the roadway level of
aggregation, may allow for greater separation of various effects. The problem at this level, of
course, 1s separating roadway segment-specific effects (which may be offset by changes in other

roadway segments) from regional effects that are closer to representing “equilibrium” results.

4-2


TABLE A-4b
ELASTICITY RUNS (SHORT / LONG RUN) - NORTH CAROLINA

Dependent Variable LOG(VMT/CAP)
IN 1200 1200 1100 1100 1100 1100 1100
Adjusted R-Square. 0976 0976 0977 0977 0977 0977 0977
INTERCEPT est 455 475 393 470 503 396 355
t 213, 838 668 319 530 896 553
Log (VMT/CAP) est 0186 0185 0.162 0162 0154 0158 0158
lagged | year t 797 795 683 683 646 663 663
Log (LM_CAP) est 0438 0429 0424 0.396 0.402 0.458 0446
t 129 103 110 865 879 116 108 |
Log (POP) est x -0022 x 0072 ~0174 x x
q x 0387 x =112 “227 x x
Log (PCI_98) est x x 0061 00st 0.005 x 0044
t x x 123 103 0091 x 0367
Cog GOBS) est x x x x 0123 0070 6 13E-02
t x x x x 244 169 144
0.438 0.429 0.424 0.396 0.402 0.458 0.446
0.538 0.526 0.506 0.473 0.475 0.543 0.530
Dependent Variable LOG(VMT)
IN 1200 1200 1100 1100 1100 1100 1100
dyusted R-Square 0997 0997 0997 0997 0997 0997 0997
INTERCEPT est 806 470 902 4610 4937 669. 740
t 221 828 1460 309 3.40 1296 1103
Log (VMT) est 0238 0187 0.206 0164 0156 0174 0169
ee t 104 80 a9 69 65 73 7
Log (LM) est 0.455 0.426 0432 0394 0.400 0418 0424
t 10 66 1021 932 860 874 O17 929
Log (POP) est x 0367 x 0373 0275 x x
t x 761 x 634 393 x x
1g (PCI_98) est x x -0.039 0055 0.009 x =0.080
t x x 0789 im 018 x “165
[Log JOBS) est x x x x 0121 0.226 0237
t x x x x 239 553 372
hort-Run Elasticity 0.455 0.426 0.432 0.394 0.400 0.418 0.424
ng-Run Elasticity 0.597 0.525 0.545 0.471 0.474 0,506 0511



TABLE A-6a
TRAFFIC VOLUME / ELASTICITY RUNS - MARYLAND

ALL LOW VOLUME MID VOLUME HIGH VOLUME ||
[Dependent Vanable LOG(VMT/CAP) LOG(VMT/CAP) LOG(VMTI/CAP) LOG(VMT/CAP)
IN. 621 621 177 77 325 325 119 9 Traffic Volume Classifications
[Adjusted R-Square 0964 0964 0962 0962 0961 0961 0990 0.990 Low Volume - VMT/LM < 2000
INTERCEPT est 191 142 278 3.48 233 183 219 507 Mid Volume - 2000< VMT/LM < 5000
t 105 253 549 232 803 178 414 362 High Volume- =.VMT/LM > 5000
lLog (VMT/CAP) est_| 0556 | 0556 | 0273 0275 0486 | _0.489 0640 | 0544
lagged | year t 159 159 319 320 973 971 840 630
|Log (LM_CAP) est 0159 0 166 0215 0219 0.203 O21 0217 0 262
t 620 617 275 278 458 445 344 404
Log (POP) est x x x x x x x x
t x x x x x x x x
Log (PCI_98) est x 0.055 x -0071 x 0056 x -0.242
t x 0910 x -0.494 x 0.499 x 222
\Short-Run Elasi 0.159 0.166 0.215 0.219 0.203 0.211 0.217 0.262
Long-Run Elasticity 0.357 0.374 0.296 0.302 0.395 0.413 0.604 0.575
ALL LOW VOLUME MID VOLUME HIGH VOLUME
Dependent Vanable LOG(VMT) LOG(VMT) LOG(VMT) LOG(VMT)
IN 621 621 621 177 77 177 325 325 325 119 19 9
Adjusted R-Square 0996 0997 | 0996 0972_ | 0974 | 0973 0985 0987 0986 0995 0996 | 0996
INTERCEPT est 230 148 128 862 582 622 476 286 261 240 240 457
t 345 362 204 710 398 319 556 314 192 239 245, sx
LAG YMT est_| 0768 0359 | 0559 0322 | 0231 0232 0649 | 0500 | 0501 0765 0 668 0597
t 291 160 160 379 266 266 155 988 985, 113 863 729
ILM est 0143 0215 0215 0100 0.088 0091 0001 0140 0145 0 182 0 182 0275
L 260 409 410 104 0943 0970 0010 120 122 213 218 301
[POP est x 0 288 0284 x 0 362 0361 x 0 280 0275 x O14 0170
t x 855 805 x 320 317 x 489 450 x 240 323
PCI_98 est x x 0026 x x -0.044 x x 0.028 x x 0248
t x x 0.428 x x 0310 x x 0248 x x =225
|Short-Run Elasticity 0.143, 0.215 0.215 0.100 0.088 0.091 0.001 0.140 0.145 0.182 0.182 0.275
Long-Run Elasticity 0.617 0.488__| 0.489 0147 | 0.114 [| 0119 | -0.003 [0.280 | 0.291 0.775_| 0.547 682_]|



TABLE A-4c
ELASTICITY RUNS (SHORT / LONG RUN) - VIRGINIA

Dependent Variable LOG(VMTICAP)
IN 2496 2496 2496 2496 2496 2496 2496
(Adjusted R-Square 0984 0984 0984 0984 0.984 0984 0984
INTERCEPT est 102 0723 0806 0512 0515 0 884 0754
t 139 472 425, 221 222 919 394
Log (VMTICAP), est 0817 087 0816 0816 0815 O8!5 0815
[lagged | year t 732 733 B0 731 730 730 29
Log (LM_CAP) est OH2 0147 ons 0147 0148 0120 0120
t 954 745 960 749 733 976 975
ILog (POP) est x 0.045 x 0.045 0038 x x
t x 221 x 221 183 x x
Log (PCI_98) est x x 0024 0023 0017 x 0015
t x x 123 12 0854 x 0783
Log GOBS) est x x x x 0014 0019 0018
t x x x x 157 221 2.00
IShort-Run Elasticity 0.112 0.147 0.113 0.147 0.148 0.120 0.120
[Long-Run Elasticity 0,609 0.801 0.611 0.802 0.803 0.650 0.649
= Vanable LOG(VMT)
IN 2496 2496 2496 2496 2496 2496 2496
[Adjusted R-Square 0996 0997 0996 0997 0997 0996 0996
INTERCEPT est 0891 0.489 0492 0118 O12 0719 0448
t 6.49 331 225 0531 0541 493 205
lbog (VMT) est 0876 0841 0875 0.840 0840 0.868 0868
lagged 1 year t 90.0. 779 898 778 776 872 872
Cog (LM) est 0134 0128 0135 0129 0130 0135 0 136
t 702 678 709 686 688 710 74
Log (POP) est x 0.086 x 0.086 0.082 x x
t x 703 x 699 634 x x
Log (PCI_98) est x x 0044 0041 0037 x 0032
t x x 233 222 197 x 166
Log (OBS) est x x x x 0.008 0.028 0.025
t x x x x 0920 348 3.05
[Short-Run Elasticity 0.134 0.128 0.135 0.129 0.130 0.135 0.136
Long-Run Elasticity 1.08 0.808 1.08 0.811 0.811 1.02 1.03



WASHINGTON DC - BALTIMORE EXPANDED METROPOLITAN AREA
LOG FIRST DIFFERENCES CORRELATION MATRIX

VARIABLE | DESCRIP | L_VMTDFF| L_VCPDFF | L_LMDIFF | L_LLMCDFF | L_POPDFF | L_PCIDFF | L_JOBDFF
L_VMTDFF |[Coefficrent | 0 92897 006175 -0 10456 0 24646 0 29974 0 24666
Std Error 0 00001 0 2088] 0033 0 0001 0.0001 00001
N 416 416) 416) 416 416) 416) 416
L_VCPDFF |Coefficient 0 92897 1 0.04094 0 11494: -0 12977 0 3074) 013518
Std Error 9000! 0 0 405 0019 0.008 0.0001 0.0058
N 416 416 416 416 416 416 416
L_LMDIFF {Coefficient 006175 0.04094 1 0 77867! 0 05822 -0 02857 -0 00205
Std Error 0 2088) 0 405) 0 00001 0 2361 05611 0 9667
IN 416) 416 416 416) 416 416 416)
L_LMCDFF |Coeffictent -0 10456) 011494 077867 1 -0 58103! -0.02208| -0:19451
Std Error 0033 00191 00001 ty) 00001 0 6534 00001
IN 416 416 416 416) 416 416 416)
L_POPDFF |Coefficrent 0 24646 -0 12977 0 05822 -0 $8103 I -0 00193) 0 30682
Std Error 00001 0 008 02361 00001 0 0 9687) 00001
N 416 416 416 416 416 416 416
L_PCIDFF |Coeffictent 0 29974 0 3074 -0 02857; -0 02208 -0 00193 1 0 39408
Std Error 0.0001 0.0001 05611 06534 0 9687 0} 00001
IN 416 416 416) 416 416 416 416
IL_JOBDFF |Coefficient 0 24666) 0 13518 -0 00205 -0 19451 0 30682 0 39108: 1
Std Error 0000! 6 0058 0 9667, 00001 00001 0.0001 Q
N 416 416) 416 416 416 416} 416



ALL COUNTIES LOG REGRESSION [INCLUDING WASHINGTON DC)

Variable DF

‘VASOUTHA
VASPOTSY
VASTAFFO,
VASURRY
VASUSSEX
VATAZEWE
‘VAWARREN
‘VAWASHIN
VAWESTMO
VAWISE

1
rf
1
1
2
2
1
1
2
2
a
1
1
1

Parameter
Estimate

0.163450
0.576666
0.740435

-0.247539
0.677467

+0 153227
0.087642
0.229952

-0.751582

-0.178450
1.076086
0.685535,
0 328637
0.173034

Standard
Error

0 07080442
0.04729181
0.04802066
0 12104506
0 08440212
0.04228956
0 07637712
0.04061402
© 07005920
0 04683239
0.07586208
0 05905185
© 04025034
0 08847046

T for HO:
Parameter=0

2.308
12.194
15.422
-2.219

8.027
23.741
1147
$.652

-10.728
+3.810
14.185
11.609

8.165
1.956

Prob > |T|

0.0211
0.0001
0 0002
0.2230
0.0001
0.0002
0.2513
0.0001
0.0001
0.0002
0.0001
0 0001
0.0002
0.0506


TABLE A-2c
BASE RUN RESULTS - VIRGINIA

Dependent Variable VMT/CAP LOG(VMT/CAP)
[Logs of Independent Vanables? 70 10 TO 10 no TO yes yes yes yes yes yes. yes yes.
IN 2592 2592 2592 2592 2592. 2592 2592 2592 2592 2592 2592 2592 2592 2592
|Adjusted R-Square 0 884 0 887 0 889 0.887 0 890 0890 0930 0947 0946 0947 0947 0947 0947 0947
INTERCEPT est 144 913 101 817 747 752 272 499 490 397 389 388 458 382
L 156 822 913 603 556 560 106 S77 200 122 982 981 324 116
ILM_CAP. est, x 524 500 531 S17 509 x 0496 0 506 0498 0 508 0510 O519 OSI7
t x B44 3h 852 838 817 x 272 155 274 156 157 270 270
POP est Ey x 73 39E-05| x -3 94E-05] -3 90E-05) x x 0013 x O03 -0:009 x x
t x x -686 x -763 752 x x 0385 x 0366 -0 261 x x
PCI_98 est x x x 8 26E-05 | 2 43E-04 | 2 48E-04 x x x ono ono 0089 x 9089
1 x x x 124 353 359 x x x 325 325 256 x 257
JOBS est x x x Ey x -4 63E-06| x i x x x 0 048 0055 9047
t x x x x x -0 949 x x x x x 304 364 305
[Dependent Variable VMT LOG(VMT) |
[Logs of Independent Variables? no. no | __no no no} 10 yes yes yes yes yes yes yes yes
IN 2592 2592 2592, 2592 2592 { 2592 2592, 2592 2592 2592 2592 2592 2592 2592
|Adjusted R-Square. 0873 0916 0985 0923 0985 0985, 0982 0984 0 987 0984 0987 0 987 0985 0985
INTERCEPT est 339296 | -2371133| -930863 | -2927310) -1035005|-1018985] 129 916 490 778 389 388 754 703
t 412 234 -204 279 206 -204 475 AS2 200 189 982 98 315 173
ILM est x 10274 3244 9599 3229 3193 x 0 667 0 506 0658 0 508 0510 0646 0647
t x 356 234 344 230 229 x 185 Iss 186 156 157 184 184
POP est x x 186 x 183 183 x x 0507 x 0504 0 480 x x
t x x 10s x 100 101 x x 257 x 256 226 x x
|PCI_98 est x x x 681 105 942 x x x 0147 die - 0089 x 0059
t x x x 145 485 436 x x x 384 325 | 256 x 155
JOBS. est x x x x x 0 908 x x x x x 0048 0 185 0 180
t x x x x x 600 x x x x x 3.04 18 3



Variable DP

CUMBERLA
DICKENSO
DINWIDDI
ESSEX
FAIRFAX
FAUQUIER

JAMESCTY
KINGQUEE
KINGGEOR
KINGWILL
‘LANCASTE
LEE
‘LOUDON
LOUISA
‘LUNENBUR
MADISON
MATHEWS
MECKLENB
MIDDLESE

RAPPAHAN
RICHMOND
ROANOKE,

ROCKBRID
ROCKINGH

PEEP PPP EE

Parameter
Estimate

-0 440047
-0 483307
0 090172
0 056289
0 686269
0 201156
-0 513895
-0 440139
-0 091461,
© 248516
-0.197607
0 080524
0 288913
+0 659185
0 053318
0 581644
-0 279351
0 384212
0 070288
-0 120557
-0.701709
0 201911
0 142379
-0 285294
0 109915
+0 300691
-0 348842
+0 294875
-0 033306
0 044635
-0 712702
-0 043723
+0 167340
-0 208047
0 126407
0 092233
+0 235839
© 753520
-0 117153
-0 238095
+0 400317
-0 102246
-0 584082
-0 510212
-0 352444
-0 302253
-0 214573
0 323387
0 330905
-0 045803
-0 428106
0 036420
0 304952
0 400302
0 014086

Standard
Error

0 04838296
0.03451882
© 02988653
0,04098121
0 06939297
0 03274133
0 04607212
0 04596711
0 02897696
0.03077108
© 03262468
© 03337593
0 03583306
0.03208786
0.05575140
0 04174887
0.03004290
© 03319917
© 04923693
0 03092700
0 05736291
0 03104127
0 03255512
0 04664133
0 03755244
0 045e54a4
0 04481338
0 03046914
© 03584836
© 03039962
0.04120760
0 03828115
0 05751705
© 03116724
0.04630635,
© 03173871
0 03335865
0 03854918
0.03602736
0 04820334
0 03234038
0.03240942
0 03679305
0 03245549
0 03313390
0 04108320
0 03200799
0 03171086
0 04421846
0 02948479
0 04308120
0 05158405
0 03498546
0 03494799
© 03390868

-NIA -2-.--ES --- ..3REL_--.

T for KO
Parameter=0

-9 260
+14 001
3017
1374

9 890

6 144
-11 660
+9875
+3156
7979
-6.249
2.413

8 063
-20 543
0 956
13 932
79.298
11.573
1.428
-3.898
+12 :233
6 505
4373
-6 117
2 927
-6 557
-7 784
-9 678
0.929
1.468
-17 295
-1 142
-2 909
-6 675
2773
2 906
-7 070
19 547
+3 252
-4 939
-12 378
+5 623
-15 875
-15 720
-10 637
-7: 387
+6 704
10.198
7 493
-1 553
-9 937
0.706
8 717
11 454
0 415

Prob > [TI

0.0002
0 0001
0 0026
0 1697
0.0001
0 0001
0 0001
© 0002
0.0016
0.0001
0 0001
0 0159
0.0001
0 o001
0 3390
0 0001
0 0001
0 0001
0 1535
0 on01
0 0002
0 0003
0 0002
0 0001
0 0035
0 0002
0 0002
0 0002
0 3529
0 1422
0 9002
0.2535
0 0037
© 0001
0.0086
0 0037
0 G001
0 0001
0 0012
0 002
0 002
0 0001
0 0001
0.0002
0 0002
© 0003
@ 0002
© 0002
0 0002
0 1204
0 0002
0 4802
0 0002
0 0002
0 6779


TABLE OF CONTENTS

Page

1 INTRODUCTION AND BACKGROUND .. wececece oct tte ete cee tees LD
1.1 Induced Demand: The Issue and Underlying Economy Theory .. 1-2

1.2 Analysis Methodology «0... ..csccessecsssesse e cetsese os ceseeseenteen os . we 1-6

1.3) Data Descraption o.oo. ecessesee cece cosceten oo coeeaces cassecseee sete saeeeeaneareateneay ae . 18

2. PRELIMINARY DATA ANALYSIS . «0.00.00. estes eects te teeter eee Qe]
3 RESULTS OF ECONOMETRIC ANALYSIS . 3-1
31. Base Model Run Results ...... ... . 3-1

32  Short/Long Run Elasticity Run Results . 3-7

3.3. Low/High Traffic Volume Run Results .. 3-8

4. SUMMARY AND CONCLUSIONS .... cesses sstesess e cssettesse cesteesssiees oe ot seesseesee 4-1
R-1

APPENDIX A. DETAILED RUN RESULTS
APPENDIX B: CORRELATION MATRICES
APPENDIX C DATA LISTING FOR DC AND DESCRIPTION OF DC METRO AREAS

APPENDIX D: COMPLETE LISTINGS FOR BASE RUNS


NORTH CAROLINA COUNTIES LOG REGRESSION (PRIMARY ROADS ONLY)

Variable DP

WASHINGT 1
WATAUGA 1
WAYNE 1
WILKES 1
WILSON 2
YADKIN 1
‘YANCBY 1

Parameter
Estimate

-0 200326
+0 117204
0.334317
225160
-0.007913
0.110196
-0.484492

Standard
Error

0.13530050
0 07839499
0,03285602
0.04741566
0.04364529
0.08398633
0 12896225

T for Ht
Parameters

1,482
-1.495
-10.495
74.749
-0,182
1.312
-3.757

Prob > |T|

0.1390
0.1352
0.0001
0.0001
0 8562
0.1898
0.0002


VIRGINIA LOG CORRELATION MATRIX

VARIABLE _|DESCRIP LOGYMT_| LOGVCAP. LOGLM_|LOGLMCAP|LOGVMTLM|_LOGPOP_|LOGPOPDNJLOGLMURB] LOGPCI98 [ LOGJOBS | LOGGAS93
LOGYMT Coefficient 1 0 26044| 0 77408 -0 62378 0 86651 0 87958 0 70391 -0 56678) 0 56353] 0 55452| -0 14549
Std Error 0 0000! 0.000! 00001 00001 00001 00001 00001 0000! 00001 0 0001
N 2592 2592 2592 2592) 2592) 2592 2592 2592 2592 2592) 2496)
ILOGVCAP Coefficient 0 26044, | 0 19073 0 44844, 0 23425) -0 23025 -0 26103 019071 0 22463 -0 03382| -O 22887,
Std Error 0.0001 0 0.0001 00001 0.0001 00001 00001 00001 0.0001 0 0852| 0 0001
N 2592! 2592) 2592! 2592| 2592) 2592) 2592) 2592| 2592 2592) 2496)
LOGLM Coefficient 0.77408) 0 19073} 1 -0 19954 0.35474) 0 68623 032041 -0 25436: 0 22439 0 48373) -0. 01904)
Std Error 0.000) 0000! 0} 0.0001 00001 00001 00001 00001 00001 0.000! 0 3417]
N 2592 2592 2592 2592) 2592 2592 2592| 2592 2592 2592 2496)
ILOGLMCAP = [Coefficient -0 62378] 0.44844 -0 19954] 1 -0 7639 -0 84969) -0 89678 OTII78 -0 45339 -0 42472) 0.03183
Std Error 0 000! 00001 0.0001 0} 0.0001 0.0001 0000! 00001 0.0001 0.0001 01119]
N 2592| 2592) 2592! 2592) 2592) 2592, 2592, 2592) 2592 2592 2496
ILOGVMTLM Coefficient 0 86651 0 23425 0 35474 -0 7639 1 0.75794) 078693) -0 63649} 0.65533} 0.43753) -0 20026
Std Error 0.0001 00001 0.0001 00001 0] 0.0001 00001 0.0001 0.0001 0.0001 0.000!
N 2592| 2592 2592| 2592 2592| 2592 2592 2592) 2592 2592 2496)
LOGPOP Coefficient 0 87958] -0 23025, 0 68623, -0 84969) 0.75794 I 0839, ~0 66524; 045731 057557 -0 0339
Std Error 0.0001 0.0001 0.0001 00001 0.0001 0 00001 0.0001 0.0001 0.0001 0.0904
N 2592 2592 2592| 2592| 2592 2688 2688 2592) 2592) 2592 2496)
LOGPOPDN = |Coefficient 070391 -0 26103, 0 32041 -0 89678) 0 78693) 0839 1 -0.77879 0 58345! 0 47825 -0 03202
Std Error 0.000! 0.0001 00001 0.0001 0.0001 00001 cy] 6.0001 00001 0.0001 0 1097
N 2592 2592 2592 2592, 2592) 2688! 2688 2592 2592) 2592| 2496)
LOGLMURB |Coefficient -0 56678) 0 19071 -0 25436) 071178) -0 63649} -0 66524) -0 77879] 1 -0 48651 -0 47159] 0.03459)
Std Error 00001 0.0001 0 0001 00001 00001 00001 00001 0 0 0001 00001 0 0841
IN 2592| 2592! 2592 2592; 2592 2592) 2592 2592, 2592, 2592) 2496)
LOGPCI98 Coefficient 0 56353 0 22463) 0 22439) -0 45339) 0 65533) 045731 0 58345 -0 48651 1 0 35982 -0 26565!
Std Error 0.0001 0.0001 0.0001 00001 00001 0.0001 0.0001 0.0001 0 00001 0.0001
IN 2592) 2592! 2592! 2592 2592| 2592 2592! 2592 2592 2592 2496)
LOGJOBS Coefficient 0 55452 -0.03382| 0.48373 -0 42472, 0.43753} 057557 0.47825 -0.47159 0 35982 1 -0 0526)
Std Error 0.0001 0.0852 0.0001 0 0001 00001 0 0001 0.0001 0.0001 00001 0} 0 0086}
IN 2592 2592 2592! 2592 2592 2592! 2592| 2592 2592 2592 2496)
ILOGGAS98 | Coefficient -0 14549) -0 22887) -0.01904 0.03183) -0 20026) -0 0339 -0 03202) 0.03459] -0 26565 -0 0526) 1
Std Error 0.0001 00001 03417 01119) 00001 0.0904 0 1097 00841 0.0001 0 0086} 0
IN 2496, 2496) 2496 2496) 2496) 2496) 2496) 2496) 2496) 2496 2496,



