Transportation
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TRANSPORTATION PLANNING & ENGINEERING, INC.
VICTOR H BISHOP P E. President
O..uVIO H. ENGER. P E. VIce President
2101 112th AVENUE N E. SUITE 110 - BELLEVUE, WASHINGTON 98004
TELEPHONE (425) 455-5320
FACSIMILE (425) 453.7180
December 17, 1998
RI=t;r=IVED
Mr Richard Buchco
RITE AID CORPORATION
14880 N E 24th Street
Redmond, WA 98052
DEe 18 1998
MULVr-., ,;. I I ,.,.1"1 11'Il::h~HIt-'
BELLEVUE
RE Yelm Rite Aid Drugstore
Building Size
Dear Mr Buchco
Our Yelm Rite Aid Druqstore Traffic Impact Analvsis dated August 19, 1998 evaluated
the traffic Impact of a 16,750 sq ft. drugstore facility Subsequent to this analysis we
understand that the building size has been scaled back to 13,800 square feet.
Table 2R shows the revised trip generation associated with a 13,800 sq ft. drugstore
building The resultant trip generation is less than originally estimated in
Yelm Rite Aid Druqstore Traffic Impact Analvsis The only material affect of this change is the
calculated City of Yelm traffic mitigation fee which is based on net new PM peak hcur trips The
revised traffic fee based on the new building size is calculated as follows
. 68 PM peak hour trips x $750/PM peak hour trip = $51,000
The impact of the reduction in site generated traffic volume to the intersection level of
service at the analysis intersections would be negligible
Very truly yours,
1
TRANSPORTATION PLANNING
& ENGINEERING, INC
12fr/<:;P
Mark J Jacobs, P E
Associate
M,..IJ sv
cc' Joel Wilbur, Mulvanny Partnership
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TABLE 2 R
YELM RITE AID DRUGSTORE
TRAFFIC IMPACT ANALYSIS
TRIP GENERATION
TIME PERIOD TRIP RA TE TRIPS TRIPS DRIVEWA Y PASS BY STREET
ENTERING EXITING TOTAL TRIPS % TOTAL
Pharmacy/Dru~ store (ITE Land Use 881, 13,800 SQ. ft.)
Average T = 88 16X 608 (50%) 608 (50%) 1,216 50% 608
Weekday
AM Peak Hour T = 2 66X 21 (57%) 16 (43%) 37 50% 19
PM Peak Hour T = 10 40X 71 (49% 73 (51%) 144 50% 721
II Single Family Detached Housing (ITE 210, 2 housinq units) I On g~ ~l ~ ~}(,.-u d- S1/ '-
.... I
Average T = 9 57X -- -- <19> 0% <19>
Weekday
AM Peak Hour T = 0 75X -- -- <2> 0% I <2>
PM Peak Hour T = 1 01X -- -- <2> 0% ~
I Church (ITE 560, 3,440 sq ft.) I
Average T = 9 11X -- -- <31> 0% <31>
Weekday
AM Peak Hour T = 0 72X -- -- <2> 0% I <2>
PM Peak Hour T = 0 66X -- -- <2> 0% <2>
I Net New I
Average -- -- -- 1,166 -- 558
Weekday
AM Peak Hour -- -- -- 33 -- 15
PM Peak Hour -- -- -- 140 -- /6a'"
T = Trips
X = 1,000 sq ft., housing units
A vehicle trip is defined as a single or one direction vehicle movement with either the origin or
destination (exiting or entering) inside the study site These trip generation values account for all site trips
made by all vehicles for all purposes, including commuter, visitor, recreation, and service and delivery
vehicle trips
Used for LOS analysis
Used to calculate traffic mitigation fee
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677 Woodland Square Lp SE
Lacey, WA 98503
r.o Box 3485
Lacey, WA 98509-3485
(360) 493-6002
(888) 493-6002 Toll Free
(360) 493-2476 Fax
July 2, 1998
Cathie Carlson
City Planner
City ofYelm
PO Box 479
Yelm, WA 98597
RE Rite-Aid Drug Store - Scoping Letter Review Comments
SCA #97006~005
Dear Cathie
,
I have completed my review of the proposed Rite-Aid Drug Store trip generation and distribution
letter prepared by Transportation Planning and Engineering, Inc. The methodology used to
estimate the trip generation for the proposed project' is acceptable as presented in the scoping
letter report. The site distribution component is based on the Yelm traffic model distribution that
was prepared by our office
Based on the traffic scoping analysis, this project is expected to generate 87 new pm peak hour
trips Because of the high level of traffic expectation from this site, we are recommending that a
formal Traffic;: Impact Analysis (TIA) be prepared to address traffic impacts and appropriate
mitigation measure for this development. The project scope should include,at a minimum, an
analysis of the following primary intersections
1 Yelm AvenueNancil Road (Realignment Design Alternative only)
2 Yelm Avenue/Bald Hills Road
3 Yelm Avenue/First Street
4 Yelm Avenue/Plaza Drive
5 Yelm Avenue/103rd Avenue
6 Yelm Avenue/Solberg Street
7 Yelm Avenue/Site Driveway Entrance
8 Vancil Road/Site Driveway Entrance
The analysis should include project traffic expected from the proposed Safeway deyelopment
located easterly of the site and a 4% background traffic growth rate The site driveway analyses
should focus on the type of traffic treatment required to facilitate safe ing!essand egress
movE;lments to the projec~. Of particular concern is the Yelm Avenue entrance that is located in
close proximity to the Vancil Road intersection A "queue" analysis will be required for
eastbound traffic flows on Yelm Avenue to evaluate whether left turn ingress and egress will be
/
C .I V I L
TRANSPORTATION
\
PLANNING
SURVEYING
Cathie Carlson
July 2, 1998
Page 2
allowed at the Yelm Avenue Site entrance In absence of information to support left turns this
site driveway must be restricted to right in, right out movements
This completes our review of the traffic scoping letter provided by the proponent's traffic
engineer If you have any questions or need clarifications, please give me a call at "(360) 493-
6002. Thank you
Sincerely,
SeA Engineering ~
~~~E~ .
prin~1 SI
PAS/ct
(f.\text\corres\Jul-98\70060701.ltr)
C I V I L
TRANSPORTATION
PLANNING
SURVEYING
II1II
~'K'~;
ENGINEERING
1rl
TRANSPORTATION PLANNING
& ENGINEERING, INC.S"
2101 112th Avenue N.E.. Suite 1 fo R E (; f= I V E 0 I
BELLEVUE. WASHINGTON 98004
[1~uu~w @[? uW&Im~UYi]Duu&[1
(425) 455-5320
FAX (425) 453-7180
UUN 0 1 1998
JOB NO.
TO
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MULVANNY PAK II'Il:HSHI
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> WE ARE SENDING YOU ~ttached 0 Under separate cover via
o Shop drawings 0 Prints 0 Plans
the following items.
o Samples
o Specifications
o Copy of letter
o Change order
o
COPIES DATE NO DESCRIPTION
:l ~/2?-/9a - r,r I<> 6 ('.... ~-..A. _ v -... ? J /J,J/y. j ~h~- / ~~
I
-
THESE ARE TRANSMITTED as checked below'
>
o For approval
A For your use
. ~ requested
o For review and comment
o Approved as submitted
o Resubmit
copies for approval
o Approved as noted
o Submit
copies f.or distribution
o Returned for corrections
[] Return
corrected prints
o
o FOR BIDS DUE
19
o PRINTS RETURNED AFTER LOAN TO US
REMARKS
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YELM RITE AID DRUGSTORE PROJECT
TRIP GENERATION AND DISTRIBUTION LETTER
Prepared for
Ms Cathy Carlson
CITY OF YELM
POBox 479
Yelm, WA 98597
2101 - 112th Avenue N E , Suite 110
Bellevue, Washington 98004
(425) 455-5320
1 ~
, .
YELM RITE AID DRUGSTORE PROJECT
TRIP GENERATION AND DISTRIBUTION LETTER
Prepared for
Ms Cathy Carlson
CITY OF YELM
P. O. Box 479
Yelm, WA 98597
Prepared by
TRANSPORTATION PLANNING & ENGINEERING, INC.