TABLE A-5c
TRAFFIC VOLUME RUNS (LOW / MID / HIGH VOLUME ) - VIRGINIA

ALL. LOW VOLUME ‘MID VOLUME, HIGH VOLUME.
pendent Variable LOG(YMTICAP) LOG(YMTICAP) LOG(YMTICAP) LOG(VMTICAP)
iN 2592 759% 2592 1352, 1352 1352 1054 1054 10s4 186 186 186 Traffic Volume Classificntinns
[Adjusted R-Square os | 0947 | 0947 | 0923 | oa | asa | 0961 | 0971 | a972 | o900 | 0934 | 098s
INTERCEPT. et | 272 499 397 2 364 594 280 494 134 283 658 384 Low Volume VMT/LM < 2000
I t 106 377 122 7387 357 152 818 296 240 354 250 6a Mid Volume - 2000< VMT/LM < 5000
Log (LM_CAP) | est x 0496 | 0498 x 063s | O64 x 0478 | 0504 x 0692 | 0689 High Volume VMT/LM > 5000
t x 22 274 x 139 189) x 77 188 x 146 146
[Log (POP) est x x x x x x x x x x x x
t x x x x x x x x x x x x
[Log (PCI_98) ext x x O10 x x 0033 x x 0398 x x 0234
i x x 325 x x 0853 x x 664 x x 167
ALL. LOW VOLUME, ‘MID VOLUME, HIGH VOLUME
Dependent Variable LOG(¥MT) LOG(YMT) LOG(VMT) LOG(VMT)
IN 2592, 2592 | 2592 2392 1352 1352 1352 1352 10s4 1054 1054 1054 186 186 186 185
lAdwusted R-Square 0982 | 0984 | 0987 | 0987 | o977 | 098 | o981 , 0981 | 0974 | o975 | o980 | oom | o986 | 0990 | 0991 | O991
INTERCEPT. est | 129 916 490 389 129 942 sas | 613 128 106 625 265 12 103 813 106
i] 47s 452 200 982 28 m3 1086 965 376 345 160 EEC 213 274 124 708
[Log (LM) est x 0667 | 0506 | 0508 x 0626 | 062 [0621 x | o4oz [ 0330 | 0301 x os79 | 0549 | Osal
t x 15 | 155 156 x 1250 1270 | 1269 x 724 662 620 x 766 760 753
[Log (POP) est x x | 0507 | 0508 x x 0352 | 0354 x x 0473 | 0424 x x 0227 | 0227
t x x 257 256 x x 773 777 x x 156 141 x x 403 406
|[Log (PCi_98) est x x x O10 x x x 0.033 x x x 0455 x x x 0255
t x x | 3x 325 x x x 0.837 x x x 753 x x x 185



AORTA UnnUbIN.. Cn TIES Lue

variable DF

GREENE
GUILFORD
HALIFAX
MARNETT
HAYWOOD
HENDERSO
HERTFORD
HOKE
HYDE
IREDELL
JACKSON
JOHNSTON
JONES
LEB
LENOIR
LINCOLN
MACON
MADISON
MARTIN
MCDOWELL
MECKLENB
MITCHELL
MONTGOME
MOORE
NASH
NEWHANOV
NORTHAMP
ONSLOW
ORANGE
PAMLICO
PASQUOTA
PENDER
PERQUIMA

PEP PPP PPP PP PE PE PP PP PE

Parameter
Estimate

-0.322067
© 015606
-0 127642
-0 151904
0 218910
© 010876
-0 422712
-0 329239
-0.769563
0 313124
-0 039685
0 154765
0 083335
-0 066894
-0 215791
-0 265999
-0 297609
-0 631069
-0.338561
0 138399
0.142858
-0.550911
-0.154045
-0 316897
0 089492
-0 127848
-0 038493
-0 412699
0 117156
-0.350542
-0,448529
-0.040302
-0.277707
-0,.582866
-0.382774
0 045632
-0,156691
-0 352612
0 009267
+0 253853
0.113342
-0.422249
+0 242563
-0.229340
-0 377658
-0 730350
0 046308
-0 234695
0.273623
-0,516641
-0 289758
-0.103256
0.069966
-0.392074
+0 222636

eeevc0c ee CacD DCD OBC OCOD DDD DDO O OOD OOOO DODO ODD ODDR0RD0S0ER00

REGRessiun (Pkimarr ROAvS uu)

Standard T for HO.

Error Parameter=0 Prob > |T|
103599868 -3°109 © 0019
07086893 0.220 0 6257
04832064 -2 642 0 0084
03466217 -4 382 0 0001
05106389 4 287 © 0001
03709047 0 293 0 7694
08628835 -4 687 © 0001
09227203 -3 568 0 0004
15707353 -4 899 0 0001
03270748 9 573 0 0002
07586428 +0 523 0.6010
04496329 3 442 0 0006
12978904 0 642 0 S209
05998164 2.215 0 2650
04168376 -5 177 0 0003
05161626 +5 153 0 0002
08260690 -3 603 © 0003
09851259 6 406 © 0001
07928852 +4 270 © 0001
06241211 2 218 0 0268
08758794 1 631 0 1032
11038008 -4 992 0 0001
06332869 -1 849 0 0648
04012582 7,898 © 0001
04304192 2079 0 0378
03007214 -4 251 0 9001
08911113 -0 432 0 6658
02936072 14 056 @ 0001
02810739 4.168 © 0001
12512088 -2,802 0.0052
07928607 +S 657 0 0001
07503251 70.537 0 5913
13729138 -2 023 0 0433
07268394 -8 019 0 0001
03289150 -11 637 0 0001
10926553 0.418 0 6763
03065072, $112 © 0001
05341740 76 601 0 0001
04134323, 0 222 0 8246
03272082 -7.758 0 0001
02588007 -4 380 0 0001
04261152 +9 909 0 0001
05452665 -4 449 Q 0001
06792183 3.377 oO 0008
04732003 -7 «981 0 0001
06023559 12 125 0 0001
03931930 1178 0.2392
12257270 +2 915 0.0558
07941144 -3 446 0.0006
17878293 -2.890 0.0039
02831952 -10.232 0 0001
06310383 71,636 0 1020
08095530 0.864 0 3876
09903389 3.959 0.0001
11339172 1.963 0 0498


TABLE 3-2

SUMMARY OF FIRST DIFFERENCE RUN RESULTS

Dependent Vanable LOG(VMT) Difference*
State All States Maryland North Carolina Virginia DC Baltimore
Years of Data 1986-1995 1970-1996 1986-1997 1971-1996 1971-1996
N 2200 2200 621 621 1200 1100 2496 2496 416 416
Adjusted R-Square 0.053 0055 0175 0181 0129 0.131 0184 0186 0328 0.328
Intercept est 0.006 0.005 0.058 0.057 -0.020 | -0027 0.034 0031 0 068 0.064
t 0275 0 238 3.01 295 -0 874 “Li 272 243 3.97 3.26
Log (Lane Miles Difference) est 0.434 0433 0.517 0527 0609 0.612 0 149 0145 0.153 | 0154
t 5.84 583 340 3.47 6.95 677 3.56 3.45 166 | 166
[Log (Population Difference) | __est 0.067 0075 0114 0 243 0281 0.372 O47 0.143 0.347 0379
t 0.485 0535 0 423 0877 0989 117 2.21 267 188 192
Log (Income Per Capita est. x 0.023 x 0 257 x 0.095 x 0.103 x 0 062
Difference) (19983) t x 0334 x 203 x 102 x 273 x 0 454

* Difference denotes the differences of the logs of the variables (year t minus year t-1)



we

TABLE 3-1
SUMMARY OF BASE RUN RESULTS

Dependent Variable LOG(VMT)
State All States Maryland North Carolina Virgima DC - Balumore
combined metro area
Years of Data 1985-1995 1969-1996 1985-1997 1970-1996 1970-1996
IN 2420 2420 644 644 1300 1200 2592 2592 432 432
Adjusted R-Square 0710 0713 0 948 0.948 0 856 0 838 0 883 0 884 0 963 0963
Intercept est 451 221 3.38 319 485 4.24 490 3.89 609 527
t 9.23 3.01 777 462 780 4ii 200 9.82 136 573
Log (Lane Miles) est. 0.587 0 564 0451 0.451 0.475 0435 0.506 0 508 0331 0327
t 124 9 801 8.00 9.79 802 15.5 156 617 610
Log (Population) est. 0.520 0 569 0659 0.655 0.560 0 585 0507 0.504 0.518 0 502
t 136 143 242 220 107 9.39 25.7 256 170 160
Log (Income Per Capita) est. x 0.195 x 0.026 x 0.057 x 0110 x 0 167
(1998$) t x 418 x 0 369 x 0958 x 325 x 187



MORTH UonvbINh vue TIES sans nEGRivoa,

» (PRarans ROALS veut)

Parameter Standard _T for HO
Variable DP Estimate Brror Parameters0 Prob > |[T|
GRANVILL = 1 0 052808 =o: 07341272. ong 0.4721
GREENE 1 -0.290678 0: 12527803 -2 320 0 0205
GUILFORD 1 0 009629 0. 08370793 0.225 D 9084
HALIPAX = 1 -0 082676 0 :06298339 -2 313 0 1896
HARNETT = 1. -0 124830 0: 04636056 -2 693 9 0072
HAYWOOD 1 0 252748 © -0.:06181919 4 089 © 0002
HENDERSO 1 0011811 0 04023815, 0 294 0 7692
HERTFORD 1 -0 380291 0: 10720361 -3 547 © 0006
HOKE 1 -0 308427 © o:11496476 -2 683 0.0074
HYDE 2 +0 705379 0: 18933152 -2:726 0 0002
IREDELL = 1 0 330902 0 03608671 9 170 ¢.0002
JACKSON 1 © 001168 0 094930S0 0.012 0 9902
JOHNSTON 1 0 188893 0 05192949 3637 0 0003
JONES 1 0.125218 =o: 15409289 0 813 0 4166
LEE 1 -0 065971 0 06799164 -0 970 0 3321
LENOIR 1 +0 195808 = 0: 04955491 3952 © 0001
LINCOLN 1. -0 255821 0 05930152 4314 © 0002
MACON 1 -0 264660 0: 09952423 2 659 0.0079
MADISON = 1 -0 588492 0 12253758 “4.803 0 0002
MARTIN 1 +0 299843 =o 09770958 -2 069 @ 0022
MCDOWELL = 1 0 187464 © 007795322 2 405 0.0163
MECKLENB 1 0 112393 0: 10573351 1 063 0 2880
MITCHELL = 1 -0 517632 0: 13277840 -3 898 © o002
MONTGOME 1 -0.113535 0 10313784 72101 0 2732
MOORE 1 -0 312524 = -0.:04450906 =7 022 0 0001
NASH 1 0 120954 0 04914709 2 462 0 0140
NEWHANOV 1 -0 136790 0 03213951 -4 256 © 0001
NORTHAMP 1 0 015015 0: 11177683 0 134 0 9932
‘ONSLOW 1 -0 389832 0 :03596810 10 938 0.0002
ORANGE 1 0 099748 =o 03080438 3 238 0 0012
PAMLICO 1 -0.330769 0 14836699 2.229 0 0260
PASQUOTA 2 -0 437627 0.09256922 “4-728 0.0001
PENDER 1 0 006288 =o: 09391180 0 067 0 9466
PERQUIMA 1 -0 262982 0: 16281874 1 615 0 1066
PERSON 1 -0.568641 0 08659430 -6.567 9.0002
PITT 1 -0.356579 003671548 “9.712 0 0002
POLK 1 0 047791 0, 12529023 0 361 0 7030
RANDOLPH = 1. -0 139951 003361630 “4 163 0 0001
RICHMOND 1 -0 329412 0 06673992 -4 936 © 0003
ROBESON = 1. 0.060568 © 0 05355921 2131 0.2584
ROCKINGH = 1 +0 231901 003802996 -6 098 © 0001
ROWAN L +0 116841 0 02752108 -4 246 9.0002
RUTHERFO 1 -0 401741 005174368 -7- 764 0 0001
SAMPSON 1. -0 202130 0 :06675218 -3 028 0 0025
SCOTLAND 1 -0 211651 0 _:08267358 -2 560 0 0106
STANLY 1 -0 370270 0 05545844 -6 677 0 0002
STOKES 1 -0.713682 007218364 -9 987 0 0001
SURRY 2 0 065843 0 04617097 2 426 01541
SHAIN 1 -0 193602 0: 15209952 <1 273 0 2033
TRANSYLV 1 -0 252570 0: 09306142 -2 14a 0 o06e
TYRRELL = 1 -0 459749 0: 21397786 -2:149 0 0319
UNION 1 -0 279454 0 03139622 -8 901 0 0003
VANCE ry +0 090941 = -0:07521120 -1 209 0 2269
WAKE 1 0 058160 0 09661827 0 602 0 5473
WARREN 1 -0 351191 0: 12479687 2 814 0 0050


differences, since urban capacity expansion tends to be oriented toward road widenings while
tural expansion tends to be focused on new roadways and major upgrades that may attract new
development. Another potential source of “noise” in the data 1s the presence of non-local traffic,
which may be a high percentage of total traffic on some roadways, especially on rural interstates.
High levels of through-traffic may explain the relatively high short-term elasticities we are
obtaining for rural counties For example, new highways may attract through-users relatively
quickly, while this may be less important for upgrades to other primary roads These may take
longer to attract increased traffic, as new development may often play a more important role in
these cases. If so, the net effects would be washed out in the data we have used in this study,

which include the total travel from all state-manntained primary roads 1n each county.


Model: MODELL
Dependent Variable: LOGVMT

we eneTIMune oudUREs sans REGnowo iN = wainudT row uf
Analysis of Variance
Sum of Mean
Source DF Squares Square P Value Prob>P
Model. 43-392 60684 9 13039 1799 475 0.0001
Brror 388 1.96868 0 00507
¢ Total 431-394 $7552
Root MSE 0.07123 R-square 0.9950
Dep Mean 1476357 Adj R-sq 0 9945
cv. 0 48248
Parameter Estimates
Parameter Standard TT for HO
Variable DF Estimate Error Parameter=0 Prob > |T|
INTERCEP 1 6 091677 0 44669832 13 637 © 0002
LOGLM 2 0 331473 0 05370070 6 173 © 0002
LocPop 2 0.517561 0 03045897 16 992 © 9001
ANNEARUN 1 0 240031 —-0.:07097928 3 382 © 0008
ARLINGTO 1 0 008511 0.02543845 0 335 0 7381
BALTIMOR 1 0 298375 —-0.09057449 3 294 © 0011
CARROLL = 1 0.322518 = 0 03759505 -8 579 © 0001
CALVERT = 1 -0 424018 = -0:04864306 -8 717 © 0001
CHARLES 1 -0.263211 0: 04629584 -5 685 © 0001
FAIRFAX = 1 0 482998 =o: 05455135 8 asa 0.0002
FAUQUIER 1 -0 062849 0: 04957812 -1 268 0.2057
FREDERIC 1 9 091325 0 06726887 1358 0.1754
HARFORD 1 -0 299494 004541410 -6 595 0.0003
HOWARD 1 0.274655 0 04467076 6 148 © 0001
LOUDON 2 +0 316845 —-0.03430075 -9 237 © 0001
MONTGOME 1 0 268135 © 0 gas9ez82 2118 0 0020
PRINCEGE 1 0 343413 0 09297815 2693 0 0003
STAFFORD 1 0 267465 0 04467290 5 987 © 0002
YR1971 1 0.057373 0 02524995, 2 272 0 0236
YR197200 1 9.098283 0 02533836 3.879 © 0002
YR19730 1 0 149865 0 02546889 5 884 0.0001
YR974. 1 0 096227 © -0.02569202 3.745 © 0002
YR97S 1 0.126089 0 02588873 4 870 0 0001
yR9760 1 0 187814 © 0: oz608456 7 200 0 0001
yR.1977, 1 0 225404 © -0.:02631266 8 566 © 0002
yr1978@ 1 0.259228 0: 02662892 9735 0.0002
YR_1979 1 0 218323 002693220 8 106 0 0002
yR1980 1 0.230214 0 :02724825, a 449 0 0002
yR1981 2 0.243661 0 02752056 8 854 9 0002
YR_1982 2. 0.240435 0 02777133 8 658 9 0001
YR_1983 1 0.313045 0 02801370 11175 9 0001
yR_19840 1. 0 347244 «=O :02836128 12 244 0 0001
yri985 1 0411246 0 :02873182 14 313 © 0001
yR1986 01 0 459197 0 :02935065 15 645 9 0001
YR1987 1. 0 504231 0 :03011369 16 744 © 0002
yr 1988 1 0 537744 = :03064527 17 547 0 0001
yR_1989 1 0.561535 0 :03117281 18 014 © 0001
yR_1990 1 0.582731  0.03169154 18.388 0 0001
¥R_1991 1 0.609667 0 03226303 1a 897 9 0003
YR_19920 1 0 607976 0, 03297155 19 439 © 0002
YR19930 01 0 618756 —-0.03335504 18 552 0 0002
YR_19940 1 0 640312 © 0-:03381607 18 935 0 0003
YR1995 1 0641755 003440016 18 656 0 0001
YR1996 1 0 655364 © -0.:03486786 18 810 0 0001


APPENDIX B: CORRELATION MATRICES

This appendix contains four sets of correlation matrices for each study area: one for logs and one
for non-log variables, for each of the basic variables and the first-difference forms of the

variables The following pages contain 24 tables, 4 for each of the five study areas.


Variable DF

INTERCEP
LOGLM
LOGPOP
LOGPCI98
YR_1972
YR_1972
¥R_1973
YR_1g974
YR_1975,
¥R_1976
¥R_1977
YR_1978
¥R_1979
YR_1980
YR_1981
Y¥R_1962
YR_1983
YR_1984
YR_1985
YR_1986
¥R_1987
¥R_1988
Y¥R_1989
¥R_1990
YR_1992
YR_1992
Y¥R_1993
YR_1994
¥R_1995
¥R_1996

PEP PEE EE

VIRGINIA COUNTIES LOG REGRESSION

Parameter Estimates

Parameter
Estimate

3 891537
0 508289
0 504424
0 110210
0 019124
0 066768
© 102396
9 059923
0 089929
0 127535
0.158800
0 191140
0 190708
9 165910
0.171537
9 192914
0 220066
0 267600
9 311250
0 364516
0 409649
0 456034
0 473599
0 478434
0 465978
0 si2368
0 548312
0 559764
0 573436
0 594937
© 115310

-0 112438

-0 067585

-0 090437

-0 126523
0 338038
a 268546

-0 624279

-0 171587
0.167863
9 376176
0 004312
-0 213942

-0 232868

-0 006277
0 629912

-0 105564

-0 353603

+0 373313
0 139671
0 056445

-0 959358

+0 128283

200000006 00000008080000900000000900000099000000000000

Standard
Error

39612725
03264000
01973690
03395794
91503536
01543734
02610389
01608257
01598002
01655478
01695418
01784163
01789657
01771228
01782490
01773716
01843815
01980627
02028469
02127928
02174269
02231145
02283837
02257693
02212010
02249266
02275140
02330856
02364486
0238s268
03568527
03286005
04542928
02939025
03944806
05293533
03873777
04754528
03006656
04225493
02995054
03205662
03242685
03508128
02985161
03152295
02878537
0sse7693
03383826
04435197
04202843
05294156
03175460

T for HO-
Parameter=0

9 824
18 573
25.557

3245
1 272
4325
6 358
3726
5 628
7 704
9 189
10 713
10 656
9 367
9.623
10 876
11 935
13 511
15 344
17 130
18 B41
20 439
20 737
21 491
21 066
22,779
24 100
24 015
24.252
24 942
3248
-3 422
+1 488
-3 082
+3 207
6 386
7514

-13 130
+5 707
3973
12 560
0 134
-6 598
-6.638
+0 210
19 983
-3 667
~6 328

-21,.032
3149

1 343

-18 121

-4 040

Prob > |T!

0 0001
9 0001
9 9002
9 0012
0 2035
90 0001
0 0003
0 0002
0 0001
© 0001
0.0001
0 0001
0.0001
0 ocor
0 0001
0 0003
0 0002
0 0002
© 0002
0 0001
0 0001
0 coor
0 0001
9 0901
0 0001
9 0001
0 0002
0 9002
0 0001
0 coon
0 0012
0 0006
0 1370
0 0021
0 0024
0.0002
0 0001
0 001
0 0001
0 0001
0 0002
0 8930
0 0002
0 0001
0 6335
© 0003
0 0003
0 coo.
0 0001
0 0017
0 1794
0 0001
0 0001


VIRGINIA LOG FIRST DIFFERENCES CORRELATION MATRIX

[VARIABLE |DESCRIP__| L_VMIDFF | L_VCPDFF | L_LMDIFF | L_LMCDFF | L_POPDFF | L_PCIDFF L_JOBDFF
IL_VMTDFF = {Coefficient 1 091514) 007767 -0.03247| 0 12938 0 23081 01149
Std Error 0) 0.0001 0.0001 0 1048] 00001 00001 0000!
N 2496) 2496) 2496) 2496} 2496) 2496) 2496)
IL_VCPDFF =| Coefficient 091514 ] 0.04393 0 23283) -0 28135 0 25566) 004911
Std Error 00001 0 0.0282 00001 00001 0.0001 00141
N 2496 2496 2496) 2496) 2496, 2496) 2496
IL_LMDIFF Coefficient 007767 0.04393 | 0 70778: 0 07682 0 02578 0.00931
Std Error 0.0001 0.0282] 0 00001 00001 0 1979] 06419
N 2496) 2496 2496 2496) 2496) 2496) 2496)
L_LMCDFF [Coefficient -0 03247 0 23283 0 70778) if -0 64998 | 0.07594 -01011
Std Error 0 1048) 0.0001 00001 0} 0.0001 0.0001 00001
N 2496) 2496 2496 2496) 2496) 2496) 2496
L_POPDFF Coefficient 0 12938) -0 28135 0.07682 -0 64998} 1 -0.07944 015271
Std Error 0.0001 00001 00001 00001 0} 0.000! 00001
IN 2496 2496 2496) 2496) 2592, 2496) 2496,
L_PCIDFF Coefficient 0 23081 0 25566) 0.02578! 0.07594 -0 07944 1 0 2646)
Std Error 00001 0.0001 01979 00001 0.0001 0} 0.0001
N 2496 2496 2496 2496) 2496) 2496) 2496
L_JOBDFF Coefficient 01149 004911 0.00931 -0 1011 015271 0 2646) !
Std Error 0.0001 00141 06419 00001 0.0001 0.0001 0}
N 2496 2496) 2496) 2496 2496 2496) 2496



for example, Johnston, 1984) using the assumed short-long run relationship underlying the

distributed lag function

Table 3-3 presents the results of these regressions, for the same variable specifications as the
base muns, except for the addition of the one-year lagged dependent vanable. The resulting

estimated short run and long run elasticities are presented at the bottom of each column

These runs show a high degree of significance overall, and for lane miles and population in every
study area. The short and long run elasticity calculation, shown at the bottom of the page,
indicates that there 1s a significant increase in the elasticity from short to long run in every areas,
although the difference is smallest in North Carolina, and largest in Virginia. The Virginia long
run elasticity 1s the highest elasticity found in any specification of EEA’s analysis. Maryland
shows relatively low short-run and long run elasticities than North Carolina. The DC-Baltimore
results are similar to Virginia in the short run, but closer to North Carolina and Maryland in the
Jong run. Not surprisingly, the “all states” regression results are intermediate between those of

the individual states included 1n these runs.

While the runs for all the study areas show significance for lane miles, the variation across the
areas 1n terms of short run and Jong run elasticities 1s somewhat difficult to interpret. It probably
reflects variations in any number of factors that could not be captured in this analysis, such as the
detaiied Jand use patterns in each area, types and locations of lane-mile additions, levels of
congestion on the corridors with lane miles added, etc Analysis of more detailed VMT and lane
mile data for these states (which may be available for some areas, but not from the state offices

that EEA worked with to obtain data), may provide further insights 1n this area.

3.3. TRAFFIC VOLUME AND POPULATION DENSITY MODEL RUN RESULTS

In theory, one would expect induced travel effects to be larger 1n an area with more congestion.
Any increase in existing road capacity would give a more than proportional decrease in travel

umes (in the short-run). On the other hand, congested areas also tend to be fully developed, so

3-8


ALL COUNTIES LOG REGRESSION (INCLUDING WASHINGTON DC}

Variable oP

‘VASMYTH
VASOUTHA
VASPOTSY
VASTAFFO
‘VASURRY
VASUSSEX
VATAZEWE
VAWARREN
VAWASKIN
VANESTMO
VAWISE
VARYTHE:
VAYORK
VASUFFOL
WASHDC

eer te

Parameter
Estimate

0.147100
0.208424
0 530249
0.699589

-0.063884
0.763233
-0,123650
0.088696
0.247597

-0 685391

0.152944
2.107627
© 631247
0.319489

-0,.030603

Standard
Error

0,05416002
0.07135388
0.04840395
81894
12223878
08655005,
04173824
07609128
4068113
7156831.
04705370
07595306
0.06024537
0.04015910
0.20089228

e@oeesc900e

T for HD:
Parameter=0

2.716
2 921
10.955
14.330
0,523
8.818
-2.963
1.166
6.086
-9.577
3,250
14 583
10.478
7.956

Prob > {T|

0.0067
0.0035
09,0002
9.0001
0.6013
0.0001
0.0032
0,2839
0.0001
0.0001
0.0012
0 9001
0.0002
0 a001
0, 7612


Although it 1s certainly possible to develop more complex assumptions regarding the residual
variations (e g., by using a “random effects” model), the fixed effects approach is generally
considered reasonable in cases where the data encompass the population in question (which is
mostly true here, since in most counties all daily travel on the types of roads included in the
analysis 1s reflected in the data). For a more detailed discussion of the fixed effects specification

see for example Kennedy (1992).

A logarithmic specification of the fixed effects model can be shown as
log(VMT,) =a, + B, + A‘ log(X!) +e,
kh

where:

VMT,, is the daily vehicle miles of travel for county 1 in year t;

Q, is the fixed effect for county i, estimated in the analysis;

B 1s the fixed effect for year t, estimated in the analysis;

X‘ 1s the value of explanatory variable k for county 1 and year t;

at is each of the set of K coefficients to be estimated;

& 1s the outcome of a random variable for county 1 1n year t, assumed to be

7

normally distributed with mean 0

This model, and variants of this mode] that introduce a lag structure, have been used throughout

this analysis.

13 DATA DESCRIPTION

As mentioned, most of the database used 1n this analysis was described 1n detail in a previous
EEA document (EEA 1998). A brief summary of the data is reproduced here for the readers’

convenience.


WASHINGTON DC - BALTIMORE EXPANDED METROPOLITAN AREA
LINEAR FIRST DIFFERENCES CORRELATION MATRIX

VARIABLE | DESCRIP_| VMTDIFF | VCAPDIFF | LMDIFF | LCAPDIFF | POPDIFF PCIDIFF JOBDIFF
VMTDIFF [Coefficient ! 050541 029599) 0 18305 0 44819 0 22042 0 35381
Std Error 0 0.0001 00001 0 0002 0.000! 00001 00001
N 416 416] 416] 416 416 416 416
VCAPDIFF {Coefficient 050541 [ 0 11658) 0.07138 -0 03943 0 28769 0.09095
Std Error 0 0001 tt) 00174 0 1462] 04225 00001 0 0638)
N 416 416 416 416 416 416 416
/LMDIFF Coefficient 0 29599 0 11658 1 056977 0 17572) -0.01018 0.07875
Std Error 0 0001 00174 0 00001 0.0003 0 836 0 1087)
N 416 416 416 416 416 416 416}
LCAPDIFF Coefficient 0 18305 007138 056977 1 00861 -0 0083 0 14021
Std Error 0 0002 0 1462) 0.0001 0 00794 0 866 0.0042,
IN 416 416 416) 416 416 416 416)
POPDIFF Coefficient 044819 -0 03943 0 17572 00861 I 0.09002 0 24559}
Std Error 0 0001 04225) 0.0003; 00794 0 0 0666) 00001
N 416) 416 416) 416 416 416 416
PCIDIFF Coefficient 0 22042 0 28769 -0.01018) -0 0083) 0.09002 i 0.29592
Std Error 0.0001 00001 0 836 0 866 0 0666) 0 0.0001
IN 416 416 416) 416) 416 416 416)
JOBDIFF Coefficient 0 35381 0 09095 0.07875) 0 14021 0 24559 0 29592 1
Std Error 00001 0.0638 0 1087 0 0042: 0.0001 00001 0
N 416 416 416) 416 416 416 416)



ALL COUNTIES LOG REGRESSION (INCLUDING WASHINGTON DC)
Model MODELL
Dependent Variable LOGVMT

Analysis of Variance

Sum of Mean

Source DF Squares Square P value Prob>F
Model 231 (2476 .08137 10 71897 1774 190 © 0001
Error 2188 132905 © 00604
¢ Total 2419 2489.30041

Root MSE © 07773 R-square 0 9947

Dep Mean 13 45850 Ady R-sq 0 9941

cv 0 57754


that roadway capacity expansion provides, on net, costs or benefits to society This analysis

focuses on the first of these questions

While this study does not directly address the second issue, it should be noted that the size and
nature of the effect has important implications for whether capacity expansion provides net
benefits to society. A large rebound effect, for example with an elasticity near -1, indicates that
nearly all the congestion benefits of highway expansion are lost to increased traffic volume (over
whatever tume period the elasucity applies). On the other hand, it also suggests that there must
have been considerable “pent up” travel demand that was released when the cost of driving was
lowered Conversely, a small rebound effect, for example with an elasticity near -O 1, would
indicate that most congestion benefits from capacity expansion are retained, and also that there 1s
not significant latent travel demand going unfilled. The relative importance of these two effects
(lost congestion reduction benefits v. additional travel benefits) are at the crux of the debate
regarding whether capacity expansion is still worthwhile in the face of an established presence of
induced demand. Of course, from an environmental point of view, a failure of increased capacity
to reduce congestion suggests that capacity expansion 1s likely to be a poor approach to reducing
vehicle highway emissions. Highway development also may have important 1mpacts on how and

where communities are developed, which too may have a variety of impacts on the environment.

It should also be noted that any findings of a significant presence of a travel “rebound” effect to
capacity additions, or the existence of “induced demand”, from this study should not be
interpreted to mean that all roadway capacity expansion projects will suffer from this effect.
Given the aggregate nature of the data used in this analysis, 1t would be inappropriate to draw
conclusions about induced travel effects on any specific proyect. The conclusions from this
analysis are relevant primarily at a general policy level — in general, does capacity expansion
trigger a significant increase in travel levels? For any specific proyect, more detailed analysis of

existing travel conditions and other factors would be required.

14



Model: MODEL
Dependent Variable. LOGVMT

Source

Model
Brror
¢ Total

Root MSE
Dep Mean
cv.

seneeNIA cues TES 2 .GRE---~.

Analysis of Variance

‘Sum of Mean
DF Squares Square
124 2163 73405 17 44947
2467 26 59305 0 01078
2891 2190 32710
0 10382 © R-square
12 95902 Ady R-sq

0 80117

F Value

1618.763

0 9879
0 9872

Prob>P

0.0002


TABLE C-2 WASHINGTON DC DATABASE PRINTOUT

TRAVEL DATA

YEAR LM LM_UC *_| LM_AD] ** VMT VMT_UC * | VMT_ADJ **
1995 3,376 2,306 1,070 9,493,151 2,098,630 7,394,521
1994 2,708 1,638 1,070 9,446,575 1,945,205 7,501,370
1993 2,435 1,365, 1,070 9,547,945 1,972,603 7,575,342
1992 2,714 1,644 1,070 9,758,904 1,917,808 7,841,096
199) 2,703 1,651 1,052 9,397,260 1,857,534 7,539,726
1990 2,695 1,643 1,052 9,334,247 1,854,795 7,479,452
1989 1,371 331 1,040 9,353,425 1,854,795 7,498,630
1988 1,349 332 1,017 9,328,767 1,841,096 7,487,671
1987 1,334 326 1,008 9,227,397 1,764,384 7,463,014
1986 1,350 338 1,012 9,005,479 1,704,110 7,301,370
198S 1,370 356 1,014 8,830,137 1,742,466 7,087,671
1984 1,389 359 1,030 8,805,479 1,816,438 6,989,041

* Urban Collector Lane Miles / VMT
** Adjusted Lane Miles / VMT - do not include urban collector data for reasons discussed in text

DEMOGRAPHIC DATA

YEAR POP POPDEN GAREA PCI LABOR EMPLOY _|UNEMPLOY JOBS GASPRICE
1995 554,256 9,027 6140 31,176 284,142 258,833 25,309 733,232 1101
1994 567,013 9,235 6140 30,348 299,530 275,034 24,496 747,791 1 085
1993 578,127 9,416 6140 29,697 307,378 280,873 26,505 767,792 1173
1992 585,794 9,541 6140 29,227 310,124 283,586 26,538 768,148 1217
1991 594,301 9,679 6140 27,792 314,607 289,945 24,661 774,905 1303
1999 603,618 9,831 61.40 27,380 329,162 307,369 21,793 786,573 (425
1989 624,168 10,166 6140 26,051 315,000 299,000 16,000 777,829 1324
1988 630,432 10,268 6140 25,259 332,000 315,000 16,000 769,851 1279
1987 636,930 10,373 61 40 23,558 330,000 310,000 21,000 746,085 1258
1986 638,269 10,395 6140 22,821 323,000 298,000 25,000 732,726 1259
1985 634,549 10,335 6140 22,293 324,000 296,000 27,000 713,254 1621
1984 633,382 10,316 6140 21,827 321,000 292,000 29,000 698,919 1692



Model. MODEL
Dependent Variable LOGVMT

Source DF
Model 123
Error 2468
¢ Total 2591

Root MSE

Dep Mean

cv

NIA

Analysis of Variance
Sum of Mean
Squares Square
2163.62051 17 $9041
26.70659 0 01082
2190.32710
0 10402 R-square
12 95902 Adj R-sq

© 80272

P Value

1625 559

o 9876
0.9872

Prob>F

0.0001


ALL COUNTIES LOG REGRESSION (INCLUDING WASHINGTON DC)

Variable DP

‘NCPERSON
NCPITT
NCPOLK
NCRANDOL,
NCRICHMO
NCROBESO
NCROCKIN
NCROWAN
NCRUTHER
NCSAMPSO
NCSCOTLA
NCSTANLY
NCSTOKES
NCSURRY
NCSWAIN
NCTRANSY
NCTYRREL
NCUNION
NCVANCE
NCWAKE
NCWARREN
NCWASHIN
NCWATAUG
RCHAYNE,
NCHILKES
NCHILSON
NCYADKIN
NCYANCEY
VAACCOMA
VAALBEMA
VAALLEGH
VAAMELIA
VAAMHERS
‘\VAAPPOMA
VAARLING
‘VAAUGUST
VABATH
VABEDFOR
VABLAND
VABOTETO
VABRUNSW
VABUCHAN
VABUCKIN
VACAMPBE,
VACAROLI
VACARROL
VACHARLE
VACHARLO
VACHESTE
VACLARKE
VACRAIG
VACULPEP
VACUMBER
VADICKEN
VADINWID

PEE EP

Parameter
Estimate

-0 417184
-0 318998
© 184485
-0 100299
-0 185860
0 103428
-0 182838
+0 025091
0.272404
-0 162693
+0 042436
-0 244083
+0 598093
0 128987
0 028754
-0 118993
-0.212297
-0 207169
© 080923
0.017668
-0 168658
© 003722
© 063313
-0 235597
-0 117586
0 059813
0 257683
-0 294530
0 218342
0 254569
0 239382
0 274413
0 093417
0 179733
0 435563
0 390339
-0 394813
+0 047206
0 730709
0 640375,
0 274489
-0 098249
-0 070269
0 107830
0 944188
0 333075
0 198466
-0 135041
0 203697
0 330815
+0 554362
0 112270
-9 191305
-0 211932
0 316791

Standard
Error

0 06124401
© 03640512
0 08620617
© 03615596
0 04808220
0 04052261
0 03351468
0 04131463
0 04253575
0 03785999
9 0591664.
0 04600507
0 04891720
0 03515493
0 09924732
0 06261861
0 13893620
0 03621851
0 05899134
0.08333497
0 07830952
0 09080761
0 osocag71
0 03676361
0 04029137
0 03435756
0 05961124
0 08354884
0 05306698
0 03656995
0 07725768
0.10813002
0 06121086
0 09425459
9.08000529
0 03520892
0 11875265
0 04059481
0 10700127
0 05474126
0 07345360
0.06584599
0 08635598
0 04327048
0 06308089
0 05713112
0 12467650
9 08627205
0 05921127
0 09230720
0 12881927
© 06656154
0 11909077
0 08206962
0.05952769

T for HO
Parameter#0

+6 811
-8 762
2140
+2774
-3 865
2 552
+5 455
-0 607
+6404
+4 297
-0.717
=5 306
+12 227
3 669
0 293
+1 900
-1 $28
-5 720
1 372
0.212
+2154
0 042
1 054
-6 408
+2918
1 741
4 323
-3 525
4.114
6 961
3 098
2.538
1.526
1 907
5 444
11.086
-3 325
+1 163
6.829
11 698
3737
+0733
-0 814
2 492
13-383
5 830
1 592
+1 602
3 440
3 584
-4 303
1 686
+1 677
-2 S81
5 322

Prob > |T|

0 0002
0 0001
0 0325
0 0056
© 0002
0 o108
0 0001
0 5437
0 0002
0.0001
0 4733
0 002
0 0002
0 0002
0 7698
0 0575
0 1267
© 0001
© 1703
© 8321
0.0314
0 9673
0 2918
© 0002
0 0036
0 0818
0 0001
0.0004
0 9002
0 9002
0 0020
@ 0112
0 1271
0 0567
0.0001
0 0002
0 9009
0 2450
a 001
0 a001
0 0002
0 4638
0 4159
0 0128
0 0001
0 0001
0 1126
9 1092
© 0006
9 0003
0 9002
0 0919
0 0937
0 0099
0 001


ALL STATES LOG CORRELATION MATRIX

VARIABLE [DESCRIP LOGVMT | LOGVCAP | LOGLM_|LOGLMCAP|LOGVMTLM|LOGLMURB] LOGPOP_[LOGPOPDN| LOGPCI98 | LOGJOBS | LOGGAS98
LOGVMT Coefficient 1 0 03744) 0 86461 -0 70735 0 85887 -0 67331 0.92595 08150! 059347 0.78446) 0.00302
Std Error t) 00656) 0.0001 00001 00001 00001 00001 0.0001 0.0001 00001 0 882|
iN 2420) 2420] 2420) 2420) 2420) 2420 2420) 2420 2420) 2420) 2420)
ILOGVCAP Coefficient 0 03744] 1 -0 00877, 053135) 0.07409 0 34676 -0 34272) -0 34428 0 10286 -0 23615 -0 06004,
Std Esror 00656 it) 0 6662 00001 0.0003) 00001 0.000! 0.0001 0 0001 00001 00031
IN 2420! 2420) 2420] 2420) 2420! 2420 2420) 2420 2420) 2420) 2420)
ILOGLM Coefficient 0 86461 -0 00877 1 -041741 0 48523 -0 47708) 081613] 0 57964 0 36472 071394 0.04508
Std Error 00001 06662) 0 00001 00001 00001 0000! 0.0001 0.0001 00001 00266]
IN 2420) 2420 2420) 2420) 2420 2420 2420) 2420) 2420) 2420] 2420)
ILOGLMCAP {Coefficient -0 70735 053135 -0.41741 1 -0 80546 078831 -0 86578) -0 9075 -0 50037| -0 68178| -000IT
Std Error 0.000! 00001 00001 0} 00001 00001 0.0001 0.0001 0.0001 0.0001 09567
IN 2420 2420) 2420 2420) 2420) 2420) 2420) 2420! 2420) 2420) 2420
LOGYMTLM |Coefficient 0 85887 0.07409} 0 48523 -0 80546 1 -0 6854 0 77941 082744 0 66099) 0 63737, -00407
Std Error 0.0001 0.0003 0.0001 00001 iv) 00001 0.0001 0000! 0.0001 00001 00453
IN 2420) 2420 2420) 2420) 2420) 2420 2420) 2420) 2420) 2420} 2420)
LOGLMURB {Coefficient -0 67331 0 34676 -0.47708 078831 -0 6854 1 -0 76402 -0 80957 -0.49226 -0 68868; -001255)
Std Error 0.0001 00001 00001 0.0001 00001 Q| 0.0001 00001 0.0001 0.0001 05371
IN 2420) 2420 2420] 2420) 2420 2420! 2420) 2420! 2420) 2420! 2420)
ILOGPOP Coefficrent 092595 -0 34272 081613 -0 86578 077941 -0 76402 ! 0 8963 051904! 0 82672} 0.02553;
Std Error 0.0001 00001 00001 0.0001 0.0001 0.0001 0} 0.0001 0.0001 0.0001 0 2093
IN 2420 2420 2420| 2420) 2420) 2420; 2420 2420) 2420) 2420) 2420}
ILOGPOPDN = {Coefficient 081501 -0 34428 0 57964 -0 9075| 0 82744 -0 80957; 0.8963, I 0 64643| 0 74785) 005157)
Std Error 00001 00001 00001 0.0001 0000! 0.0001 0.0001 iy 0.0001 00001 00112
IN 2420 2420 2420) 2420] 2420) 2420 2420 2420) 2420; 2420] 2420)
ILOGPCI98 Coefficient 059347| © 10286 0 36472 -0 50037 0 66099] -0 49226] 0.51904] 0 64643 ! 0.49796] -0. 02778
Std Error 0.000t 0000! 00001 0.0001 00001 00001 0.0001 0.0001 0 0 0001 01719
IN 2420) 2420! 2420! 2420 2420) 2420! 2420) 2420 2420 2420] 2420
LOGJOBS Coefficient 078446] -0 23615 071394 -0 68178 063737 -0 68868} 0 82672 074785 0 49796) 1 001814
Std Error 0.0001 0.0001 0000! 0.0001 0.0001 0.0001 0.0001 0.0001 0 0001 0} 03725
IN 2420 2420) 2420, 2420) 2420) 2420 2420 2420) 2420) 2420 2420)
ILOGGAS98 [Coefficient 0.00302} -0 06004 0.04508) -00011 -0.0407] -0.01255 002553) 005157 -0 02778) 001814] 1
Std Error 0 882| 00031 00266 0.9567] 0.0453) 05371 0 2093 00112 01719] 03725 0
IN 2420] 2420) 2420 2420 2420) 2420) 2420) 2420) 2420} 2420} 2420)



LOW VOLUME RUNS

TABLE A-7
SUMMARY OF TRAFFIC VOLUME RUNS, ALTERNATIVE METHOD

[Dependent Vanable LOG(VMT)
State All States Maryland North Carolina ‘Virginia DC - Balumore
Years of Data 1985-1995 1969-1996 1985-1997 1970-1996 1970-1996
IN 880 880 84 84 520 480 999 999 x x
Adjusted R-Square: 0980 0980 } 0927 0926 | 09386 0986 0968 0968 x x
Intercept est | 407 | 319 [| 722 [ 709 [ 394 [| 221 346 | 439 x x

t [331 j 219 | 262 | 241 367 139 [| 433 | 489 x x
Log (Lane Mites) et_| 0475 | 0449 [ 0143 | 0147 | 0428 | 0296 | 0676 | 0679 x x

«| 461 [| 426 | 0805 | o803 | sor | 293 | 934 | 940 x x
|Log (Population) est 0 606 0619 0440 0415 0628 0660 | 0529 0540 x x

« [61s [ 627 124 too [607 [ sas [| 882 [ 900 x x
Log (Income Per Capita) | est x 0093 x 0036 x 0222 x 0114 x x
1998S) 1 x 114 x 0131 x 229 x 224 x x
MID VOLUME RUNS
[Dependent Vanable LOG(VMT)
State All States Maryland North Carolina Virginia DC - Balumore
Years of Data 1985-1995 1969-1996 1985-1997 1970-1996 1970-1995
IN 1320 [ 1320 [420 420 720 660 (350 | 1350 [189 189
[Adjusted R-Square 0990 | 0990 | o9a5 | 098s [| 0993 | 0994 | o9a2 | 0983 | 0983 | o984
intercept est | 524 136 [0092 | -o771 [ soo | 696 | Sas 198 136 | -379

| 828 ta2_ [0136 | 0797 [ 631 506 | 137 | 409 | 142 [ -204
[Log (Lane Mites) est_[ 0707 | 0662 | 0798 | o8is | 0542 | 0558 | 0523 | osi2 | 0434 | 0375

t 103 [| 967 [ ses | 877 | 879 | 84a [| 130 | 132 | soo | 483
[Log (Population) et [0387 | 0468 [ 0764 | o7ae [ 0512 [ 0464 [ 0439 [ 0423 | 0875 [ 0880

t_[ 824 | 960 [ 209 | 18s [ 743 | 575 | 196 | 194 | 956 | 990
Log (Income Per Capita) | est x 0334 x 0 104 x -0 153 x 0399 x 0553
(1998) t x 538 x 125 x =191 x 896 x 320
HIGH VOLUME RUNS
[Dependent Vanable LOG(VMT)
‘State All States Maryland Nonth Carolina Virginia DC - Baltimore
Years of Dara 1985-1995 1969-1996 1985-1997 1970-1996 1970-1996
IN 220 220 140 140 65, 60 243 243 243 243
[Adwusted R-Square o994 | 0995 | o9s4 | 0995 | 0993 | o995 | 0993 | 0983 | 0996 | 0996
intercept est_| 295 | -0732 [ 542 | 958 | 528 391 [0966 | 134 [ 821 948

1 | 296 | -0343 [ 723 | 840 | 405 315 73 | 712 168 106
[Log (Lane Miles) es | 0568 | 0560 | 0435 | 0480 | o«o1 | 0535 | 0210 | o1g4 | 0241 | 0264

« [331 323 [417 | 500 | 687 | 657 | 277 [| 241 315 321
Log (Population) est [ 0683 | 0740 [ 0532 | 0638 | 0461 | 0587 | 0220 | 0187 | o381 | 0392

t | 378 | 898 181 179 [| 432 [ saa [ 383 [| 318 140 140
Log (Income Per Capnta) | est x 0296 x -0603 x 0474 x 0342 x 0148
19988) t x 198 x “457 x 168 x -209 x “170



9%

TABLE 2-3
DATA DISTRIBUTIONS (NUMBER OF COUNTIES IN EACH CATEGORY IN 1995)

Range Maryland awn Virginia DC Balt All
# of Counties 23 100 96 16 220
Persons per Square Mile
Low < 100 7 53 74 1 134
High > 100 16 47 22 15 86
Population per Lane Mile
|Low < 100 6 30 44 0 80
Med 100 - 200 9 42 38 3 89
High > 200 8 28 14 13 51
IVMT per Lane Mile
[Low < 2000 2 33 24 ie) 59
Med 2000 - 5000 16 55 56 6 127
High > 5000 5 12 16 10 34
Income / Capita
[Low < 20000 5 58 49 0 112
High > 20000 18 42 47 16 108



TABLE A-4a
ELASTICITY RUNS (SHORT / LONG RUN) - MARYLAND

pendent Vanable LOG(VMT/CAP)
621 621 621 621 621
Adjusted R-Square 0964 0 964 0964 0964 0965
INTERCEPT est 191 137 142 105 185
I; t 105 332 253 166 268
Log (VMT/CAP) est 0556 0 552 0556 0 553
lagged 1 year t 159 158 139 158
Log (LM_CAP) est 0159 0.225 0166 0 226
t 620 428 617 429
Log (POP) est x 0079 x 0072
t x 145 x 131
Log (PCI_98) est x x 0055 0 042
t x
[Log JOBS) est x
t x
[Short-Run Elasticity 0.159
[Long-Run Elasticit; 0.357
= Vanable LOG(VMT)
621 621 621 621 621 621 621
|Adyusted R-Square 0996 0997 0996 0996 0997 0996 0996
INTERCEPT est 230 148 0916 128 197 251 268
t 545 362 139 204 287 620 392
(VMT) est 0768 0559 0747 0559 0537 0593 0592
lagged |_year t 291 160 273 160 149 172 71
(LM) est 0 143 0215 0153 0215 0 187 0125 0123
L 260 409 280 410 348 237 233
(POP) est x 0 288 x 0 284 0214 x x
t x 855 x 805 469 x x
(PCI_98) est x x 0 168 0.026 -0023 x -0020
t x x 273 0428 -0 352 x -0 307
[Log JOBS) est x x x x 0101 0222 0227
x x x x 240 145 689
0.215 0.153 0.215 0.187 0.125 0.123
0.488 0.606 0.489 0.403 0.307 0.302



TABLE A-5b
TRAFFIC VOLUME RUNS (LOW / MID / HIGH VOLUME ) - NORTH CAROLINA.