2101 -112th Ave. N E, Suite 110
Bellevue, Washington 98004
Telephone - (425) 455-5320
FAX - (425) 453-7180
May 27,1998
-f~j
S/77-/9,f
I EXPIRES 4/3/ 00
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TRANSPORTATION PLANNING & ENGINEERING, INC.
vie Ton It l3ISHOP P [ P'C~ldelil
DAVID H ENGER P.E. Vice P,esldenl
2101 1121h AVENUE NE SUITE 110 - BELLEVUE. WASHINGTON 96004
TELEPHONE (425) 455.5320
FACSIMILE (425) 453.7180
May 27,1998
Ms Cathy Carlson
CITY OF YELM
POBox 479
Yelm, WA 98597
RE Yelm Rite Aid Drugstore Project
Trip Generation and Distnbution Letter
Dear Ms Carlson
Per the request of Mr Richard Buchco of Rite Aid Corporation, we are pleased to
submit this trip generation and distribution letter for the proposed Yelm Rite Aid
Drugstore project. This project is located in the s~+corner of the Vancil Rd./SR
507 intersection in the City of Yelm
This letter has been prepared to identify the project trip generation and
dlstnbution This information IS to facilitate the determination of the appropriate traffic
analysis requirement for the proposed project, if any
PROJECT DESCRIPTION
Figure 1 is a vicinity map showing the location of the site and the surrounding
street system
Figure 2 shows a preliminary site plan prepared by Mulvanny Partnership
received by TP&E, Inc on April 24, 1998 The plan consists of a 16,750 square foot
drugstore building with drive-up service and 84 parking stalls including four handicap
stalls Access to the project is proposed via two dnveways One of the driveways is
onto Vancil Rd and the other IS on Yelm Ave East (SR 507) The plan also dedicates
substantial right of way (ROW) to facilitate the City's plan to improve the Vancil Road
alignment at Yelm Ave East.
Full development of the 16,750 sq f1. Yelm Rite Aid Drugstore project is
expected to occur later this year
c'\-Projecls\ Y070298\ Y07029~rpl. .doc
, .
1r~
Ms Cathy Carlson
CITY OF YELM
May 27, 1998
Page 2
TRIP GENERA TlON AND DISTRIBUTION
A vehicle trip IS defined as a single or one direction vehicle movement with either
the origin or destination (exiting or entering) inside the study site
Table 1 shows the vehicular tripS during an average weekday and during the AM
and PM street traffic peak hours for the proposed 16,750 sq ft. Rite Aid Drugstore
project. The trip generation is calculated using the average trip rates in the Institute of
Transportation Engineers (ITE) Trip Generation, Sixth Edition, 1997 for
Pharmacy/Drugstore with Drive-through Window (ITE Land Use Code 881) These trip
generation values account for all site trips made by all vehicles for all purposes,
Including commuter, visitor, recreation, and service and delivery vehicle trips
A pass-by trip is an existing trip that comes directly from the traffic flow on a road
adjacent to the project site and does not require a diversion for another roadway
According to Table VII-1 of Trip Generation, Fifth Edition, 1991, a pass-by rate for
discount store (K-Mart #14 on list) is identified at 50% In addition, an article titled
Refinement of Procedures Used for Estimating Pass-by Trip Percentages by Massoum
Moussavi and Michael Gorman published by the ITE Journal, May, 1992, shows pass-
by percentages averaging 50% for five discount stores Rite Aid stores are discount
oriented and are expected to have similar trip making characteristics to discount stores
No reduction for diverted linked trips nor existing trips, due to the existing development,
were taken Therefore, our analysis is conservative
Trip Distribution
Figure 3 shows the estimated trip distribution and the calculated site-generated
traffic volumes Trip distributions to and from the site are based on T-Model output
provided by the City of Yelm
TRAFFIC MITlGA TlON
The City of Yelm Concurrency Ordinance 580 requires payment of a
transportation facility charge of $750 per new PM peak hour trips According to our trip
generation analYSIS the project is calculated to generate 87 new PM peak hour trips
Thus a traffic fee of $65,250 (87 trips x $750/trip) is calculated Credits against this fee
may be available for contribution to transportation faCIlities on the approved plan or
provide a public benefit
C:\-Projecls\ Y070298\ Y07029Krpl. doc
1r~
.,
Ms Cathy Carlson
CITY OF YELM
May 27, 1998
Page 3
The subject site is located in the southwest corner of the Vancil Road/SR 507
mtersection The Vancil Rd alignment requires improvement. Improving the alignment
requires the subject development to dedicate substantial right of way at the intersection
Providing ROW for future realignment of Vancil Rd is a public benefit therefore credits
against the transportation facility charge would appear appropriate
SUMMARY AND CONCLUSIONS
This letter has provided a project descnption, trip generation and distribution,
calculated the transportation facility charge and discussed the need to provide ROW to
allow realignment of Vancil Rd , by others.. in the future for the proposed Rite Aid
Drugstore project.