ALL ‘LOW VOLUME, MID VOLUME HIGH VOLUME,

IDependere Variable LOGVMTCAP} LOS(VMTICAPD LOG(VMTICAP) LOG(VMTICAPL
IN 1200 1300 1200 300) 300 475 701 701 a0 Ec 9 3 ‘Trafic Volume Classifications
lAdyusied 2-Squere o9s7 [ove | oser | oso | osx | o9sa | oom | ose | oom | 9355 | 0530 | 0982
INTERCEPT et | 260 317 445 252 407 208 261 444 796 255 596 729 Low Volume. VMT/LM < 2000

t 128 n9 643 120 150 226 14a 36 B34 316 125 293 Mid Volume ~ — 2000¢ VMT/LM < 5000
Log (LM_CAP) | est x 0459 | 0426 x o368 | 0258 x oxo | osm x 0617 | _0581 High Volume- VMT/LM > S000

t x 9 949 x 397 348 x 27 964 x 720 657
Log (POP) est x x x x x x x x x x x x

t x x x x x x x x x x x x
‘Log (PCI_98) est x x 0.055 x x 0168 x x 70218 x x 0129

t x 5 0929 x x 22 x x 254 x x 0.497

ALL LOW VOLUME ‘MID VOLUME HIGH VOLUME
‘Dependent Variable LOG(VMT) LOG(VMT) LocvéMT) LoG(vMT)
1300 T1300 1300 1207 509 300 300 475 701 701 701 640 9 | «(8S 99 5

|Adwusted R-Square os04 | o99s | 0995 | a95 | 0990 | 0991 | 0992 | 0992 | 0991 | 0992 | 0993 | 0993 [| o992 | 0995 | 0996 | 0997
|INTERCEPT est 142 108 485 4am 122 102 254 229 162 105 629 10 140 928 415 S27
1 1 ort 361 730 au 558 27 216 35 732 289 TO4 609 265 130 310 191
[og Lm est x ose7_| 0475 | 0435 x 0389 | 0439 | 0247 x 0612 | 0523 | 0532 x o799 | o716 | 0684
| t x m3 979 802 x 469 554 259 x 100 839 820 x 664 655 628
Log (POP) est x x 0560 [0585 x x 0763 | 0725 x x 0412 | 0309 x x 0490 | 0549

t x x 107 939 x x 639 397 x x S16 321 x x 435 457
Log (PCI_98) est x x x 0.057 x x x 0169 x x x 0268 x x x 0164
L ni x x x 0958 x x x 212 x x x 293 x x x 0635,



TABLE C-1

LISTING OF COUNTIES IN
DC-BALTIMORE EXTENDED AREA
ANALYSES
IMARYLAND VIRGINIA
[Anne Arundel Arlington
Baltumore Fairfax
Calvert Fauquier
Carroll Loudon
Charles Prince William
[Frederick Stafford
Harford
[Howard
Montgomery
Prince George's



2. PRELIMINARY DATA ANALYSIS

This section presents the results of a preliminary data analysis, intended to provide information
on the characteristics of the study areas as well as basic variable relationships, to serve as
background for the econometric analysis presented in Section 3. Detailed data analysis was
conducted as part of the previous study and 1s contained in EEA (1998). A short comparative
analysis of the study areas 1s provided here to set the context for the following multiple

regression analysis.

Basic characteristics of the five study areas (and all areas taken together) are shown 1n Table 2-1.
Note that averages in this data are taken across the counties without weighting for relative
population (which is appropriate when comparing these averages to subsequent regressions using
ordinary non-weighted least squares) Note also that data for Maryland and Virginia extend back
to 1969 and 1970, respectively, while data for North Carolina and the DC (and therefore the
DC/Baltumore area) extend back only to 1985 and 1984, respectively There are also differences
im the coverage of road types in the different states, as discussed above in Section 1. Thus some

caution must be exercised in comparing the averages across states.

Keeping in mind these caveats, several] potentially important differences can be seen across the
different study areas. While the average geographic area of counties in each study area is quite
simular, the average population (and therefore population density) varies considerably The
DC/Baltimore area has about 1,600 persons per square mile, Maryland has about 420 per square
mule, Virginia has slightly under 200 per square mile, and North Carolina has less than 150 per
square mule. The travel per capita 1s somewhat inversely correlated with population density, with
Virginia showing 30 percent to 40 percent more daily travel per capita (on interstates and state-
maintained primary roads) than North Carolina and Maryland, with the DC/Baltimore metro area

about ten percent below Maryland. This suggests that the more densely populated areas require


TABLE A-6c
TRAFFIC VOLUME / ELASTICITY RUNS - VIRGINIA

ALL LOW VOLUME MID VOLUME HIGH VOLUME
|Dependent Vanable LOG(VMT/CAP) LOG(VMT/CAP) LOG(VMTICAP) LOG(VMTICAP)
2496 2496 1272 1272 1040 1040 184 184 Traffic Volume Classifications
0984 G 984 0976 0976 0 987 0987 0992 0992 Low Volume - VMT/LM < 2000
est 102 0 806 185 197 147 0244 264 262 Mid Volume - 2000< VMT/LM < 5000
t 139 425 130 7:08 13 0642 658 224 High Volume - = VMT/LM > 5000
est 0817 0816 0739 0738 0730 0717 0 636 0637
t 732 730 4h ait 351 340 6 3
est O12 0113 0245 0 245 0159 0175 0297 0297
t 954 960 993 991 781 843 592 5.82
est x x x x x x x x
t x x x x x x x x
|Log (PCI_98) est x 0024 x -0013 x 0143 x 0.002
t x 123 x -0 506 x 344 x 0016
\Short-Run Elasticity 0.112 0.113 0.245 0.245 0.159 0.175. 0,297 0.297
(Long-Run Elasticity 0.609 0.611 0.939 0.936. 0.588 0.617 0.818 0.818
ALL LOW VOLUME MID VOLUME HIGH VOLUME
[Dependent Vanable LOG(VMT) LOG(VMT) LOG(VMT) LOG(VMT)
IN 2496, 2496 2496 1272 1272 1272 1040 1040 1040 184 184 184
[Adjusted R-Square 0996 0997 0997 0993 0993 0993 0992 0992 0992 0995 0995 0995
INTERCEPT est 089 049 012 190 15) 152 183 135 026 306 285 386
t 649 33) 053 756 402 350 676 470 064 462 406 290
LAG VMT- est 0.876 0841 0840 0769 0764 0764 0 806 0763 0747 0673 0 656 0 646
t 900 779 778 44 67 4320 4311 441 3759 36 25 ug 1099 10 64
[LM est 0.134 0 128 0129 0199 0 203 0 203 0125 0 125 0122 0.258 0 260 0 262
t 702 678 6 86 577 5 868 5865 3741 377 371 433 435 438
POP est x 0.086 0 086 x 0.042 0042 x 0 103 0.094 x 0041 0041
t x 7.03 699 x 136 136 x 466 425 x 090 092
IPCL_98 est x x 0041 x x 0.00! x x 0149 x x -0.093
t x x 2215 x x -0.040 x x 3.688 x x -089
\Short-Run Elasticity 0.134 0.128 0.129 0.199 0.203 0.203 9.125, 0.125 0.122 0.258 0.260 0.262
[Long-Run Elasticity 1.078 0.808 0.811 0.863 0.860 0.860 0.647. 0.528 0.483 0.790. 0.757 0.742



1. INTRODUCTION AND BACKGROUND

This report documents the analysis undertaken by EEA under subcontract to Sierra Research Inc.
for the work assignment (EPA Contract No. 68-C7-0051) WA 1-01 This analysis focuses on
estimating econometric models of “induced travel demand”, using data from the U.S. Mid-
Adanuc region, in a database that was developed by EEA mostly under a previous work
assignment (and under a different contract). Documentation of this database 1s provided in a
previous EEA report “Measuring Induced Demand and Emussions Impacts from Transportation

Facilities, Database Documentation and Preliminary Analysis”!

The principal objective of this analysts 1s to develop econometric models to empirically test the
relationship between vehicle travel and roadway capacity, while controlling for other potentially
important factors that affect travel. The basic hypothesis to be tested 1s that additions to roadway
capacity (measured here as increases in lane miles) have a significant positive impact on travel

(measured here as daily average vehicle miles of travel, or VMT).

The following section provides a discussion of the phenomenon known as induced travel
demand, and how this analysis address the questions surrounding the issue. This 1s followed by a
description of the methodology and database used in the analysis. Section 2 provides a basic
analysis of the data and a comparison of the study areas. Section 3 provides the principal results
and interpretation of the econometric analysis. Section 4 provides a brief summary of the
findings, and conclusions that emerge from the analysis. Several Appendices provide additional

results and other background information.

' Under Subcontract to Industrial Economics Incorporated, EPA Contract 68-D4-0102, WA 4-15,
September 1998


MARYLAND LOG FIRST DIFFERENCES CORRELATION MATRIX

VARIABLE __|DESCRIP_ | L_VMTDFF | L_VCPDFF | L_LMDIFF | L_LMCDFF | L_POPDFF | L_PCIDFF | L_JOBDFF
L_VMTDFF {Coefficient 1 09722! 0 19222 0 03934) 0 15981 01722 0 18458
Std Error 0 00001 00001 03277 00001 0.0001 00001
IN 644 621 644 621 621 621 621
IL_VCPDFF = {Coefficient 09722) 1 0 16248) 0 18343 -0 07579 017514 0.08841
Std Error 0.0001 0 0.0001 00001 00591 00001 0 0276)
IN 621 621 621 621 621 621 621
L_.LMDIFF Coefficient 0 19222 0 16248 ! 071713 012014 -0 04817) 0.07752
Std Error 0.0001 0.0001 0 0 0001 0.0027 0 2307) 00535
N 644 621 644 621 621 621 621
L_LMCDFF {Coefficient 0 03934 0 18343 071713 | -0 60573 -0 03504 -0 22801
Std Error 03277 0.0001 0.0001 0 00001 0 3833] 00001
IN 621 621 621 621 621 621 621
IL_POPDFF Coefficient 0 15981 -0 07579} 0 12014! -0 60573 | -0 00507] 041329
Std Error 00001 00591 0.0027) 0 0001 te) 0 8996) 0.0001
N 621 621 621 621 621 621 621
|L_PCIDFF Coefficient 0 1722! 0.17514! -0. 04817 -0 03504 -0 00507) 1 047135
‘Std Error 00001 00001 0 2307 0 3833) 0 8996] 0 00001
N 621 621 621 621 621 621 621
L_JOBDFF Coefficient 0 18458 0.0884! 007752 -0 22801 0.41329 0.47135) !
Std Error 0.0001 0.0276] 0 0535 0.0001 0000! 00001 0}
N 621 621 621 621 621 621 621



uncommon for first difference runs, which tend to draw out the stochastic component of the

change in variables from year to year.

The coefficient on lane miles varies from a low of 0 15 (DC-Balumore) to a maximum of 0 61
(North Carolina), a range that 1s slightly broader than, but not inconsistent with, the base run
results. The lane mile coefficients for Virginia are similar to those for DC-Balumore, and much
lower than for Maryland and North Carolina. These are the two areas for which population 1s
significant, which may explain the difference in Jane miles (and may indicate that growth in

travel 1s more population-driven in these areas than in the other states).

In general, the significant results for the first difference models, and the relatively similar
coefficients on Jane miles for these models as for the base models (taken on levels rather than
differences), suggests that the coefficient estimates are relatively robust. It also indicates that the

base regressions do not suffer from significant bias due to autocorrelation.

3.2. SHORT/LONG RUN ELASTICITY RUN RESULTS

In order to separate the short and long run elasticity between lane miles and VMT, several lag
adjustment models were tested. EEA chose to use a distributed lag model structure for these
tests. This functional form allows the simultaneous estimation of short run and long run
elasticities, with the restriction that the effects are assumed to be strongest in the first year and
then decline through ume. This would seem appropriate in areas where capacity expansion
reduces congestion, but it may not correctly capture the nature of the lag structure in areas where
roadway expansion triggers growth and development, which may be minor at first but increase in
intensity through time The approach also implicitly assumes that all variables in the model have
the same lag structure with respect to the dependent variable. Whule these restrictions may
introduce some bias into the estmation, the approach is appealing due to 1ts econometric
simplicity and ease of interpretation. The distributed lag models were specified by introducing a
one-year lagged dependent variable into the specification. The resulting coefficients of the

distributed lag specification can be converted to estimates of short and long run elasticities (see,

3-7


ALL STATES LOG FIRST DIFFERENCES CORRELATION MATRIX

VARIABLE | DESCRIP_| L_VMTDFF| L_VCPDFF | L_LMDIFF | L_LMCDFF| L_POPDFF | L_PCIDFF | L_JOBDFF
L_VMTDFF |Coefficient 1 0.97252) 0 14691 0 06234 0.09155 006131 0.08969
Std Error 0 06001 0.0001 0.0034 0.0001 0.004 00001
N 2200) 2200) 2200) 2200) 2200 2200 2200)
L_VCPDFF |Coefficrent 097252 1 0 13666) 0.19919} -0 14282) 0.09949 0.03271
Std Error 0.0001 0 0.000! 00001 00001 00001 01251
N 2200) 2200} 2200 2200) 2200; 2200; 2200}
L_LMDIFF |Coefficient 0 14691 0 13666 | 078549] 0.04002) 002918 0.03563!
Std Error 0.0001 0.0001 0 00001 0 0606) 01712) 00948
N 2200) 2200 2200) 2200) 2200 2200 2200;
L_LMCDFF [Coefficient 0.06234 0 19919) 0 78549! 1 -0 58695 0 12576 -0 12065)
Std Error 0.0034 00001 0.0001 0 00001 0.0001 00001
N 2200) 2200 2200} 2200) 2200) 2200 2200)
IL_POPDFF [Coefficient 0.09155 -0 14282) 0.04002) -0 58695 1 -0 16487 024141
Std Error 0.0001 00001 0 0606} 0.000! 0 00001 00001
IN 2200; 2200) 2200} 2200) 2200 2200} 2200
L_PCIDFF  |Coefficient 006131 0.09949) 0.02918 0 12576 -0 16487 1 0 28437
Std Error 0.004 00001 01712] 00001 00001 0 00001
N 2200) 2200} 2200) 2200) 2200 2200; 2200
L_JOBDFF |Coefficient 0.08969 0.03271 0.03563 -0 12065 024141 0 28437 1
Std Error 0 0001 01251 0.0948] 0 0001 0.0001 0.0001 0
N 2200 2200) 2200) 2200; 2200 2200 2200)



NORTH CAROLINA LINEAR CORRELATION MATRIX

VARIABLE [DESCRIP VMT VMT_CAP. LM LM_CAP VMT_LM_|LMCAPURB. POP POPDEN PCI_98 JOBS GAS_PR98
VMT |Coefficient 1 -0 04205 0 82042 -0 49} 22] 0 85676} 030401 0.95087 0 84075 0 66684 0.93827] -0 0668)
Std Error tt) 01297 00001 0.0001 00001 00001 00001 00001 00001 0 0001 0 0267
IN 1300) 1300} 1300] 1300) 1300} 1300 1300| 1300} 1200) 1200] 1100,
VMT_CAP \Coefficrent -0 04205 1 0 04346} 047094 -0 0385; -0 13099 -0 25199) -0 29147; 0069 -0 22555) -0 12966)
Std Error 0 1297] 9 01173 0.000! 0 1654 0 0001 0 0001 0.0001 00168 0.0001 0.0001
IN 1300} 1300) 1300) 1300) 1300} 1300] 1300) 1300) 1200) 1200 1100}
LM Coefficient 0 82042] 0 04346 1 -0 35139} 0 56253 0 36466 074809 055785 0 43639 0 68519) -002231
Std Error 00001 01173 ie) 0.0001 0.0001 0 0001 00001 0.0001 00001 0.0001 04597
IN 1300] 1300) 1300} 1300| 1300) 1300} 1300 1300, 1200] 1200 1100]
LM_CAP Coefficient -0 49122 0 47094, -0 35139 t -0 68488 -0.49925, -0 53478 -0 5833 -0 44481 -0 4736 0.00821
Std Error 0.0001 0.0001 0.0001 0 00001 0000! 0.0001 00001 0.0001 0.0001 07857
IN 1300} 1300) 1300} 1300) 1300| 1300| 1300| 1300} 1200] 1200] 1100)
VMT_LM Coefficient 0 85676] -0 0385 0 56253 -0 68488) 1 0 39326} 0 80864 0 85584) 0.75909) 0 79056) -0 10622
Std Error 00001 0 1654 0.0001 00001 0} 0 000! 0.0001 0 0001 00001 00001 0 0004|
IN 1300} 1300} 1300) 1300, 1300| 1300} 1300| 1300] 1200] 1200} 1100)
ILMCAPURB {Coefficient 0 3040) -0 13099) 0 36466, -0.49925, 0 39326) t 0.25137 0 26798 0 27606) 0 19083, -0014,
Std Error 00001 00001 00001 00001 00001 0 0 0001 0.0001 0.0001 0.0001 06429
IN 1300) 1300 1300) 1300} 1300) 1300 1300! 1300} 1200} 1200) 1100}
POP Coefficient 0 95087 -0 25199} 0 74809 -0 53478} 0 80864) 025137 ! 0.89703 061922 0 98302) -0.02081
Std Error 0.0001 00001 00001 0 0001 00001 0 0001 0 0.0001 0.0001 0.000! 0 4906
IN 1300} 1300} 1300] 1300) 1300} 1300 1300} 1300] 1200} 1200) 1100
POPDEN Coefficient 0 84075] -0 29147 055785 -0 5833 0.85584 0 26798 089703) 1 0 65562 0.89511 -002155
Std Error 0.0001 00001 0.0001 0.0001 0 0001 0 0001 0.0001 0} 0.0001 0.0001 04752
WASHINGTON IN 1300} 1300) 1300} 1300] 1300) 1300] 1300) 1300) 1200] 1200) 1100)
PCI_98 Coefficient 0 66684] 0 069 0 43639] -0 44481 0 75909 0 27606) 061922} 0 65562} 1 063481 -0 21025)
Std Error 0.0001 00168 0.0001 00001 00001 0 0001 00001 00001 0 00001 0.0001
IN 1200] 1200} 1200} 1200} 1200) 1200] 1200) 1200) 1200) 1200) 1100)
JOBS Coefficient 0 93827| -0 22555 068519 -0 4736) 079056 0 19083; 0.98302 089511 0 63481 1 -002772
Std Error 00001 0.000! 00001 00001 00001 00001 00001 00001 0.0001 0 0 3583
IN 1200} 1200) 1200) 1200} 1200] 1200] 1200] 1200] 1200] 1200} 1100
IGAS_PR98. Coefficient -0 0668) -0 12966 -002231 0.00821 -0 10622) -0014 -0.02081 -002155) -0 21025 -0 02772 1
Std Error 0.0267 0.0001 04597 0 7857 0 0004 0 6429 0 4906) 04752 0.0001 0 3583 0
IN 1100] 1100} 1100} 1100} 1100} 1100} 1100] 1100} 1100] 1100] 1100)



travel [LOGVMT], with the R coefficient for each of these variables with respect to VMT over
0.5. Further, multicollinearity between these variables is generally not a problem, with few cases
of coefficients among the basic variables above 0.5. The most severe case among the basic
variables 1s the combination of population and employment, which 1s over 0 8. Several
coefficients for respecifications of the basic variables are also above 0.5, such as VMT per lane
mule [LOGVMTLM] with income. Overall, multicollinearity does not appear to be a significant

problem in the analysis.

2-8


REFERENCES

EEA, 1998, “Measuring Induced Demand and Emissions Impacts from Transportation Facilities’
Database Documentation and Preliminary Analysis”, Energy and Environmental Analysis,
prepared for EPA, Office of Policy Planning and Evaluation, under subcontract to Industrial
Economics Incorporated.

Hansen, Mark, and Yuanlin Huang, 1997, “Road Supply and Traffic in California Urban Areas”,
Transportation Research - A. Volume 31, No. 3, pp. 205-218.

Johnston, J., 1984, Econometric Methods, McGraw Hill, New York
Noland, Robert B., 1999, "Relationships Between Highway Capacity and Induced Vehicle

Travel", paper presented at the 78th Annual Meeting of the Transportation Research Board,
Washington D.C., paper no. 991069

R-!