We request that the City of Yelm review this letter and schedule a scoping
meeting as soon as possible If you have any questions, please call me
Very truly yours,
MJJ es
TRANSPORTATION PLANNING
& ENGINEERING, INC
~ J c:l ~ d -
/ ~\.... 0 .~~t-v')
Mark J Jacot> , P E
Senior Transportation Engineer
cc Richard Buchco. Rite Aid Corporation
Wayne Miller, Mulvanny Partnership
Allen Kann, The Baldridge Group
C'\-Projeclsl Y0702981 YO 70297.rpl.. doc
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TABLE 1
YELM RITE AID DRUGSTORE
TRIP GENERATION AND DISTRIBUTION LETTER
TRIP GENERATION
TIME PERIOD TRIP TRIPS TRIPS DRIVEWA Y PASS BY STREET
RATE ENTERING EXITING TOTAL TRIPS % TOTAL
Pharmacy/Drugstore (ITE Land Use 881, 16,750 sq ft.)
Average Weekday T = 88 16X 738 (50%) 738 (50%) 1,4 76 50% 738
AM Peak Hour T = 2 66X 26 (57%) 19 (43%) 45 50% 23
PM Peak Hour T= 1040X 85 (49%) 89 (51 %) 174 50% 87
T = Trips
X = 1,000 sq ft.
A vehicle trip is defined as a single or one direction vehicle movement with either
the origin or destination (exiting or entering) inside the study site These trip generation
values account for all site trips made by all vehicles for all purposes, including
commuter, visitor, recreation, and service and delivery vehicle trips
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LEGEND
xx% Trip Distribution Percentage
YELM RITE AID DRUGSTORE TRAFFIC SCOPING LETTER
PROJECT GENERATED TRAFFIC VOLUMES & DISTRIBUTION
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Table VII-1
Summary of Weekday Pass-By Trips and Diverted Linked Trips-
Shopping Centers
Size Pass-
( 1000 Wee!a:L1y No. of Primary Non-Pass- Diverted By
Sq. Feet Survey Inter- Tune Trip By Linked Trip Trip
1 Name of Develooment GLA) Location Date l'iews Period (%) (%/ (%) (%) ADT Source
1 N. Hills Village Mall 430 Ross P A 6/25/80 178 12-9 P.M 76 24 24,960 Aayroord Keyes
2. Shoppers World Framingham MA 12/19/82 92 3:30 -7 39 38 Assoc. (KM)
720 23 73,628 Aayroord Keyes
Plaza PM Assoc. (KM)
3. Christiana Mall 890 Newar1< DE 7/20/84 179 3-8 P.M. 49 39 12 Aayroord Keyes
4. Manassas Mall Manassas VA 6/8/84 87 4-6 P.M. 25 27 Assoc. (KM)
402 48 Aayroord Keyes
5. NOflhway Mall 6/25/80 175 5:30-7 P.M. 54 Assoc. (KM)
462 Ross P A 36 27,200 Aayroord Keyes
6. Huntington Square Hunlinglon U NY 11/22/85 181 47 P.M. 21 33 Assoc. (KM)
234 46 34,630 ~~
Mal
7 West Bell Mall 658 WayM NJ 9/14/84 243 3.6 P.M. 61 12 27 85.600 Aayroord ~
8. Conlidenlial 1980 364 4- 6 P.M. 35 Assoc. (KM)
1200 Washington DC 40 25 Gorove-5lade