TABLE A-8

SUMMARY OF TRAFFIC VOLUME / ELASTICITY RUNS, ALTERNATIVE METHOD

LOW VOLUME
[Dependent Vanable LOG(VMT)
State All States Maryland North Carolina Virginia DC- Baltimore
Years of Data 1986-1995 1970-1996 1986-1997 1971-1996 1971-1996
IN 300__|__ 800 81 31 480_| 440 | 962_[ 962 x x
[Adjusted R-Square 0987 | 0987 | 0920 | 0918 | 0993 | 0993 | 0989 | 0989 x x
Intercept est | 234 | 0198 | 645 | os) | 333 | 123 | 0849 | 0576 | x x

«| 204 [oro | 219 | 207 { 368 | 0981 | 108 | 102 x x
Log (WMT) est | 0417 | 0417 [ 0204 | 0206 | 0092 | 0066 | 080s | 0804 x x
[ageed 1 year) c{ 38 [39 | 139 | ise | 266 | 193 [aii | 408 x x
Log (Lane Mites) es_| 0329 | 0268 | 0156 | 0154 | 0456 | 0295 | 0161 | O16 x x

cf 3a [273 | oes | osi2 | 655 | 375 | 336 | 336 x x
Log (Population) est | 0328 [0370 | 0258 | O269 | 0555 | 0592 | O104 | 0105 x x

1_| 360 | 403 | 067s | 0632 | 636 | 597 | 273 | 272 x x
Log (income Per Capita) et | x | 0217 x | oo | x | 0302 x__| 0003 | x x
19985) t x 794 x | 0063 | x 402 x | ons] x x
[Short-Run Elasteity 0329 | 0268 | 0156 | 0156 | 0456 | 0295 | 0161 | 0161 x x
Long-Run Elasticity 056s [0460 [ 0196 | 0193 { 0502 | 0316 [| 0873 | 0824 x x
MID VOLUME
[Dependent Vanable LOGWMD,
State All States Maryland North Carolina Virginia DC - Balimore
Years of Data 1986-1995 1970-1996 1986-1997 1971-1996 1971-1996
IN 1200 [1200 | 40s [40s | 660 | 605 | 1300 | 1300 | 182 182
Adjusted R-Square 0994 0994 0990 0990 0995 0996 0995 0995 0991 0991
ctercept et _| 239 [ 170 | 0133 | -osao | 378 | 796 | osi9 | -o271 | oves | -262

t | si9 [198 | -0229 | 0636 {486 [550 [ 288 [ 101 | 0257 | 17
Log (WMT) es: | o6ia | oe | ose | 0559 | 03a | 0297 | 084s | 0833 | 0728 | 0697
[(ageed 1 year) cy 26s 260 Ti | 0 | or [ 8% | 386 [367 [zi | a
Log (Lane Mules) est | 0405 | 0400 | 0365 | 0376 | 0380 | o4i8 | 0137 | O141 | O10 | 0129

t | 706 [69s | 424 | 429 | 678 | 704 | 600 | 623 | 221 | 207
Log (Population) es | 0039 | o0ss } 0348 | 0301 | o28a | 0189 | ova | ors | 0249 | 0286

t_ [0904 [126 [ 762 [| 723 [ 418 | 237 [| saa | 557 [ 284 | 325
Log (Income Per Capua) et | x | 0057 | x | 0087 x | om | x | 007 x 0290
1998S) t x 108 x | 068 | x “378 x 396 x 216
[Shon-Run Elastcny 0405 | 0400 | 0365 | 0376 | 0380 | O418 | 0137 | o1a1 | 0140 | 0129
[Long-Run Elastien; ros | roa | oss { o8s2 [ 0576 | 0594 [ aves [ o8ee [ osia | 0428
HIGH VOLUME.
[Dependent Vanable LOG(VMT)
[State AllStates Maryland North Carolina Virginia DC - Baltimore
Years of Data 1986-1995, 1970-1996 1986-997 1971-1996 1971-1996
IN 200 [200 | 135 135 60 35 4] 234 | 23a | 234
[Adwusted R-Square 0997 | 0997 | 0997 | 0997 | 0993 | 0994 | 0996 | 0996 | 0999 | 0999
imercept ext | o9as | -143 [ 238 | 349 [ sos | s71 | 0736 | 156 | 180 | 236

uf a3 [a7 | 336 | 316 | 3i9 [tes [is | iso [377 [ 350
[Log (VAT) est_| 0673 | 0663 | 0645 | 0608 | 0197 | 0172 | 0897 | 0893 | 0751 | 0748
[dagged | year) cf ou2 To [03 | ess [a7 [ze [a6 Fai [ies | 187
Log (Lane Miles) es_| 0229 | 0229 [ 0162 | 194 | 0536 | 0501 | O15 | 0101 | o175 | 0176

« {365 [| 368 | 205 | 235 [| 49a [ sio | 288 [| 275 [| 383 | 385
[Log (Poputawon} ext | 0206 | 0255 | 0156 | 0204 | 0283 | 0376 | oon | O004 | 0070 | 0075

c[ 262 {302 377 | 370 | 206 [| 243 | 03 [ ons | 307 | 323
Log (income Per Capita) est | 0206 | 0187 x [|-ows] x [ons[ x [oo | «x | 0061
[c1998s) «| 262 | 134 x 131 x | 0353 [ x | 086s] x Hg
[Shon-Run Elasticity 0229 | 0229 | 0162 | 0194 [ 0536 | 0501 | 010s | 0101 | O15 | 0176
Long-Run Elasticny 699 | 0680 | 0457 | 0495 | 0667 | o60s | 102 | 0942 | 0702 | 0699



Table 3-1 focuses on the two specifications that are, in a sense, the most basic. These are log-log
(linear in logs) models that include basic sets of explanatory variables lane miles and population,
one also with and one without income per capita. As shown in Table 3-1 and the more complete
set of runs in Appendix A, the coefficients on Jane miles and population are fairly consistent
across specification - adding measures of income and employment does not significantly alter

their value or level of significance.

The R-squared values in these regressions are generally extremely high - above 0.95. This 1s not
surprising in a fixed effect specification, as the county and year intercepts capture most of the
residual variation not accounted for by the continuous explanatory variables in the model As

shown in Appendix D, most county and year intercept coefficients are significant in most runs

The results across the 5 study areas are very consistent, in terms of both the statistical
significance and the coefficient values, with respect to the lane miles and population variables
Income per capita 1s more varied and much less significant! The consistently strong significance
for population 1s not especially surprising, since the number of people living in an area 1s
expected to be a principal determinant of the level of vehicle travel in the area. The generally low
value and low significance for income per capita suggests that in most areas, increases 1n income
do not strongly correlate with increased vehicle travel (at least at the county level of analysis).
This also may reflect the fact that, quite often, greater distances must be covered in rural areas,
which also generally have lower income levels In addition, other recent analyses suggest that in
the U.S., although income has been an important estimator of travel levels in treme series
analyses, from a cross-sectional point of view travel levels and annual household expenditure on

vehicle travel are relatively insensitive to income’,

' With 60 or more degrees of freedom, at value of about 2.0 or higher represents significance at the 95
percent confidence interval (2-tailed test)

? Personal communication, Philip Patterson, U S. Dept of Energy, based on analysis DOE has done in
this area


ALL COUNTIES LOG REGRESSION (INCLUDING WASHINGTON DC)

Parameter Estimates

Parameter
Variable DF Estimate
INTERCEP 1 2.207920
LOGLM a 0 563626
LOGPOP 1 © 563862
LOGPCI98 = 1 © 195368
YR.1986 1 © 010912
yR_1987 1 0.048941
yRuiges ot 0 092404
yRise9 1. 0 122677
¥R_1990 1 0 129479
yRU1991 1 0.141308
YR199200 1 © 174781
YR_1993 1 0 191144
YR1994 1 0 206360
YRI1995 1. © 227435
MDANNEAR 1 0 278170
MDBALTIN 1 0 246324
MDCALVER 1 0.129706
MDCAROLI 1 -0 031942
MDCARROL 1 -0 001785
MDCECIL = 1 0 066290
MDCHARLE 1 © 025919
MDDORCHE = 1 -0 060059
MDFREDER 1 © 256114

2 0 003250
MDHARFOR 1 0 104382
MDHOWARD 1 0 456624
MDKENT 1 -0 139822
MDMONTGO 1 9 120164
MDPRNCGE 1 9 217558
MDQUEENA 1 9 338572
MDSTMARY = 1 -0 069818
MDSOMERS 1 0172374
MDTALBOT 1 0 371312
MDWASHIN, 1 0 237498
MDWICOMI 3 0 036263
MDWORCES 1 0 213989
NCALAMAN 1 0 077302
KCALEXAN 1. -0 235078
NCALLEGH 1 -0 566196
WCANSON 1 -0 121025
NCASHE 2 -0 385301
NCAVERY 1 0 145990
NCEEAUFO 1 -0 292611
NCESRTIE 1 0 152065
NCELADEN 1 -0 207016
NCBRUNSH 1 0 258872
NCBUNCOM 1 0 145225
NCBURKE 1 0 106772
NCCABARR 1 0 150393
NCCALDWE 1 -0 182378
NCCAMDEN 1 0 226503
NCCARTER 1 0 279695
NCCASWEL 1 -0 394242

Standard
Error

© 73415275
0 04745869
© 03979429
0 04671029
0 00771785
© 00806573
0 00882077
9 00950663
0 00944328
9 00927184
0 00993127
0 01061028
0 01135965
9 01214803
0 08442747
0 10432891
0 04403237
0 05570495
0 04423664
0 03512124
0 03774200
0 05247574
0 05144525
0 05220210
0 os190982
0 05001281
0 06447302
0 11291390
0 10442501
0 04546679
9 03405579
0 O664664a
0 05447081
0 04022485
0 03456943
0 0a34a162
0 04108053
0 09245003
0 09795778
9 06020871
0 06538901
@ 08381931
0 04134836
0 06469019
© 05299635
0 03689273
004946844
0 03596963
0 04315045
0 04238568
0 12104656
0 04488201
0 06808168

T for HO
Parameters0

4 007
13 876
14 295

4 183

1414

6 068
10 476
12 904
13711
18 241
17599
28 015
18.166
38722

3295

2 362

2 946
-0 573
-0 040
-1 887

0 687
“1245

4978

0.062
2.011

7 609
2.169

2 064

3.041

7447
-2 050

2 593

6 617

5 904

2 089

4 949

1 882
-2 543
“5 780
-2 oie
-5 908

1 742
-7:077
-2:352
-4 661

7017

2 934

2 913

3495
-4 303

1872

6 232
-§ 791

Prob > |T'

0 0027
0 0001
0.0002
0 0003
0 1575
0.0001
0 o001
0 0002
0 0001
0.0001
© oo01
© 0001
© coor
0 0001
0 0020
0 0183
0 0033
0 5664
0 9678
0 0592
0 4923
0 2525
0 0003
@ 9504
0 0445
0 0001
0.0302
0 2074
0 0024
© 9001
0 9405
© 0096
© 0001
© 0003
0 2943
0 002
0 D600
0 0132
0 0002
0 0445
o 0001
© 0817
0 0001
© 0188
0 0002
0 0003
0 0034
0 0036
0 0005
9 0001
0 0615
0 0001
0 0001


Dependent Vanable

TABLE A-3a

FIRST DIFFERENCE RUNS RESULTS - MARYLAND

VMT/CAP FIRST DIFF LOG(VMT/CAP) FIRST DIFF
ILogs of Independent Variables? no no no no no To yes yes yes yes yes yes yes yes
IN 621 621 621 621 621 621 621 621 621 621 621 621 621 621
|Adjusted R-Square 0040 0065 0.065 0070 0070 0.069 0073 0102 0 102 0 108 0107 0 108 0 104 0 108
INTERCEPT. est 0943 0797 0814 0784 0800 0795 0070 0.059 0058 0057 0.057 0.057 0059 0 057
L 220 188 191 185 189 187 361 3.04 301 297 295 296 305 298
|LM_CAP DIFF est x 1056 1040 4024 1009 1011 x 0599 0517 0577 0527 0522 0610 0 586
1 x 402 394 390. 383 384 x 438 340 422 347 344 446 428
POP DIFF est x x 22 56E-05} x 2 41E-05] -2 27E-05} x x -0 369 x 023) -0311 x x
t x x -1 02 x 0967 0 884 x x -123 x -0753_ | -0987 x x
IPC1_98 DIFF est x x x 3 18E-04 | 3 14E-04| 3 19E-04 x x x 0278 0257 0206 x 0.243
t x x x 213 210 2 x x x 226 203 154 x 139
JOBS DIFF est x x x x x -5 50E-06| x x x x x 0 126 0162 Q 100
t x x x x x -0.242 x x x x x eal 153 Q910
‘Dependent Vanable VMT FIRST DIFF LOG(VMT) FIRST DIFF
Logs of Independent Variables? no. no no no no no yes yes yes yes yes yes yes yes
iN 621 621 621 621 621 621 621 621 621 621 621 621 621 621
djusted R-Square 0396 0429 0439 0430 0440 0453 0105 0122 0120 0126 0125 126 0125 Q 127
INTERCEPT est_ | 69018 49959 472 48852_| 45893 1493 0.064 0057 0058 0056 0057 0.057 0058 0056
t 184 136 130 133 126 143 332 298 301 289 295 296 30! 293
ILM DIFF est x 2879 2704 2902 2727 2826 x 0522 0517 0536 0527 0522 O54 0 527
1 x 584 550 589 555 582 x 344 340 354 347 344 339 348
POP DIFF est x x 715 x 723 526 x x O14 x 0243 0 168 x x
t x x 333 x 337 241 x x 0.423 x 0877 0589 x x
PCI_98 DIFF est x x x 174 186 124 x x x 0232 [ 0257 | 0206 x 0183
L x x x 135 145 0975, x x x 188 203 154 x 143
IOBS DIFF est x x x x x 743 x x x x x 0 126 0 187 Q 142
L x x x x x 387 x x x x x iu 178 129



Variable DF

NCCHATHA
NCCHEROK
NCCHOWAN

PEP PPP BER PEE PP EP

NTI | 2 REL. oN

Parameter
Estimate

-0 121179
-0 263564
-0 330985
~0 228178
-0 068654
-0 315394
-0 058084
-0 203773
0 289096
0 446452
-0 019523
0 325852
~0 221223
0 321519
+0 402210
0 171164
-0 242085
0 101537
-0 235388
+0 793261
9 129602
+0 190435
0 102117
-0 071368
+0 053667
0 295887
0 174935
-0 208383
-0 128730
-0 669319
© 304634
0 061654
0 387686
0 187687
0.104144
-0 091430
-0..089234
+0 167100
+0 548728
+0 239596
0.257558
0.234267
-0 397092
-0 068392
-0 218107
0.140097
0 c49a91
0 ce4g00
-0 264479
0 254979
-0 175495
+0 241201
0.007097
-0 047153
-0 448820

Standard
Error

9.04533738
0 06624544
0 09878293
0 12517207
© 03423231
0.03706B45
© 03584053
0 05976813
0 os16ssae
0.05972100
0,03860910
0,05838019
9 04941200
0.04a6ca78
0 03559899
0.05845597
0 04719417
0 04645107
0 09618154
© 11275362
0.04655675
0 07839859
© 06925256
© 03509529
0 03542274
0 03769300
0 04562367
0 06592739
0 08381568
0 11140138
© 03436358
0 05348948
0 03754598
0 09277487
© 05822119
0 04041405
0 05412373
0 06436680
9 06950712
9 05686647
9 04696016
0 08117526
9 ops69804
0 95832145
0 03648264
o 03549178
0 04748186
0 06074893
0 04407535
0 04248957
© 10024678
0 07522184
0 06889607
© 11720941
0 06100289

JING

T fo:

Parameter:

SGT.

x HO

-2 452
-3 979
-3 351
+1 823
-2 006
B 508
-1 622
-3 409
3 S40
7 476
-0 503
5 582
“5 474
6 615
11 298
2 928
“5 130
2 186
-2 447
-7-035
2 784
-2 429
2 475
-2 034
-1 515
7 850
3 834
-4374
-1 536
-6 012
11 193
0 779
4 999
2 023
1 789
-2 262
1 649
-2.596
-7 895
-4 223
5 496
2 886
4.634
-1:173
-5 978
a 947
1 051
0 724
-6 000
6.001
-2.752
-3 207
-0 145
-0 402
-7:357

Prob > ||

© 0143
9 O00L
9 0008
0 0685
9 0450
D 0002
0 1052
0 0007
0 0004
0 9001
0 6150
0 D001
Q e001
0 0001
0.0002
0 0034
9 0002
0 0289
0 0145
0 0002
0 0054
0 0152
9 1405
9 0421
0 1299
9 0001
0 0002
0.0002
0 1247
0 0001
0 0002
0 4362
© 0001
0 0432
0 0738
0 0238
0 0994
0 0095
9 0002
9 0002
0 0002
0 0039
0 0002
0 2422
0 0001
9 0002
0 2935
0 4690
0 9002
0 0001
© 0801
0 014
0 8846
0 6875
0 0001


MARYLAND LOG CORRELATION MATRIX

[VARIABLE |DESCRIP_ | LOGVMT | LOGVCAP | _LOGLM_[LOGLMCAP|LOGVMTLM[LOGLMURB| LOGPOP_|LOGPOPDN] LOGPCI98 | LOGIOBS | LOGGASS8
LOGVMT Coefficient 1 -0 18107 091413 -0 88674) 0.95363) -0 73392 095819 0 92606] 0 67929) 0.96486) 0.094)
‘Std Error 0) 00001 0.0001 0.0001 0.0001 0.0001 00001 0.0001 0.0001 0.0001 0.0215]
IN 690} 644, 690 644) 690) 644 644 644) 644 644 598
ILOGVCAP Coefficient -0 18107, 1 -0 24475, 054396) 0.11307) 0 52899} -0 45489) -0 44225 017114 -0 37085 -0 14071
Std Error 00001 0 00001 0.0001 00041 00001 0.0001 00001 00001 0.0001 0 0006)
IN 644) 644 644 644! 644) 644) 644 644 644) 644 598}
ILOGLM Coefficient 091413] -0 24475 1 -0 74972} 074971 -0 64547 0.90317] 0 81807) 0.48292 0 89734 -0 01397
Std Error 00001 0.0001 0} 0.0001 0.0001 0.0001 0.0001 00001 0 0001 00001 0 7332]
N 690) 644 690) 644] 690 644) 644) 644) 644) 644) 598)
ILOGLMCAP {Coefficient -0 88674 0 54396 -0 74972, 1 -0 89524] 0 84696 -096121 +0 96537 -0 56115) -0 93657 0 05928]
Std Error 0.0001 0.0001 0 0001 0 00001 0.0001 0.0001 0.0001 0.0001 00001 0 1477]
IN 644) 644) 644) 644) 644! 644) 644) 644) 644 644) 598)
LOGYMTLM Coefficient 0 95363 -0 11307 074971 -0.89524| 1 -0 72197 0 8966) 0.90824 075533) 091205) -0 14563
Std Error 0.0001 00041 00001 0.0001 0 00001 0.0001 00001 00001 00001 0 0004}
N 690 644) 690) 644) 690] 644) 644) 644) 644 644 598)
ILOGLMURB = |Coefficient -0 73392 0 52899 -0 64547, 0 84696 -072197| ! -0 81847] -O 84887 -0 46414 -0 77243 | 003351
Std Error 0.0001 0.000! 0 0001 00001 00001 0| 00001 0.0001 0000! 0.0001 04134)
IN 644) 644 644 644) 644) 644) 644) 644 644 644 598)
LOGPOP Coefficient 0 95819} -0.45489) 090317 -096121 0 8966) -0 81847 1 0.96723] 056531 098159 -0 04427,
Std Error 00001 0.0001 0.0001 0.0001 00001 0.0001 0 0.0001 00001 00001 0 2798
N 644) 644) 644 644 644! 644) 644! 644] 644 644 598)
ILOGPOPDN = [Coefficient 0 92606; -0 44225 0 81807 -0 96537) 0 90824 -0 84887; 0 96723} 1 0 62805) 094619 -0 04774)
Std Error 0.0001 0.0001 0 0001 0.0001 0.0001 00001 0.0001 0 0.0001 00001 0 2438
IN 644 644) 644 644 644) 644) 644) 644 644) 644! 598)
ILOGPCI98 Coefficient 0.67929 017114) 0 48292] -0 56115) 075533 -0 46414 056531 0 62805! 1 062798 -0 27684|
Std Error 0.000) 0.0001 0 0001 0 0001 00001 0.0001 0 0001 00001 0} 00001 0.0001
N 644) 644 644) 644 644] 644) 644) 644) 644 644) 598)
ILOGJOBS Coefficient 0 96486 -0 37085 0 89734 0.93657 091205} -0 77243 0.98159] 0.94619} 0 62798 f -0 08399
Std Error 0.0001 0.0001 0.0001 0.0001 00001 00001 00001 0.0001 00001 0 0.04]
N 644) 644 644) 644) 644 644 644) 644) 644 644) 598
ILOGGAS98 =| Coefficient 0.094] -0 14071 -0.01397 0.05928 -0 14563 003351 -0 04427) -004774 -0 27684 -0.08399] 1
Std Error 00215 0.0006 0 7332| 01477 0.0004! 04134 0 2798) 0 2438 0.0001 004 0
IN 598) 598] 598} 598} 598 598) 598) 598) 598 598} 598



ALL COUNTIES LOG REGRESSION

variable DF

NCPITT
NCPOLK
NCRANDOL,
NCRICEMO
NCROBESO
NCROCKIN
NCROWAN
NCRUTHER
NCSAMPSO
NCSCOTLA
NCSTANLY
NCSTOKES
NCSURRY
NCSWAIN
NCTRANSY
NCTYRREL
RCUNION
NCVANCE
NCHAKE
RCWARREN
RCHASHIN
RCWATAUG
RCWAYNE
NCWILKES

VABUCHAN
VABUCKIN
VACAMPBE
VACAROLI
VACARROL
VACHARLE
VACHARLO
VACHESTE
VACLARKE
VACRAIG

VACULPEP
VACUMBER
VADICKEN
VADINWID
VABSSEX

2
1
1
1
1
sy
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
1
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
rf
1
1
1
1
1
1
a
1
1

Parameter
Eatimate

+0 300061
0 189885
-0 073258
+0 224960
© 060501
-0 173580
0 008396
-0 285965
-0 193097
-0 086229
-0 245212
-0 617348
0 134165
~0 100732
-O 137483
-0 355414
-0 178205
© 055189
0.164941,
-0 278999
+0 071278
0 027467
-0 236497
-0 117247
© 070324
© 238077
-0 384413
0 184790
Q 317127
0.167846
© 208492
0 045105
0.124015
0 640818
0 397282
-0 473295
+0 035141
0 584650
© 612080
0 163404
-0 086249
-0 168988
0 123716
0 788207
0 264704
0 121326
-0 241464
@ 326479
© 322398
+0 677273
© 124488
-0 280142
-0 289527
0 287302
9 303952

Standard
Error

0 03625839
© 09652077
0 03570654
0 04734208
0 03934881
© 03356732
© 04068389
0 04257155
0 03729535
0 ose4si638
0 04617735
0 04888329
0 03526513
0 09358294
© 06269735
© 33526324
0 03568403
0 osBes031
0 07581480
0 07400939
0 03935eqa
0 05965855
9 03690229
0 04044287
0 03439443
0 05965021
0 08104149
0 05265450
Q 03349695
0.07562410
0 10737763
0 06033723
0 09328534
0 06342455
0 03530207
@ 11770295
04064453
© 20152285
© 05452601
0 06874154
0 06546151
0 08338037
0 04326560
© 06187653
0 05494854
0.12376866
0.08064124
@ 05133838
0 09263244
0 12589437
0.06676767
0 11251797
0 08024045
0 05933106
0.09845927

T for HO
Parameter=0

-8 276
2 195
-2.052
-4 752
2 538
“5.272
0 206
-6 717
-5:178
“1475
-5 310
12 629
3 805
-1 076
-2 193
-2 630
~4 994
0 337
2176
+3770
+0 798
0 460
+6 469
-2 899
2 045,
3991
“4743
3 509
9 467
2 219
1 942
0 748
2 222
10 104
11 254
-4 021
-0 B65
5 759
11 225
2377
-1 318
-2 027
2 859
12 738
4817
9 980
-2 994

CENCLUDING WASHINGTON DC]

Prob > |T|

0 0002
0 0283
0 0403
0 0002
0 1243
@ goor
0 8365
© 0002
0 0001
0.1403
0 0002
D 9002
0 0001
0 2819
0 0283
g.0085
© 0001
0.3485
0 0297
0 0002
0 4251
© 6453
© 0001
0 0038
© 0410
0 0001
0.0001
0 0005
0 0002
0.0266
0 0523
0 4548
0.2218
0 0002
0 002
0 003
0 3874
0 0001
9 0002
0 0175
0 1878
© 0428
0 0043
0 9002
0 9001
0 3271
0 0028
@ 9002
0 0005
0 0001
0 0624
0.0129
© 0003
© 0001
0 0020


Il-€

TABLE 3-4

TRAFFIC VOLUME INTERACTION MODELS

All States model

Congestion Level

Congestion Level
interaction with

Dependent variable = LOG(VMT) interaction distributed lag
N 2420 2200
R-Square 0711 0.785
Intercept est 2728 0996

t (3.832) (1 501)
Log (VMT) lagged | year est 0 507

t (28 416)
Log (Lane Miles, low VMT/LM ratio) est 0 240 0175

t (3.151) (2 533)
(Confidence interval (95%) 0091 ~ 0.390 0040-0311
Log (Lane Miles, medium VMT/LM ratio) est 0530 0.356

t (7 192) (5.617)
(Confidence interval (95%) 0 385 ~0 674 0 232-0 480
[Log (Lane Miles, high VMT/LM ratio) est 0594 0310

t (7 846) (4.676)
(Confidence interval (95%) 0 446 ~ 0.743 0 180-0 440
Log (Population) est 0.548 0213

t (13.743) (5 463)
Log (Income Per Capita) est 0213 0 164

t (4.467) (3 899)
Long-Run Elasticity - Lane miles, low VMT/LM ratio 0.345
Long-Run Elasticity - Lane miles, medium VMT/LM ratio 0702
Long-Run Elasticity — Lane miles, high VMT/LM ratio O611

Note confidence interval provides the range of possible “actual” value for coefficient within 95% confidence interval



decades. In fully developed areas with traffic congestion, the effect of adding roadway capacity

on inducing additional travel may be more immediate.

1.2. ANALYSIS METHODOLOGY

Following the approaches of Hansen and Huang and Noland, the approach taken 1n this study 1s
to econometrically estimate the relationship between roadway capacity, measured as lane miles,
and vehicle travel, measured as daily vehicle miles of travel, while controlling for other key
factors that may influence travel. The extent of highway travel in an area 1s a function of many
factors. These include population, income, car ownership levels, land use, fuel prices (and other
variable costs of travel), and availability of alternative modes of travel, such as transit. Any
attempt to estimate the impact of additions to roadway capacity on travel levels must account for

as many of these factors as possible.

As described in the previous database documentation report, the database for this analysis
includes, for each county in Maryland, North Carolina, and Virginia, as well as the District of
Columbia, the following data’

e Geographic area,

¢ Population and population density,

e Income per capita,

¢ Employment (available as total employment and unemployment rate), and

e Extent of roadway lane miles in different roadway categories (see data discussion,
below).

Vehicle availability was not included sn the database since this 1s nearly universal now in the
U.S. (with more than one car per driver nationally), and it 1s quite highly correlated with the level
of population. Fuel prices, although potentially important, were not easily available on a county
level, only on a state level Use of state level data would result in all counties within a state
having the same fuel prices for a given year. This variable is therefore fully captured in any

regression model including an intercept term for each year of data, which all the models in this

1-6


TABLE A-3c
FIRST DIFFERENCE RUNS RESULTS - VIRGINIA

[loependent Variable VMTICAP FIRST DIFF LOG(VMT/CAP) FIRST DIFF
[Logs of Independent Vanables? no no. nO no A} no. yes yes yes yes yes yes yes yes
IN 2496 | 2496 [2496 | 2496 | 2496 | 2496 | 2496 | 2496 | 2496 | 2496 | 2496 | 2496 | 2496 | 2496
IR-Square 0217 | 0229 [ 0243 | 0233 | 0246 | 0246 [| 0174 | 0226 | 0264 | 0231 | 0266 | 0267 | 0226 | 0231
[Adjusted R-Square 0177 | o1s9 | 0204 | o194 | 0207 | 0207 | 0132 | 0186 | 0226 | 0191 | 0228 | 0228 | 0187 | 0191
INTERCEPT est 0076 | 0172 [ 0278 [| 0092 [| 0204 | 0203 [ 0008 | 0018 | 0034 | 0013 | 0031 | 0030 | 0017 | 0013
t 0237 | 0541 | o8ssi | 0289 | 0647 | 0645 | 0580 | 139 272 105 243 240 133 103
|[M_CAP DIFF est x 331 266 308 248, 248 x 0431 [ o149 | oat | o1as | 014s | 0433 | 0413
t x 599 | 478 | 556 | 445 445 x R6 | 356 120 345 347 127 120
POP DIFF est x x__|-163E-04|x__|-1 56E-04[-1 S6E-04] x x 0734 x -0712 [-0721 x x
t x x 667 x 640 | -637 x x HO x 106 | -107 x x
IPClo8 DIFF est x x x__|223-04[1 936-04] 1 94E-04] x x x 01st | 0103 | 0091 x 0147
t x x x 381 332_|[ 331 x x x 396 273 239 x 378
OBS DIFF est x x x x x__|-218E-06[ x x x x x 0048 | 0041 | 0020
t x x x x x -0 109 x x x x x 161 134_ | 0668
Se
Dependent Vanable VMT FIRST DIFF LOG(VMT) FIRST DIFF
[Logs of Independent Variables? no no nO no no no yes yes. yes yes yes yes. yes yes
IN 2496 | 2496 | 2496 | 2496 | 2496 | 2496 | 2496 | 2496 | 2496 | 2496 | 2496 | 2496 | 2496 | 2496
|R-Square 0466 | 0472 | 0501 | 0472 | 0501 | 0503 | 0208 | 0212 | 0214 | 0214 | 0216 | 0217 | o214 | 0214
\djusted R-Square 0439 | 0445 | 0475 | 0445 | 0476 | 0477 | 0168 | 0172 | 0173 | 0173 | 0176 | 0176 | 0173 | 0173
INTERCEPT. est 9641 [| 8705 | 994 | 8260 | -361 | -0293 | 0039 | 0038 | 0034 | 0036 | 0031 | 0030 | 0037 | 0035
t 0824 | 0747 | 0088 | 0708 | -0032 | 0000 | 312 3.03 272 285 243 240 293 278
ILM DIFF est x 76 666 724. 660 665 x 0152 [ o149 | o14g [ 0145 | 0145 | 0152 | 0149
t x 438_[ 461 486 | 456 | 460 x 362 356 354 345 347 363 356
P DIFF est x x 102 x 103 101 x x ou7 x 0143 | 0133 x x
t x x 7 x 9 116 x x 221 x 267 247 x x
PC1_98 DIFF est x x x 136 | 378 320 x x x 0034 | 0103 | 0091 x 0072
t x x x 0633 | 180 152 x x x 227 273 239 x 193
OBS DIFF est x x x x x 174 x x x x x 0048 | 0066 | 0057
L € x x x x x 242 x x x x x 161 225 191



1.1.1 Recent Work Estimating the Nature of Induced Demand
Recent papers by Hansen and Huang (1997) and Noland (1998) provide a review of recent

literature on estimating the relationship between roadway capacity expansion and induced travel

These two papers themselves provide important contributions to measuring this relationship

Hansen and Huang focus on metropolitan areas in California, using 17 years of pane] data They
estimated fixed effects models that included lane miles by roadway type, population and income
per capita. Their results were significant and indicate a strong relationship between lane miles
and VMT. They estimated long run elasticities on the order of 0.6 to 0.7 for counties and 0.9 to

1.0 for metropolitan areas.

Noland conducted a simular analysis using U.S. state-level data with 13 years of panel data,
including a variety of roadway types He estimated a vanity of fixed effects specifications and
distributed lag models. He estimated elastucities ranging from 0.3 to 0.6 short run and 0.7 to 1.0

long-run, on a nation-wide basis.

The analysis presented in this report follows these two previous efforts methodologically, but

provides two new contributions:

¢ It uses East Cost data to test the structure of the VMT - lane mile relationship in states
with many older, long - established communities.

¢ It provides a more detailed investigation of the differences in the VMT — lane mile
relationships across different types of counties, varying by population density and traffic
volumes.

In particular, this analysis attempts to address the question of whether induced demand occurs
differentially in areas that are initially low traffic volume (but in some cases high population
growth) v. areas that are more heavily congested but that are essentially fully developed. One
hypothesis 1s that areas that are newly developing will show a bigger difference between the short
run and long run elasticity than more established areas, since the effect of new roadways may be

to spur new development which will not occur immediately, but over a period of years or even

1-5


TABLE A-1b

DESCRIPTION OF FIRST DIFFERENCE VARIABLES

FOR ALL STATES

Vanable:

Description:

VMTDIFF

First difference - VMT

)VCAPDIFF

First difference - VMT per capita

LMDIFF

First difference - lane miles

ILCAPDIFF

First difference - lane miles per capita

POPDIFF

First difference - population

PCIDIFF

First difference - per capita income in 1998$

JOBDIFF

First difference - employment by place of work

L_VMTDFF

Log of first difference - VMT

L_VCPDFF

Log of first difference - VMT per capita

L_LMDIFF

Log of first difference - lane mules

L_LMCDFF

Log of first difference - lane miles per capita

L_POPDFF

Log of first difference - population

L_PCIDFF

Log of first difference - per capita income in 1998$

L_JOBDFF

Log of first difference - employment by place of work



L@

TABLE 2-4
ALL STATES LOGARITHMIC CORRELATION MATRIX

VARIABLE _|DESCRIP. LOGVMT_| LOGVCAP LOGLM_|LOGLMCAP|LOGVMTLM|LOGLMURB]_LOGPOP_|LOGPOPDN| LOGPCI98 | LOGJOBS | LOGGAS98
LOGVMT Coefficient 1 003744 0 86461 -0 70735 0 85887 -0 67331 0 92595] 081501 059347 0 78446 0.00302}
Std Error 0} 0 0656 0.0001 0 000! 0.0001 0.0001 0.0001 0.0001 00001 00001 0 882
IN 2420! 2420) 2420) 2420) 2420 2420] 2420) 2420] 2420) 2420; 2420)
ILOGVCAP Coefficient 0 03744 1 -0 00877 053135 0.07409 0 34676] -0 34272 -0 34428 0 10286 -0 23615; -0 06004
Std Error 0 0656 iY) 0 6662 00001 0.0003) 00001 00001 00001 00001 00001 0.0031
N 2420] 2420) 2420) 2420) 2420) 2420) 2420! 2420 2420) 2420 2420)
ILOGLM. |Coefficrent 0 86461 -0 00877 1 041741 0.48523 -0 47708 081613 0.57964) 0 36472| 071394) 0.04508
Std Error 0.0001 0 6662 0 00001 00001 0.0001 0.0001 0.0001 00001 00001 0 0266]
N 2420) 2420) 2420] 2420) 2420] 2420 2420) 2420) 2420) 2420 2420)
LOGLMCAP = |Coefficient -0 70735 0 53135) -0 41741 1 -0 80546 0.78831 -0 86578] -0 9075 -0 50037 -0 68178] -00011
Std Error 00001 0.0001 0.0001 0} 0.0001 0.0001 0.0001 0.000! 0000! 0.0001 0 9567
IN. 2420) 2420; 2420 2420) 2420 2420! 2420) 2420 2420] 2420) 2420]
ILOGVMTLM {Coefficient 0 85887] 007409 0 48523 -0 80546 1 -0 6854] 077941 0 82744) 0.66099] 0 63737 -0 0407,
Std Error 00001 0.0003} 0.0001 00001 0 0.0001 0.0001 0.0001 00001 0.0001 00453)
N 2420) 2420 2420) 2420) 2420] 2420! 2420) 2420) 2420) 2420 2420)
ILOGLMURB = |Coeffictent -0 67331 0 34676} -0 47708) 0 78831 -0 6854 1 -0 76402] -0 80957 -0 49226 ~0 68868 -0.01255|
Std Error 00001 0.0001 0.000 0.0001 0000! 0 0.0001 0 0001 0.0001 0.0001 05371
N 2420) 2420) 2420 2420) 2420 2420 2420) 2420) 2420 2420 2420;
ILOGPOP Coefficient 092595 -0 34272} 081613 -0 86578 077941 -0 76402 1 0 8963 0.51904] 0 82672 0.02553
Std Error 00001 0.0001 0.0001 00001 00001 00001 0 0.000! 00001 0.0001 0 2093
N 2420 2420! 2420 2420) 2420 2420) 2420! 2420) 2420| 2420 2420)
LOGPOPDN = |Coefficient 081501 -0 34428} 057964 -0.9075| 082744 -0 80957 0 8963 1 0 64643 0 74785 005157
Std Error 0.000! 0.0001 0000! 0.0001 00001 00001 0.0001 oY) 00001 0.0001 00112)
IN 2420 2420) 2420) 2420) 2420) 2420 2420 2420) 2420 2420) 2420;
ILOGPCI98 Coefficient 059347] 0 10286] 0 36472 -0 50037 066099 ~0 49226) 0 51904] 0 64643 1 0.49796 -0 02778
Std Error 0.0001 0.0001 00001 0.0001 00001 0.0001 0.0001 00001 0 00001 01719
IN 2420) 2420) 2420) 2420) 2420) 2420) 2420] 2420) 2420 2420) 2420
ILOGJOBS Coefficient 0 78446 -0 23615} 071394 -0 68178 063737 ~0 68868} 0 82672 074785 0.49796 i 001814)
Std Error 0.0001 0.0001 0.0001 00001 0.0001 0.0001 0.000! 00001 00001 0 03725
IN 2420 2420) 2420) 2420) 2420) 2420} 2420) 2420] 2420) 2420 2420)
ILOGGAS98 =| Coefficient 0.00302 0.06004} 0.04508 -00011 -0 0407) 0.01255} 0.02553] 005157 0.02778; 001814] 1
Std Error 0 882 0.0031 0 0266) 0 9567| 0.0453 05371 0 2093 00112) 01719 03725 0}
IN 2420 2420 2420) 2420} 2420} 2420] 2420) 2420] 2420] 2420) 2420



2%

TABLE 2-1
AVERAGE COUNTY VALUES OF KEY VARIABLES IN 1995 (BY STATE AND AREA)

Units Maryland | North Carolina{ — Virginia DC Balt All
Total # of Counties x 23 100 96 16 220
[Average Geographic Area square miles 421 487 399 417 440
Average Population people 188,699 71,867 45,582 326,878 74,804
[Average Population Density people/sq. mile 422 148 194 1,155 237
Average Daily VMT miles/day 3,536,397 1,297,601 1,064,583 5,834,860 1,457,690
Average Daily VMT per Capita VMT/person 21.62 20.55 29.25 19.77 24.43
Average Lane Miles mules 624 42 364 60 260.28 683.45 349.45
Average Lane Miles per Capita lane miles/person 0.0072 0.0087 0.0117 00031 0.0098
Average VMT per Lane Mile VMTilane mile 4,357 3,055 3,475 8,224 3,392
Average Income per Capita 1998$ 24,644 19,846 20,891 29,623 20,865
Average Total # of Jobs jobs 101,128 43,705 31,481 149,293 47,508



UCED DEMAND: THE ISSUE AND UNDERLYING ECONOMIC THEORY

11

The concept of “induced demand” involves the idea that additions to roadway capacity result in,
or induce, increases in vehicle travel on the roadway above the level that occurred before the
capacity addition, after accounting for other exogenous travel factors such as changes in
population Whether, and to what extent, addition of roadway capacity “induces” additional
travel, has been a cause of controversy in recent years. If this 1s the case, it suggests that some of
the benefits of roadway capacity expansion are eroded by this travel response (such as reductions

in traffic congestion).

As mentioned above, the basic hypothesis to be tested in this analysis 1s that this relationship
exists: that expansions to highway capacity yield increases in travel, over me. This analysis
tests this hypothesis using data at the county level of aggregation: do increases sn the total Jane
miles of major roadways in a county result in increases in travel on those roadways? An
additional hypothesis tested here 1s that the “rebound” effect (the increase in travel that occurs
after a capacity increase) will be stronger in areas with higher traffic volumes (and that

presumably experience more congested travel) than areas with lower traffic volumes.

The basic theory underlying the concept of induced travel demand 1s straightforward and well
documented and requires little treatment here. The addition of roadway capacity, either through
additional miles of roadway or additional lanes on an existing roadway, improves traffic flow and
therefore lowers the ume cost of travel. At some level of congestion, any given driver will
choose to avoid dealing with that congestion, either in favor of an alternative route, an alternative
mode, changing the departure ume of the travel, or avoiding the tp entirely. This 1s illustrated
in schematic form in Figure 1-1 Since each traveler experiences declining utility with each mile
traveled, at some point the cost of travel exceeds the benefit to the driver. Increasing ume cost
associated with increasing congestion will, in part, determine this pount. This 1s shown as point
“a” in the figure. If, however, congestion is relieved through the addition of roadway capacity,

the entire cost curve shifts outward (reflects a shift toward lower travel time cost). This allows


Variable DP

PEP PPE

VIRGINIA COUNTIES LOG REGRESSION

Parameter
Batimate

-0.239434
0.212041
-0.140626
-0.001724
0.531219
0 653263
-0.362771,
482467
-0 322491
-0.217397
0.076901
-0.820048
-0.323528
628983
328916
207918

°

e°o°o

Standard
Error

0.02878356
0 02925246
0,02838879
0,03092876
0.02906545
0.03063177
05157083
0.03511206
0.02967195
0 03733369
0.03007988
0.03230010
0.02889791
0.03415532
0,03195773
0.02988425

T for HO:
Parameter=0

8.318
7.249
74.954
-0.056
18.277
22.326
77.034
13.741
-10.869
~5.823
2.557

Prob > |T|

0.0003
0.0001
0.0002
0.9556
© 0001
0.0002
0.0001
0.0001
0.0002
9.0003,
0.0106
0.0001
0.0001
0.0002
0.0002
0.0003


ALL STATES LINEAR FIRST DIFFERENCES CORRELATION MATRIX

VARIABLE | DESCRIP_| VMTDIFF | VCAPDIFF | LMDIFF | LCAPDIFF | POPDIFF PCIDIFF JOBDIFF
'VMTDIFF = |Coefficrent t 0 38789) 03149 003576 057387 009017 0 33974
Std Error 0} 0.0001 00001 00935 00001 0.0001 00001
N 2200} 2200) 2200) 2200 2200 2200 2200)
VCAPDIFF = |Coefficrent 038789 1 007936 0 14756 -0 05762| 0.09656 -0 00728}
Std Error 0.0001 0 0 0002 0.000! 0 0N69) 0.0001 07331
N 2200) 2200) 2200) 2200 2200] 2200} 2200)
LMDIFF Coefficient 03149] 007936 I 0 44658 0 23122 0.0325 0125
Std Error 00001 0 0002! Q 0.0001 00001 0 1275 00001
N 2200) 2200) 2200 2200) 2200 2200} 2200)
LCAPDIFF |Coefficient 0.03576 0 14756 0 44658 i 006799 0 10405] -0 00464
Std Error 0.0935 00001 00001 0 00014 00001 08277)
N 2200) 2200) 2200 2200) 2200} 2200) 2200)
POPDIFF Coefficient 057387 -0 05762 023122 -0 06799 t 0 05723} 0.47917)
‘Std Error 00001 0 0069] 0000! 00014 0 0 0073 0000!
IN 2200} 2200) 2200; 2200; 2200) 2200, 2200
PCIDIFF Coefficient 009017 009656 00325 0 10405 0 05723 I 0 23797
Std Error 0 0001 0.0001 0 1275| 0000! 0.0073 0} 00001
IN 2200 2200) 2200) 2200} 2200} 2200) 2200)
SOBDIFF Coefficient 0 33974 -0.00728} 0125 -0 00464 047917 0 23797 1
Std Error 0.0001 07331 00001 08277 00001 0.0001 0
N 2200| 2200) 2200) 2200) 2200 2200 2200)



The coefficient for lane miles 1s perhaps surprisingly consistent across study area, and does not
vary significantly with the introduction of other variables, such as income (As shown in
Appendix A, it also does not vary significantly for most specifications that introduce other
variables or divide through VMT or lane miles by population.) The greatest deviation from the
“typical” coefficient value for lane miles 1s for the DC-Balumore area. For each of the three
states and for the “all states” runs, lane mules has a coefficient value between 0.43 and 0.59, the

values for DC-Baltumore are somewhat lower, about 0.33.

These results indicate that, after controlling for population and income, a ten percent increase in
lane miles correlates with 3% percent to 6% increase in daily VMT, at least in the mid-Atlanuc
region Since these models do not include any lag structure, this result should be interpreted as a
short run response. The high t-statistics and low variation in results by area suggests that the
result 1s fairly robust. This 1s especially true considering the significant differences in the

characterisucs of the different study areas, as discussed above in Section 2

The slightly lower lane miles coefficient for DC-Baltumore suggests that more densely populated
areas may not have quite as large a travel! “rebound effect” as more outlying, rural counties (or at
least the average county 1n these states). This may reflect more mature land use patterns in these
areas, or more alternatives to car-based travel However, the availability of greater alternatives
could work 1n the other direction — by creating more options for people to avoid driving during
congested conditions, which could induce a greater return to autos once these conditions are
alleviated. The result may instead reflect a different kind of lane-mile addition and response than
1s more typical in outlying areas — one that 1s related to the addition of new lanes on existing
roads, rather than new roads. If so, the scope for travel response may be, in percentage terms, not
as great as for an enurely new road. We explore this question, rural v urban rebound effect,

further below

For the “all states” regressions, the lane mile coefficient 1s actually slightly larger than for any of

the individual study areas: a 10% change in lane miles correlates with about a 5.6% to 5.9%

34


EEA collected data for this analysis at the county level for Maryland, North Carolina, Virginia,
and the District of Columbia. EEA also made extensive efforts to obtain data for Baltrmore City

but was unable to do so. This data appears to exist but not in any readily available form

As described in section 1.2 above, a variety of variables were obtained for each county in the
database. Most of these variables were obtained from the agencies that collect this data in each
of the three state governments. Most of the variables were straightforward to obtain and
incorporate into the database The greatest data development effort was need in obtaining and

cleaning the VMT and Jane mile data, and this deserves some discussion here.

The VMT and lane mile data that states submit to the Federal Highway Administration for use in
the Highway Performance Monitoring System were not available (and most cases are not kept)
on a county-by-county basis However, each of the three states does collect and track this data at
a county level. However, in most cases the data does not cover all roads or travel within each
county, and so the state totals EEA obtained do not match the summary statistics for each state
produced by the FHWA. In particular, each of these states only collect data on travel and
roadway extent for roads that are state-maintained. In each of the states included 1n the analysis,
this included all interstate lane miles, all state highways, and many (but not all) other primary
roads. Data covering some secondary roads was obtained for Maryland and North Carolina but
not for Virginia. EEA was not able to obtain estimates of the percentage of primary and
secondary roads in each state that are state-manntained, so there may be significant variation in

the roadway coverage in each state.