9. Confidential 800 Soulh N/A 1000 P.M. Peak 45 43 12 Frischer
CA Period
10. Conlidential 451 POrlland OR N/A N/A 5-6 P.M. i5 25 Buttke
11 Confidential 113 POrlland OR N/A N/A 5-6 P.M. 83 17 Buttke
i 12. Map(ewood Man 622 P.3sm:y MN 11/1/85 46 4-9 P.M. 26 30 44 36,370 Aayroord Keyes
I ~ 1 Assoc. (KM)
if 13. University Mall 736 Pensacda 10/25/85 383 3-7 P.M. 35 39 Ra'rmnl Keyes
: t FL Assoc_
'i: ""> 14. K-Marl 84 OoverOE 7/19/85 218 3:30-7 P.M. 6 44 Ra'rmnl Keyes
I Assoc. (KM)
I 15. Meriden Square 500 Meriden 4/10/85 N/A 4-6 P.M. 92 Connecticut 001
I Mal CT
I 16. Enfield Square 660 Enfield CT 4/17185 N/A 4-6 P.M. 78 22 Connecticut DOT
Mal
17 Crystal Mall 845 Waterford' CT 4/17185 N/A 4-6 P.M. 86 14 Connecticut 001
18. Westfarms Mall 1,060 West Hartford CT 4/24/85 N/A 4-6 P.M. 83 17 Connecticut DO 1
19. Cipriano Square 13t Pro Ge<xgeS Co. MD 1982183 88 4-6 P.M. 11 89 JHK (SS)
20. Ointon Pari< 181 Pro Georges Co. MD 1982183 105 4-6 P.M. 64 36 JHK (SS)
21 Eastgate 100 Pro Georges Co. MD 1982183 93 4-6 P.M_ 64 36 JHK (55)
22. Forest Valage Mall 475 Pro Georges Co. MD 1982183 130 4 -0 P.M. 80 20 JHK (S5)
23. Fl Washington 60 Pro G8Ol98S Co. MD 1982/83 72 4-6 P.M. \8 82 JHK (55)
24. Kettering Plaza 90 Pro Georges Co. MD 1982183 91 4-6 P.M. 42 58 JHK (SS)
25. Marlboro Square 78 Pro Georg!S Co. MD 1982183 113 4-6 P.M. 41 59 JHK (SS)
26. Riverdale 44 Pro Georges Co. MD 1982183 97 4-6 P.M. 49 51 JHK (55)
Shopping Center
27 White Oak 467 Pr Georges Co. MD 1982183 99 4-6 P.M_ 44 56 JHK (SS)
Shopping Center
28. Essex Green 352 W Orange NJ 3/6/86 149 4-6 P.M. 19 43 38 21,520 Aaym:rd Keyes
Shopping Center Assoc. (KM)
29. Tarpon Square 176 Tarpon Spr FL 5/16/86 124 J.7 P.M. 28 35 37 34,080 ~(~
30. Uberty Tree Mall 850 Danvers MA 7117/89 336 12-2 P.M. 40 15 45 ABEND
31 Chestnut Hill Mall 275 Newton MA 7/3 and 789 lIAM.- 43 20 37 ABEND
7/5/79 4 P.M.
I 32. Mall of New 525 Manchester NH 711 0/79 488 11 A.M. 37 13 50 ABEND
Hampshire 4 P.M.
33. South Shore Plaza 1,200 Braintree MA 7/13/79 281 11 A.M.- n 10 58 ABEND
3P.M_
34. Orlando Fashion 762 Orlando Fl Fall 1985 182 4-6 P.M. 52 23 25 Kimley Horn
Square
35. Markel Place 166 Orlando FL Fall 1985 124 4-6 P.M. 48 25 27 Kimley Horn
I 36. Westgate 129 Orlando FL Fafl1985 116 4-6 P.M_ 50 22 28 Kimley Horn
Shopping Cenler
37 Plnar Shopping 71 Orlando FL Fall 1985 81 HP.M. 44 6 50 Klmley Horn
Cenler
38. Crossgales Mall 921 Albany NY 7/31 and 196 4-6 P.M. 42 35 23 60,950 ~(~
8/1/1985
i 39. Shannon Valley 108 Overlarid Park KS 7/6 and 111 4'30- 61 13 26 34,000 N/A
Shopping Cenler 717/88 5:30 P.M.