A discussion of the VMT data collection approach in Maryland and Virginia was provided in the
previous EEA report but 1s summarized here. The method of VMT data collection appears to be
similar in the three states. In each case, the states collect VMT data primarily through traffic
counts on a sample of roadway segments. Each state has a large number of portable “periodic”
traffic counting devices, and these are placed on different roadway segments for several days at a

time throughout the year in order to obtain the samples. There are also dedicated “continuous”


o£

TABLE 3-3

SUMMARY OF SHORT / LONG RUN ELASTICITY RESULTS

Dependent Variable LOG(VMT)
State All States Maryland North Carolina Virginia DC - Baluumore
Years of Data 1986-1995 1970-1996 1986-1997 1971-1996 1971-1996
N 2200 2200 621 621 1200 1100 2496 2496 416 416
Adjusted R-Square 0 826 0 830 0960 0960 0899 0890 0965 0965 0985 0985
Intercept est. 2.72 0727 148 128 470 461 0.489 0118 127 0754
t 5.97 1.07 362 204 8 28 509 331 0531 359 126
Log (VMT) est. 0508 0504 0559 0559 0.187 0 164 0841 0 840 0.782 0779
(lagged | year) t 28.4 28.2 160 160 8 04 693 719 778 24.7 244
Log (Lane Miles) est 0 355 0.338 0.215 0215 0.426 0.394 0128 0129 0129 0130
t 840 800 409 4.10 102 8.60 678 6 86 3.64 365
Log (Population) est 0.175 0227 0.288 0 284 0.367 0.373 0.086 0.086 0.097 0095
t 480 587 855 8.05 761 654 703 699 369 358
Log (Income Per Capita) est. x 0.162 x 0.026 x 0055 x 0.041 x 0061
(1998$) t x 391 x 0.428 x 1 x 222 x 106
Short-Run Elasticity 0355 0.338 0215 0215 0 426 0.394 0128 0129 0129 0 130
Long-Run Elasticity 0722 0681 0488 0.489 0 525 0.471 0 808 O8il 0594 0.586



MARYLAND COUNTIES LOG REGRESSION

Parameter Estimates

Parameter Standard = T for HO-
Variable DF Estimate Error Parameter«0
INTERCEP 1 3 186858 = 0 69022704 4617
LOGLM 1 0 451056 =o 05635198 8 004
LOGPOP 2 0 654949 0 02978043 21 993
LOGPCI98 =k 0 026074 = 0: 07060452 0.369
YR19700 1 © 059153 0 02157769 2 741
YR971 1 0 107243 0 02167440 4943
YR1972, 1 © 136191 0: 02226263 6 207
YR1973, 1 0 189500 0: 02353059 8 053
YR19740 1 © 171356 = -o.:02295086 7 466
YRI975 1 0 239932 0 02272705, 210 557
YRUS76 1 0 269360 0 02332926 11 546
¥RIUS77, 1 0 265461 =O: 02344640 11 322
YR1978°0 1 0.333057 0 g2asc029 13 594
YR_1979 1. 0 320733 0 02431423 13 191
YR1980 1 0 3395520 02373337 14.307
YR_1981 01. 0 386174 «=o: 02403365 14.820
YR198200 1 0 354163 0 02416277 14.657
YR_1983 1 0 412701 0 02533644 16 289
YR19840 1 0 427758 =D 02781201 15 380
1 © 467300 0 -02935604 1s 918
1 © 506615 0 03149970. 16 083
1 0 544246 «= 0 03276460 16.612
1 0 561261 0 03408010 16 469
2 0 570873 0 03477056 16 418
1 0 593067 0 03423487 17 323
2 0 606558 = 0: 03295776 1a 404
1 0 597837 -o 03281975 18 216
1 0 579623 0 :03298670 17571
1 0 65160 = -0. 03380301 16.719
1 0 570174 =o 03422189 16 661
1 0 582066 0 03478966 16 731
1 0 258983 =o 05699665 4 544
1 0 199149 © 0 07706712 2 584
2 0 051876 © o:04750251 1 092
2 -0 018493 0 04542975 -0 407
1 -0 044324 0 :02553509 -1.736
2 -0 008687 0 :02356280 +0 369
2 0.041599 9 02260309 1 640
1 -0 043245 © 0 04295254 -1 007
1 0 269628 = -0.:04148748 6 499
1 0 065728 = 0: 03586954 1 932
1 -0 102381 —0:02938812 -3 404
1 0 497471 © 03830582 12 726
2 -0 060617 0: 05576232 -1 087
2 0.177214 «0 :07685726 2 306
1 0 234589 = 0 :07953612 2.949
2 0 455222 0 04645886 9.798
1 -0 131237 -0,.02175300 -6 033
1 0 221009 0 :05475512 4 036
2 0 494238 = 0: 05990784 8 250
WASHINGT = 1 0 183565 0 03060669 5.998
WICOMICO 1 0 049692 —-0:02302646 2 158
WORCESTE = 1 0 294623 0 :03862094 7 629

Prob > |T

0 9002
0.0003
0 0001
0.7120
0 0063
© 0001
0 9001
0 0001
0 0001
0 0001
0 0001
0 0001
0 0001
0 0001
0 0003
0 0003
© 0002
© 0001
© 0001
© 0001
© 0002
© 9001
9 0001
0 9001
0 0002
0.0001
0 0001
0 0002
0 0002
0 0002
0 0001
0 0001
0 0100
0 2752
0 6841
0 0832
0 7125
0 0662
0 3144
0 0001
0 0674
0 0005
0.0002
0 2775
9 0215
0 0033
0 0001
© 0001
9 0002
© 0002
0 0003
0 0313
0 0001


Auu WUUNTI5s Ws REGRESoLUN [am uuvING masninGTOn uw

Variable DF

NCCATAWB
NCCHATHA
NCCHEROK
NCCKOWAN
NCCLAY
NCCLEVEL
NCCOLUMB
NCCRAVEN
NCCUMBER
NCCURRIT
NCDARE
NCDAVIDS
NCDAVIE
NCDUPLIN
NCDURHAM
NCEDGECO
NCFORSYT
NCFRANKL
NCGASTON
NCGATES
NCGRAHAM
NCGRANVI
NCGREENE
NCGUILFO
NCHALIFA
NCHARNET
NCHAYHOO
NCHENDER
NCHERTFO
NCHOKE
NCHYDE
NCIREDEL,
NCJACKSO
NCJOHNST
NCJONES
NCLEZ
NCLENOIR
NCLINCOL
CMACON
NCMADISO
NCMARTIN
NCMCDOWE
NCMECKLE
NCMITCHE
NCMONTGO
NCMOORE
NCNASH
NCNEWHAN
NCNORTHA
NCONSLOW
NCORANGE
NCPAMLIC
NCPASQUO
NCPENDER
NCPERQUI

eperererer eee Ten eaen nena ee tte ee ee ee ee

Parameter
Estimate

© 050599
-0 128869
-0 167450
-0 272899
-0 125698
-€ 080389
-0 267357
~0 068234
-0 250157

0 348372

0 474914
-0 055791,

0 310398
+0 176664

0.225065
-0 370984

0 041565
-0 196863

0 045355
-0 131951
+0 646205,

© 171257
-0 117623
-0 021411
-0 022191
-0 022657

0 313228

0 124036
+0 206085
+0 034689
-0 540833

0 355319

0.109696

0 185511

0 265973

© 085530
+0 086475
-0 099003
-0 122025
-0 453299
20177171,

0 305391

0 073802
-0 318418

0.001634
-0 266225

0 125855
-@ 015430

0.335297
-0 254422

0 177071
-0 100067
-0 215444

0 055549

0 044sa1

Standard
Error

0 04689193
0 04520488
0 06988344
0 09938780
© 12708723
0 03421924
0 03057426
0 03578857
0 06056805
0 08257807
0 os98asos
0 03962408
0 05827863
0,04164621
08363371
03624288
06596706
04824445
04818701
09896101
117704390
04743943
08002132
07504870
03698775
03606041
03777992
04705362
06856434
08647572
11518295
03494488
05571687
03740887
09430335
08817345
04028001
05397144
06502482
07290860
05858632
04806882
08950994
08742441
06046695
03812320
03552235
04982537
06433734
04397694
04624742
10148625
07519223
05096354
11881026

eoccceesccce cece ose oOo SOD OOD DODO SOOO DO DOO

T for HO
Parameters0

1 079
-2 630
-2 396
-2 746
-0 989
-2 349
-6 913
907
130
4 219
7 930
-1 408
S 326
-4 242
4 196
-10 236
0 630
-4 081
0 941
-1 333
-5 490
3 610
-1 470
-0 265
-0 602
-0 628
8 291
2 636
-3 006
-0 401
-4 695
10 168
1 969
4 959
2 820
1470
-2.147
1 834
-1:077
-6 217
-3 024
6.353
0 825
-3 642
0 027
+6 983
3543
-0 320
2 103
+5 785
3 829
-0 986
+2 865
1 090
0375

Prob > |T]

© 00000000000000900020000009000000000000000000000000000000

2807
0086
0167
0061,
3227
0189
0001,
0567
0001
0001,
0001
1593
0001
oe
001
ooo1
5287
ooo.
3467
1826
0002
0003
1417
7754
5475,
$299
0002,
oo84
0027
eed
9001
0002
0492
0001
0048
1416
0319
0667
0607
0001,
002s,
0002,
4097
0003
9784
ooo1
0004
7568
0356
0001
0001
3242
0042
2758
7078


counters that are kept permanently in one location, but generally far fewer of these than portable
counters used for sampling. A special effort 1s often, but not always, made to collect data on
segments that are being considered for or recently had changes in capacity. WMT samples are
aggregated to estimates of total VMT using a fairly standard methodology, involving the
development of growth factors for each roadway link, based on VMT changes from previous
years’ sampling data. Although the basic approach to data collection appears similar in each
state, the details of the approaches differ somewhat, as do the number of traffic counters and the
frequency of sampling each roadway segment. This 1s, then, a source of uncertainty in the
accuracy and consistency of the VMT data across the states For this reason, EEA has chosen to
estimate separate regression models for each state (as presented below in Section 3).

There are several other weaknesses 1n the data that have been documented in the previous
reports. One 1s that no distinction is made between entirely new roadways and lane-mile
additions onto existing roadways. These two types of capacity expansion may have quite
different relationships to levels of travel, but we were not able to distinguish these in our
analysis. Also, since all VMT data 1s provided as average daily levels, no specific congestion
data or level-of-service data for each county was available. Third, the availability of years of
data varied considerably by state, with Virginia and Maryland data available back to 1970 and
1969, respectively, while data for North Carolina and DC does not extend back before 1985 and
1984, respectively. Finally, as always, there were a number of data errors, outhers, and other
issues 1n the obtained databases that required attention before the Analysis could be undertaken.
These issues and EEA’s methods for addressing them are documented in the previous EEA

documentation report (EEA, 1998).

It should also be noted that there are some defimtional differences between the roadway types
included in the databases from the four areas. For Virginia and North Carolina, data for VMT
and lane miles includes only interstate and primary state-maintained roads. No secondary roads
are included. This 1s because VMT data was unavailable for non-state-maintained roads, which
includes primarily local secondary roads. However, some primary roads are not state-

maintained, which means that our database does not include all primary roads in either state.

1-10


Model: MODEL2
Dependent Variable LOGVMT

Source

Model
Brror
¢ Total

Root MSE
Dep Mean
cv

MARYLAND COUNTIES LOG REGRESSION

oF

52
591
643

Analysis of Variance

Sum of Mean
Squares Square P Value
673 62543 12 95434 2422 590
3 16026 0 00535
676. 78569
0.07313 R-square 0 9953
14 16633 Ad} R-sq 0 9949

0.51619

ProboP

0 0002


NORTH CAROLINA COUNTIES LOG REGRESSION (PRIMARY ROADS ONLY)

variable DP

WATAUGA,
WAYNE
WILKES
WILSON
YADKIN
YANCEY

a
1
2
1
1
1

Parameter
Estimate

-0.130110
-0.357276
-0,243925
-0.027524
0 096216
-0.519649

Standard
Error

0.06496641
0.02621529
0 04015156
0.03774488
0,07106631
0,10549447

T for HO:
Parameter=0

-2.006
713.629
-6.075
+0.729
1.354
4.926

Prob > [TI

0.0452
0.0002
0.0002
0.4660
0.1760
0.0003


VIRGINIA LINEAR CORRELATION MATRIX

VARIABLE _ [DESCRIP VMT VMT_CAP LM LM_CAP. VMT_LM POP POPDEN_|LMCAPURB PCI_98 JOBS GAS_PR9I8
VMT Coefficient | 0.03891 0 55366 -0 32195] 0.90035) 09521 0 39245 0 18831 0 58725 023317] -0 10553
Std Error 0 0 0476 0.0001 00001 00001 00001 00001 00001 00001 0.0001 00001
IN 2592 2592) 2592) 2592 2592 2592 2592| 2592 2592 2592! 2496)
VMT_CAP —|Coefficient 003891 ] 0 15689} 0 42236 0 08566} 0 15553} -0 12567] -0 0839) 015583, 0 00408) -0 19394
Std Error 00476 O| 0000! 00001 0.0001 0.0001 00001 0 000! 0.0001 0 8356] 00001
IN 2592 2592) 2592| 2592) 2592 2592! 2592 2592 2592 2592 2496)
LM Coefficient 055366 0 15689} 1 -0 14584 0 34566) 0 45065) 0.08653 0 13202) 0 27278 0 29103 -002114
Std Error 0.0001 00001 0] 00001 0.0001 00001 00001 0 0001 0.0001 0.0001 0291
IN 2592 2592 2592 2592 2592 2592 2592| 2592 2592 2592) 2496)
LM_CAP Coefficient -0 32195 0 42236} -0 14584) 1 -0 44551 -0 34932 -0 23626, -0 3398) -0 28684 -0 09538) 0 01886,
Std Error 0000! 0.0001 0000! 0} 00001 0.0001 00001 0.0001 0.c001 00001 0 3463
IN 2592 2592 2592) 2592 2592 2592! 2592 2592| 2592 2592) 2496)
VMT_LM Coefficient 0.90035: 0 08566} 0 34566) -0.44551 1 0 85788 0 54955) 027931 0 6808 0 26182) -0 14992
Std Error 0.0001 0.0001 0000! 0.0001 td) 00001 00001 0.0001 0.0001 00001 00001
IN, 2592 2592 2592 2592 2592 2592 2592) 2592 2592 2592) 2496)
POP Coefficient 09521 -0 15553} 0 45065, -0 34932, 0 85788 1 0 45255, 0 15074, 051921 0 18822) -0 02796,
Std Error 0.0001 00001 0.0001 00001 0.0001 | 00001 0.0001 0.000! 00001 0 1626]
IN 2592| 2592) 2592 2592 2592) 2688) 2688) 2592| 2592 2592) 2496
POPDEN Coefficient 039245 -0 12567 0.08653) -0 23626] 054955 0.45255] 1 0.08905 051299 0 33269] -0 01312)
Std Error 0.0001 0.0001 0.0001 00001 00001 00001 0} 0.0001 00001 0.0001 05125)
IN 2592| 2592 2592! 2592 2592 2688 2688 2592 2592| 2592 2496)
ILMCAPURB = {Coefficient 0 18831 -0 08391 0 13202 -0 3398) 027931 0 15074) 0.08905) 1 0 27647] 0 25523} -0 02035
Std Error 0 0001 0.0001 0.0001 0.0001 0.0001 0.0001 00001 0 0.0001 0.0001 0 3094)
WASHINGTONN: 2592 2592! 2592! 2592 2592) 2592 2592 2592 2592 2592) 2496)
PCI_98 Coefficient 0 58725 0 15583 0 27278 -0 28684 0 6808) 051921 0.51299) 0 27647, i 0 29194 -0 24474!
Std Error 0 000! 0.0001 0000! 00001 0.0001 00001 00001 0 0001 0 00001 0.0001
IN 2592! 2592! 2592 2592! 2592) 2592! 2592| 2592! 2592 2592 2496)
JOBS \Coefficsent 0.23317, 0.00408 0.29103 -0 09538 026182 0 18822 033269 0.25523 0 29194 1 -0. 05017
Std Error 0.0001 0 8356) 00001 00001 0.0001 00001 00001 00001 0.0001 0 00122]
N 2592| 2592 2592 2592 2592 2592 2592 2592 2592 2592! 2496)
IGAS_PR98_— | Coefficient -0 10553} -0 19394) -0 02114] 0.01886) -0 14992 0.02796} -0.01312) -0.02035 -0 24474 -0 05017 1
Std Error 0 0001 0 0001 0291 0 3463 0.0001 0 1626 05125) 0 3094 0.0001 00122) O}
IN 2496 2496) 2496) 2496) 2496) 2496) 2496) 2496) 2496) 2496) 2496



MARYLAND LINEAR FIRST DIFFERENCES CORRELATION MATRIX

VARIABLE _|DESCRIP VMTDIFF_ | VCAPDIFF LMDIFF LCAPDIFF POPDIFF PCIDIFF JOBDIFF
VMTDIFF Coefficient i 041076 0 34863 0 12496 0 45941 0 2036) 0 50973
Std Error 0} 0.0001 0.0001 00018) 0.0001 0000! 00001
N 644 621 644! 621 621 621 621
VCAPDIFF —|Coeffictent 0 41076] | 0 13367) 0 14929 -0 03188 0 14488 0 02809
Std Error 00001 0 0.0008) 0 0002) 04278 0.0003 0 4848
IN 621 621 621 621 621 621 621
LMDIFF Coefficient 0 34863 0 13367, | 053595 0 25857) -0.01495, 0 15803
Std Error 0000! 0.0008) 0} 00001 00001 07101 00001
N 644 621 644 621 621 621 621
LCAPDIFF Coefficient 0 12496 0 14929 053595 1 001447 -0 03601 0 08858
Std Error 0 0018) 0.0002) 00001 0| 07189 0 3704 00273
IN 621 621 621 621 621 621 621
POPDIFF Coefficient 045941 -0 03188 0 25857) 001447 1 0 10656) 0 56192
Std Error 0.000! 04278 00001 07189) 0 0.0079 00001
N 621 621 621 621 621 621 621
PCIDIFF Coefficient 0 2036 0 14488) -0 01495 -0 03601 0 10656) i 0 32593!
Std Error 00001 0 0003) 07101 0 3704| 0.0079] 0 00001
N 621 621 621 621 621 621 621
JOBDIFF Coefficient 050973 0.02809) 0 15803) 0.08858 0 56192) 0 32593 1
Std Error 0000! 0 4848) 00001 0 0273) 00001 00001 0}
N 621 621 621 621 621 621 621



ve

AVERAGE ANNUAL GROWTH RATES OF KEY VARIABLES

TABLE 2-2

(BY STATE AND AREA AND FIRST YEAR TO LAST YEAR OF AVAILABLE DATA)

Maryland | North Carolina] — Virginia DC Balt All
Years of Data 1969 - 1996 | 1984-1997 | 1970-1996 | 1970-1996 | 1985 - 1995
Population 1.72% 0.96% 1.32% 2.66% 1 10%
Population Density 1.72% 0.97% 1 33% 2.66% 1.11%
VMT 3.46% 3.46% 3.44% 416% 3.28%
Lane Miles 0.38% 058% 0.61% 0.87% 0.45%
Population per Lane Mile 1.34% 0.38% 071% 1.78% 0 65%
IVMT per Lane Mile 307% 2.86% 2.81% 3.26% 2.82%
Income per Capita 1.50% 174% 1.87% 176% 142%
Jobs 252% 1.74% 1.94% 2.93% 1.93%



Model- MODELL
Dependent Variable LOGVMT

= 0b1...

Source DF

Model 13

Error 1186

C Total 1299
Root MSE

Dep Mean
cv

-- NTI,

Analysis of Variance

Sum of Mean
Squares Square
1254 34514 10 21544
5 05253 0 00426
1159 39768

© 06527 R-square
13.48512 Adj R-sq
0 48401

F value

2397 910

0 9956
0.9952

iLY)

Prob>P

© 0001


3. RESULTS OF ECONOMETRIC ANALYSIS

In the econometric models tested in this analysis, base model specifications were first developed
for each study area, and then all areas together, using VMT as the dependent vanable and lane
miles, population, income per capita, and employment as the potential explanatory variables.
These are reported below, followed by results for several variations on these base models that
introduce a lag structure and considerations of traffic levels on the results. Separate regressions
have been run for five geographic areas’ Maryland, North Carolina, Virginia, the DC-Balumore
extended metropolitan area, and the full database (all three states and Washington DC). The DC-
Baltimore extended metropolitan area 1s comprised of 16 suburban counties around and between
the two cities (but does not include the ciues themselves - see Appendix C for a list of counties in
this area) The main reason for excluding the District of Columbia stself in the metro area
analysis 1s that data for DC 1s available only back to 1985; excluding it allows the analysis of the
metro area to use data extending as far back as 1970 However, DC is included in regressions

that include all areas together. These are referred to below and in the tables as “all states” runs

3.1 BASE MODEL RUN RESULTS

A summary of basic results for individual areas and all areas together 1s presented in Table 3-1.
As for all of the results presented in this section, more detailed tables with many more run
specifications are provided in Appendix A. Table 3-1 presents results for two basic
specifications, with separate results presented for each of the five study areas. The runs in
Appendix A are expanded to show additional specifications including different combinations of
variables and linear (non-logarithmic) as well as logarithmic specifications. Appendix D
contains a set of more detailed outputs for the runs shown in Table 3-1, including the intercept

coefficients for each county and year.

3-1


TABLE A-5a
TRAFFIC VOLUME RUNS (LOW / MID/ HIGH VOLUME ) - MARYLAND

ALL LOW VOLUME MID VOLUME HIGH VOLUME
[Dependent Vanable LOG(VMTI/CAP) LOG(VMT/CAP) LOG(VMT/CAP) LOG(VMT/CAP)
HIN 644 644 644 195 195 195 330 330 330 119 19 ug Traffic Volume Classifications
Adyusted R-Square og34 | 9s: | 09st | o960 | 0965 | 0961 | 0939 | 0951 | 0951 | 0955 | 0982 | 0986
INTERCEPT est 240 415 370 22h 381 462 248 430 484 231 611 7 Low Volume - VMT/LM < 2000
I t 102 330 595 519 105 360 399 189 429 572 184 105 Mid Volume - 2000< VMT/LM < S000
lLog(M_caP) [ea | x 0359 | 0366 x 0304 | 0308 x o382_| ow x 0624 | 0585 High Volume. VMT/LM > 5000
t x 141 134 x 444 447 x 823 722 x 15 121
|Log PoPy est x x x x x x x x x x x x
i To x : 7 ‘i 7 x . x x 7 ‘i
[Log (PCI_98) et | x x 0051 x x 0083 x x -0 062 x x 0589
| t x x 0736 x x 0660 x x 0484 x x “518
ALL LOW VOLUME MID VOLUME HIGH VOLUME
[Dependent Variable LOG(VMT) Loc(vT) LOG(VMT) LOG(vMT)
g 644 644 aa 624 195 195 195 195 330 330 330 330 9 19 19 nn
\dyusted R-Square 0989 0990 0995 0995 0972 0973 0976 0976 0974 0974 0983 0983 0987 0989 0992 0993
INTERCEPT est 134 983 338 319 135 125 739 755 132 n7 392 44s ISI ng 842 19
cf #8 202 777_| 462 321 228 | 640 | 487 192 24_| 391 293 233 138 399 102
|Log (LM) est x 0585 0451 045i x 0176 0135 0137 x 0263 0429 0417 x 0481 0370 0535,
t x 74) sor 800 x 195 160 159 x 168 334 318 x 376 337 499
[Log (POP) ex |x x 0659 | 0655 x x Te x x 0628 | 0638 x x 0329 | 0404
t x x 242 | 20 x x 490_ | 488 x x 120 13 K x 393 756
Log (PCI_98) est x x x 0026 x x x -0019 x x x -0 060 x x x 0557
t x x x 0369 x x x -0 154 x x x 0469 x x x Au



Model. MODELL
Dependent Variable: LOGYMT

Source

Model
Error
¢ Total

Root MSE
Dep Mean
cv

MakiuAND CUUNDLES LO KEUXESSLUN

Analysis of Variance

Sum of Mean
DF Squares Square
51 673 62470 13 20833
592 316099 © 00534
6436 76.78569

0.07307 R-square

14 16633 Adj R-aq

0.51581,

F value

2473 698

0 9953
0 9949

Prob>P

0 0001


one would expect However, the coefficient values between the various lane mile interaction
terms are not statistically different, as shown by the overlap between the 95% confidence
intervals for the lane mile coefficients In other words, although the coefficients are statistically
different from zero (based on their t-statistics, they are not statistically different from each other).
The one exception 1s the base model with congestion level interactions that has a lower

coefficient value for areas with low congestion.

Although the results are inconclusive as to whether there are any differences in elasticities due to
existing population density and/or congestion effects, they suggest that there could be significant
differences in the relauonship between lane miles and travel depending on the predominant
traffic levels and levels of congestion that are present If mid-traffic volume areas show the
highest correlation between lane mules and travel, it may suggest that in areas with modest traffic
volume, there 1s a high Jong run elasticity related to growth induced by roadway additions, while
in the higher traffic volume areas, this long run response is lower. In an effort to investigate this
possibility, EEA also estmated these “traffic volume” models using a distributed lag

specification, in order to estimate differences in short run and long run elasticity in each area

Tables 3-4 and 3-5 also show results for distributed lag models, with the long run elasticities
shown at the bottom of the column. These regressions also do not show significant differences in
the coefficients across the different data groupings, with no discernable pattern for the short-run
or Jong-run elasticity. The long run coefficients are highest for the counues with mid-traffic
volumes and mid-population density, but the coefficients are not statistically different that those

for the highest traffic volume / densest counties

These results suggest that further analysis 1s needed to identify differences in the VMT/lane mile
relationships in areas of low versus high levels of population and traffic It may be necessary to
use data that distinguishes between lane mules added onto existing capacity (such as road
widenings and restripings) and entirely new roadways or major upgrades, information which was

not available for the data used in this analysis. Such a distinction may help explain urban / rural


Virgima, Maryland, and the DC/Baluumore area Clearly, the rapid growth in travel per person in

North Carolina has not (yet) resulted in roadway usage levels on a par with the other areas.

Several distributions of the counties in each study area by key variables are presented in Table 2-
3. The categories (low, medium and high) have been set somewhat arbitrarily, based mainly on
visual inspection and identification of data “clusters” that appear to fall into different categories
As with Tables 2-1 and 2-3, several potentially important differences can be seen across the study
areas. Nearly all counties in the DC/Baltimore area have relatively high population densities,
while in North Carolina less than half the counties do, and in Virginia, somewhat surprisingly,
more than three-quarters of the counties have a low population density. North Carolina and
Virginia have more counts that are in either the mid or high categories than the low category,
and DC/Baltimore 1s dominated by counties with high population and travel per lane mile. This

appears to correlate fairly strongly with low and high income per capita ratios in each area

The information in these three tables raises several questions and helps set the stage for the
following econometric (multiple regression) analysis, and may provide insights that can be used

in interpreting the results of that analysis.

One final piece of data analysis that deserves attention before presenting the results of the
econometric analysis is the basic relauonships between the key variables. A correlation matrix
presenting correlation coefficients for the logs of the primary vanables 1n the analysis, using the
ful] database encompassing the three states and DC, 1s shown in Table 2-4. A description of the
variable names 1s provided in Appendix A, Tables A-la and A-ib. A more complete set of
correlation matrices, with log, non-log, and first difference variables for the full data base and for
each study area separately, 1s presented 1n Appendix B. Table 2-4 1s intended to provide an
example of the typical correlations between the variables in the analysis. It demonstrates that
there is a fairly strong correlation between each of the principal explanatory variables (lane miles
{LOGLM], population [LOGPOP], income (LOGPCI98], and employment [LOGJOBS]) and

2-5


MARYLAND LINEAR CORRELATION MATRIX

VARIABLE _ |DESCRIP VMT VMT_CAP- LM LM_CAP_ VMT_LM_|LMCAPURB POP POPDEN PCI_98 JOBS GAS_PR98
VMT Coefficient 1 -0 21738 091809 -0 60944! 0 94929 -0 12624) 0 9639 09563) 06158 0.96559] -0 08912)
Std Error ) 00001 00001 0.000! 0.0001 00013 0.0001 00001 0.0001 0. 0001 00293
IN 690} 644 690 644) 690! 644 644 644) 644 644 598
VMT_CAP Coefficient -0 21738 1 -0 24656) 0 56086, -0 15526 -0 23675 -0 35217 -0 34519] 01759 -0 29297 -0 12519
Std Error 0.0001 0} 0.0001 00001 0 0001 0 0001 0.0001 00001 0.0001 0.0001 0.0022
N 644 644 644 644) 644) 644 644 644 644) 644 598
LM Coefficient 0 91809] -0 24656] 1 ~0 58724 0 83425 -0 01862 092517 0 89412] 0 48571 0 88655 -001754
Std Error 0.0001 0 0001 te) 00001 0.0001 0 6373 00001 00001 0.0001 0 0001 0 6685|
N 690} 644) 690) 644) 690) 644) 644) 644) 644) 644) 598)
LM_CAP Coefficient -0 60944) 0 56086 -0 58724 1 -0 70844 -0 3865; -0 63403 -0 66935, -0 44335) -0 59559 0.05412,
Std Error 0000! 0.0001 0000! 0} 0.0001 00001 00001 00001 00001 0.0001 0 1863
IN 644 644) 644 644 644 644 644) 644) 644 644 598}
VMT_LM Coefficient 0.94929) -0 15526| 0 83425: -0 70844) 1 -0 05768) 0 896) 092885 0 74436) 0.90848) -0 13961
Std Error 0.0001 0.0001 0 000t 00001 0 0 1437 00001 00001 0.0001 00001 0.0006}
N 690 644) 690) 644 690) 644) 644) 644) 644 644 598
LMCAPURB {Coefficient -0 12624} -0 23675 -0.01862| -0 3865} -0 05768 1 -0 13938} -011521 001188 -0 15382 0.00223
Std Error 00013 0 000! 06373 00001 0 1437 0 0.0004) 0.0034! 07635 0.0001 09565
N 644 644) 644) 644) 644) 644) 644) 644) 644) 644 598)
POP Coefficient 09639 0.35217 092517 -0 63403} 0 896 -0 13938} l 097996 0 55086 0.96948) -0.03474
Std Error 00001 0 0001 00001 0.0001 00001 0 0004) 0 0.0001 0000! 00001 0 3964
N 644 644) 644) 644! 644, 644! 644; 644 644 644, 598}
POPDEN Coefficient 09563 -0 34519} 0.89412] -0 66935 0 92885! -011521 0.97996} 1 060118} 0.95309} -0. 04277
Std Error 0.0001 0 0001 00001 00001 0.0001 0.0034 0.0001 0} 0.0001 00001 0 2964]
| WASHINGTONN 644) 644) 644) 644! 644) 644) 644) 644) 644) 644) 598)
PCI_98 Coefficient 06158 01759 0.48571 -0 44335} 0 74436) 001188) 0 55086 060118) 1 063565) -0 26147
Std Error 00001 0.0001 00001 0.0001 00001 0 7635 00001 0.0001 ti) 0000! 0.0001
N 644) 644 644) 644! 644) 644 644) 644 644 644 598}
JOBS Coefficient 0 96559 ~0 29297 0 88655 -0 59559 0 90848) -0 15382, 0 96948 0 95309 0 63565) ! -0 06298
Std Error 0.0001 0 0001 00001 00001 0.0001 0.0001 0.0001 0.0001 0.0001 0| 0 1239
IN 644) 644) 644) 644] 644) 644] 644) 644) 644) 644) 598)
IGAS_PR98_—| Coefficient -0 08912} -0.12519 -0.01754 0.05412} -0 13961 0.00223 -0 03474) -0. 04277 -0 26147 -0 06298 1
Std Error 0.0293 0 0022 0 6685 0 1863 0.0006) 09565 0 3964 0.2964) 0000! 01239 ti)
N 598) 598 598) 598 598} 598} 598} 598) 598) 598) 598)



Variable DP

RUSSELL

1
ry
1
1
1
2
1
rf
1
2
1
a
a
a
1
2
2

VIRGINIA COUNTIES LOG REGRESSION

Parameter
Estimate

-0,090558
0.222263
0 195317
0.131771,
-0,003987
0.802514
9 628017
-0.359599
0.477271
-0 326918
-0,234242
0 074176
-0.828030
-0.323275
0.630649
0.295302
0.092717

Standard
error

002860697
0 02921125
0.02964736
0 02846516
0.03087708
0.03032785
0.03154692
0.05148079
0.03508099
0.02964622
0 03762155
0.03003370
0.03233187
0.02899852
003409341,
0,033535¢D
0.03019218

T for HO:
Parameters0

3.166
-7.609

6.588
74.629
0.129
16.569
19.907
+6985
13.605
-11.027
6.226

2.470
-25 611
-11.148
18.498

8.806

3.071

Prob > |TI

0.0016
9.0003
0.0001
0.0001
0 8973
0.0002
0.0002
0.0001
0 0003
0.0001
© 0001
0.0136
0.0002
0.0001
9.0001
9 0001
0.0022


Model MODEL2
Dependent Variable. LOGVMT

DC BALTIMORE SUBURBS LOG REGRESSION - WITHOUT WASH DC

Source

Medel
Error
¢ Total

Root MSE
Dep Mean

cv.

variable

INTERCEP
LOGLM
LOGPOP
LOGPCI98
ANNEARUN
ARLINGTO
BALTIMOR
CARROLL
CALVERT
CHARLES
FAIRFAX
FAUQUIER
PREDERIC
HARFORD.
HOWARD
LOUDON

MONTGOME
PRINCEGE
STAFFORD
YR_1971
YR_1972
YR_1973
YR_1974
YR_1975
YR_1976
YR_1977
YR_1978
¥R_1979
YR_1980
YR_1981
¥R_1982
YR_1983
YR_1984
yR_1985
YR_1986
¥R_1987
¥R_1988
¥R_1989
¥R_1990
¥R_1991
Y¥R_1992
¥R_1993
¥R_1994
YR_1995
YR_1996

g
a

PEPE EE ER

Analysis of Variance

Sum of Mean
DP Squares Square P Value Prob>P
44 392.62437 8 92328 1769 887 0 0001
387 1 9511S 0.00504
431 394.57552

0.07101 R-square 0 9951

14 76357 Adj R-sq 9 9945

0 48055

Parameter Estimates

Parameter Standard T for HO-
Betimate Error Parametere0
4.682672 0: 87700284 5 339
0.326651 0: 05359247 6 095
0 502314 =o 03144371 15 975
0 166996 0 08954866 2 865
© 240367 0 :07075395 3397
-0 077107 «0 05244838 -1 470
0.291970 0 :09035198 3231

-0 335439 0 03811075 -8 802

-0 452318 © 0 05080784 -8 903

-0 262036 =o: 04615272 -$.678
0 441057 0: 05884537 7495
-9 109000 = 9:05527051 -1 972
0 092552 0 06705834 1 380
-0.299937 9 04527041 -6 625
0 225294 0: 05180155 4 349
-0 364042 © 0 04253911 -a 5sa
0 214271 009044552 2 369
0 361724 = 0: 09320138 3 B81
0 257180 = 0 04487120 5.732
0 050642 =o. ©2542712 1 992
0 083213 a 02651911 3.138
0127687 —o: 02803538 4555
0 077033 «= 0 02760129 2791
0 109979 =o 02721404 4 041
0 166023 0: 02850672 5 624
0.201192 0: 02926655 6 874
0 228834 0 03114853 7347
0 188002 0: 03138637 5 990
0 200179 = 0 03157767 6 339
0.222854 0 03202289 6.607
9 209038 = -0:03240055 6.452
0 273926 «=o 03492574 7843
0 299141 0 03827017 7:17
0.356610 0 04097093 8 704
0 398190 © 0 04388844 9.073
0.439465 9 :04590447 9.573
0 468949 = 0.04789601 9 791
0 490856 0 04901023 10 015
9 515320 0.04800674 10 734
0 547422 0 :04635028 21 811
0 546353 0 00660544 31 (723
0.555932 0 04733246 11745
0.575704 © 0 04833784 31 910
0 575742 0 04928408 21,682
0 Saa93e =o 04995964 12 788

Prob > |T

0 0001
0 0001
@.0001
© 0630
@ 0008
0 1423
0.0013
0 9002
0 0001
0 0003
0 0001
0 0493
0 1683
0.0003
0 0002
© 0003
0.0183
0 0001
0 coor
9 o471
0 0018
9 0001
0 0055
0 0001
0.0001
© 0001
0 0001
© 0001
0 0002
© 0002
0.0001
0 0001
© 9001
© 0002
© 9001
0 0001
© 0001
0.0003
0 0003
0.0003
0.0002
0 0003
0 0001
© 0001
a 0001


higher aggregate levels of travel before a given level of congestion 1s reached. The effect is
shown in the figure as a shift of the time cost curve and a movement of the equilibrium point
along the demand curve from point a to point b. A reduction in time cost from point p to p’

yields an increase in travel level from point g to q’.

FIGURE 1-1
SCHEMATIC REPRESENTATION OF THE IMPACT OF
ROADWAY EXPANSION ON TRAVEL

Time
Cost
per Time cost of travel
Mile of
Travel

a Demand for highway travel

ao Miles of Travel

However, while this underlying economic relationship 1s conceptually straightforward, there are
at least two controversies surrounding its implications for roadway capacity expansion. The first
1s the specific nature of the relauonship between capacity expansion and “induced” increases in

travel (the size of the rebound). The second 1s whether the existence of this relationship indicates

1-3


ALL COUNTIES LOG REGRESSION (1NCLUDING WASHINGTON ui)

Variable DF

VAESSEX
VAFAIRFA
VAPAUQUI
VAPLOYD
VAFLUVAN

VARGANOK

VAROCKIN
VARUSSEL
VASCOTT

VASHENAN

1
1
1
1
1
1
1
2
1
1
1
1
2
1
1
2
2
1
1
1
1
1
1
1
1
1
1
1
1
1
a
1
1
1
a
1
1
1
1
L
1
a
1
rf
1
1
1
1
ry
1
1
1
1
1
1

Parameter
Estimate

0 377521
9 551726
0 335366
-0 186730
-0 192245,
O 114360
9 422036
0 043855
© 369804
© 567303
-0 487509
0 347464
O 96eRaL
-0 136475
0 527940
0 093294
+0 015391
-0 439515
0 398972
0 381979
0.082376
0 380152
0 1044is
+0 170858
-0 077331
0 125097
0 343882
+0 383749
0 267463
0 325433
0 0816
0.524410
0 279682
0 028409
1 039076
0 194682
0.036886
-0 123468
0 018677
-0 332779
-0 246340
-0 232411
-0 003632
9 059840
9 $84872
0.366918
0.199217
-0 157794
0 358019
0 433870
0 724789
0 114477
0 090892
0 062606
0 449246

Standard
Error

0 09965488
0 11334987
0 04319577
0 10240566
0 10223363
0 05289552
0 03979059
0 07496803
0,05668317
0 07474153,
0 07759361,
© 12937424
©,10144110
© 093946076
0.03759004
0 06127225
0 04249649
0 14244554
0 06377249
0 06301573
© 11434903
0.08295993
0.10270064
0 10039173
0 06722584
0 05018916
0 06289457
0 10021269
© 09307212
0.12389059
9 0997296
0 10531602
0 04295185,
0 08057428
© 08520140
0.08932228
0 10641480
9 07805880
0 07072924
0 08067532
0 07784244
0.03565122
0 09157296
0,07587030
0 06739826
0 06651747
0 05631404
0 10284073
0 11840569
0 05050896
0 06491409
0 03469948
0 05779517
0 06346386
0 04908789

T for HO
Parameter=0

a
4
7
a
“a
2
10
°
5
7
6
2
9
-2
14
1
-0
<3

6.
6.

0
4
2

1

2
2
5

3
2
2
°
4
6
°

12
2
°

1
0

4

-3

-6

-0

o.

8
5
3

“2
3
8

a
3
1
0
9

7388
867
764
823
ago
162
606
sss
546
590
283
935
581
887
048
$23
362
08s
256
9062
720
582
027
102
150
493
468
829
a74
627
168
979
$12
353
196
180
347
502
264
125
165
491
018
789
678
516
538
534
024
590
165
299
873
987
152

Prob > }T]

0 0002
© 0001
0.0001
0 0684
0 0602
a 0307
© oco1
0 s5a6
0 0001
0 0001
0 ooo1
© 0034
© 0001
© 0063
9 0001
© 1280
0 7173
© 0021
© 0001
© 0001
0 4714
© 0001
0 3034
0 0889
0 2501
© 0228
0 0001
0 0001
0 0041
0 0087
0 8653
© 0002
© 0001
0 7244
0 0001
0 0294
0 7289
© 1139
0 7918
0.0002
0 0016
0 0001
0.9858
0 4304
9 0001
© 0001
0 cous
0.1252
0.0025
9 0001
0 0001
9.0010
0.1159
0.3237
© 0001


APPENDIX A: DETAILED RUN RESULTS

This appendix coniains the following tables and ron results:

Table A-1 a-b.

Table A-2 a-c.

Table A-3 a-c.
Table A-4 a-c,
Table A-5 a-c.

Table A-6 a-c.

Table A-7

Table A-8

Description of Variables Used in the Analysis

Base Run Results. The sub-tables for Table A-2, and for the sets of Tables
A-3 through A-6 in Appendix A, are as follows.

Table A-2a. _ Base Run Results — Maryland
Table A-2b —_ Base Run Results — North Carolina
Table A-2c. Base Run Results — Virgima

First Difference Runs

Elasticity Runs (short / Jong run)

Traffic Volume Runs (Jow / mid / high volume)
Traffic Volume Elasticity Runs

Traffic Volume Runs (low / mid / high volume), Alternative Method

Traffic Volume / Elasucity Runs, Alternative Method


NORTH CAROLINA COUNTIES LOG REGRESSION (PRIMARY ROADS ONLY}

variable DP

INTERCEP
LOGLM
LOGPOP
YR_1986
YR_1987
YR_1988
YR_1989
¥R_1990
YR_1991
Y¥R_1992
¥R_1993
YR71994
¥R_1995
YR_1996
YR_1997

BLADEN
BRUNSWIC
BUNCOMBE,
BURKE
CABARRUS.
CALDWELL.
CAMDEN
CARTERET
CASWELL
CATAWBA
CHATHAM
CHEROKEE

1
1
1
1
1
1
1
1
1
1
1
a
2
1
1
1
1
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
2
cy
1
rf
1
1
1
1
1
ry
1
1
1
1
1
1
1
1
a
1

Parameter Estimates

Parameter
Estimate

4 652777
0 474994
0 559822
-0 020903
0.017598
0 075987
0.131392
0 136437
0 168549
0 206690
© 219318
0 251674
© 290803
© 324428
0 335543
-0 454282
-0 774820
-0 275994
-0 570240
-0 067627
-0 410198

-0 312929
+0 354567
0 129679
0 124432
0 001483
0 078716
-0 314681
-0 026414
0 124117

-0.588852

-0 029817

+0 233364
-0 366402

-0 526179
-0 440942
+0 173259

-0 375060

-0 177340

-0.303618
0.141083
0 358334
-0 108582
0 203306

-0 268071
0 191450

-0 492902
0 057503

+0 346201
+0 010187

-0 361967

+0 937659
0.022450

Standard
Brror

0 62191229
0 04852260
0 05227269
© 00923843
0 00927066
© 00932504
0 00939117
0 00947242
0 00962922
9 00981055
© 01004797
© 01040880
© 02071672
© 01117022
0 01148593
© 09392197
© 12955933
© 0g25ss05
0 oBss96a4
0.10786097
0 05599194
© 09037027
0 07821693
0 04737147
0 04255731
0 03138571
0 02686507
0 03522047
0 15606318
0 04589615
0.08872670
0 02733977
0 05816269
0 089991886
0 11894727
0 15245754
© 03002163
0 05431958
0 02960896
0 05699074
0 10905493
© 08563005
0 03097858
0 07485969
0 06115541
0 03658848
0 04384865
0 05390989
0 06062868
0 03623784
0 13014090
0 145575321
0.05901056

T for HO
Parameter=0

7

9
10
-2

1.

8
13
14
7
21
21
24
27
29
29
“4
5
3
-6
-0
-7
3
“4

2

2

0

2
-8

-0.

2
-6
-1

-4.

-4
-2
“Ss

~6.

“5
“5
1
4

3.

2
“4

5.

“1
1
-5
-0
+2
“6
0

803
788
710
263
age
149
991
404
504
068
827
179
136
046

+233
837

980
343
662
627
326
463
533
737
924
047
930
935,
169
104
637
092
012
075
424
892
m
905
989
327
294
185
505,
716
383
233
241
067
710
281
781
441
380

Prob > [T

© 0001
0 9002
0 aoo1
0 0238
0 0579
0 0001
0 0001
0.0002
0 9001
0.0002
Q 0001
0 0003
0 aoo1
0 0001
0 0002
0 0002
0 0002
0 0009
0 0003
0 5308
0 0001
0 0006
0 9003
0 0063
0.0035
0.9623
0 0035
0 0001
0 8656
0.0069
0 0003
0 2757
0.0001
0 0001
0 0001
0.0039
0 0003
9 0002
0.0002
0 0001
0 1960
0 0002
0.0005,
0.0067
0 0001
0 0002
0 0001
0 2863
0 0002
0.7787
0.0055
0 0001
0.7037


TABLE A-2a

BASE RUN RESULTS - MARYLAND

Dependent Variable VMTICAP LOG(VMTICAP)
lLogs of Independent Variables? no no no no nO n0 yes yes "yes yes. yes yes yes yes
IN 644 644 644 644 644 644 644 644 644 644 644 644 644 644
lAdjusted R-Square o931_ | 0932_[ 0936 | 0932 | 0938 | 0938 | 0934 | 0951 | 0951 | 0951 | 0951 | 0953 | 0953 | 0953
INTERCEPT est | 102 | 871 932 { 947 | 599 [ 720 | 240 | 415 [ 338 | 370 | 319 | 487 314 315
uc] 206 24 136 [ 880 | 531 604 102 x30 [| 777 | 595 | 462 660 133 616
ILM_CAP. est x 190 236 177 313 242 x 0359 | 0451 | 0366 | 0451 | 0333 | 0466 | 0463
L x 305 391 280 494 361 x la. Bol 134 800 565 143 142
POP. est x x -| 98E-05| x -2 78E-05| -4 14E-05} x x Oil x 0 106. -0 224 x x
t x x 661 x -758 [| -706 x x 184 x 173 | -265 x x
PCI_98 est x x x _|-535E-05| 2 51E-04|243E-04] x x x 0051 | 0026 | -0092 x -0078
t x x x 0932 [370 | 359 x x x 0736 | 0369 | -127 x -109
OBS est x x x x x 1 49E-05 x x x x x 0247 0152 0164
t x x x x x 296 x x x x x 556 S10 | 516
Dependent Variable YMT LOG(VMT)
Logs of Independent Variables? 0 no nO. no no 0 yes yes yes yes yes yes yes yes
IN 644 644 644 644 644 644 644 644 644 644 644 644 644 644
[Adjusted R-Square 0931 | 0964 | 0977 | 0967 | 0977 | 0991 | 0989 | 0990 | 0995 | o991 | 0995 | 0995 | 0995 | 0995
INTERCEPT est $8428 | -7159437| -5107833 | -8401708 | -475 1878) -1791609 134 983 338 379 319 487 651 725
t | 0250 | -202 [ -169 | -22t | -124 | -670 | 448 202 | 777 | 408 | 462 6.60 167 | 980
LM est x 15874 | 9347 | 14992 | 9214 | 4310 x oses | 0451 | 0558 | o4s1 | 0333 | o211 | 0203
t x 232 145 222 141 953 x 74l 801 738_| 800 | 565 346 332
POP. est x x 186 x 195 | -263 x x 0659 x 0655 | 0443, x x
t x x 187 x 166 | -244 x x 242 x 220 | 924 x x
PCI_98 est x x x 136 | -276 | -168 x x x 0653 | 0026 | -0092 x -0.091
t x x x 724 -150 -141 x x x 751 0 369 -127 x -118
jOBS est x x x x x 270 x x x x x 0247 [ 0557 | 0573
li t x x x x x 289 x x x x x 356 | 221 199