II 40. Cherokee 118 Overland Park KS B/l1/B8 t23 4:30- 55 20 25 N/A
" Shopping Center 5:30 P.M.
l
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; 1-24 [nstirute of Transportation Engmeer
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Refinement of Procedures
Used for Estimating Pass-~y
Trip Percentages
BY MASSOUM MOUSSAVI AND MICHAEL GORMAN
P ass-by mps are defined as mos at-
tract~d to a particular development
from the traffic "passing by" on the ad-
jace:H street. Being able to predict and
a~ply pass-by trip percentages as pare of
a traffic Impact analYSIS allows engIneers
to more accurately estimate the traffic
Impacts of a new development on the
surrounding street system
Intuitively, It seems reasonable that
the hIgher the adjacent street average
daily traffic (ADT) and level of service
(LOS). the higher the pass-by percent-
age. It also follows that there would be
a hIgher pass-by percentage 10 an office
park setting than 10 a reSIdential one.
While we can IOmltlvely agree these re-
lalionshlps exist, there currently are no
generally accepted gUldehnes for estI-
mating pass-by tflp percentages. This
S1tuanon causes problems when a devel-
oper and the reviewing agency disagree
on appropriate pass-by mp percentages.
It IS possible, panicularly with larger de-
velo~ments. that the difference in pass-
by tnp percentages used could result in
thousands of dollars In Improvements
being required of the developer. With
thIS much at stake, it is imperative that
we have better procedures for estimating
pass-by trip percentages.
Purpose
The purpose of this study was to develop
a senes of regression equations that can
be used for predicting the percentage of
pass-by tnps for grocery slore. discount
smre and fast-food restaurant deve!op-
ments In Omaha Nebraska.
Study Sites
'\ total of 17 sites were Included 10 this
study S~ven involved large grocery
stores either standing alone or 10 con-
JunctIOn with other large or small shops.
Five sites IOvolved discount scores. whIch
agalO were eIther standing alone or part
or a community-sized shoppmg center.
Five sites involved fast-food restaurantS.
These sites were selected because they
represent a good cross sectIOn of store
Size, gross leasable area (GLA) adja-
cent street ADT, land use. and adjacent
street volume-to-<:apaarv (vie) ratio In
general the follOWIng characteristics
were studied for e:::!ch site:
Percentage of pass-by mps
Percentage of diverted tnps
Percentage of new tnps
. Store GLA.
Major adjacent street ADT
Minor adjacent street ADT
Total adjacent street ADT,
vie ratio of adjacent inter.>ectlons,
, vie ratio of adjacent srreets.
Percentage of commercial land use
Wlthin I-mile radius of SIte,
. Percentage of residential land use
within I-mile radius of site. and
. Percentage of office land use within
I-mile radius of site.
Interviews
A mmlmum of 57 inrerviC'Ns were con-
ducted at e:Jch site. It was de~ermIned by
mal and error thac thiS IS the ma:<Imum
number of Interviews that c:Jn be con-
venIently obtained bv one person work-
mg two hours without stoppIng Ob-
viously more interviews would have
provided more accuracy, but the mtent
of thiS study and the reliabil1ty of the
data. that IS, tnp generation rates, did
not justify the additional effort required.
The actual Interview quesaons were as
follows'
Where did your trip begm
arriving here? (a) home
work (c) another store
other
.., Will you go direccly home from here"
(a) yes (b) no
J Did you change your normal drivmg
route to come here? (a) yes (b)
no
J. If yes. approximately how far out of
the way did you travel? (a) 0 5
mile (b) 0.5 to 1 mile (c) I
mile
5 If no, please describe angm and des-
tmanon.
pnor to
(b)
(d)
If the answer to Quesaon I was work.
another store, or other and to Question
3 was no, the trip was considered a pass-
by trip If the answer to Question I was
home and to Question 2 was yes, the tnp
was conSidered to be new. All other com-
bmatlons of answers were conSidered to
identify diverted-linked trips.. This was
probabiy a conservative approach, but it
was decided that the only true pass-by
ITE JOURNAL MAY 1992 13
tnps during the time period surveved (4
P m_ to 6 p.m ) were from work or an-
ocher origin without changing normal
routes All other tnps probably involved
a primary purpose or multiple destina-
tIOns The cnteria used for new tnps
were also conservative It was decided
that any origm other than home would
probably not me:m that che trIp was pn-
mary Tnere were a few people who In-
dicated m Question 5 that chelr ongm
and destination was work. In those In-
stances, the tnp was considered pnmarv.
The deCISion tree used to IdentIfv new
diverted, and pass-by tnps IS illustrated
m Figure 1.
The time penod of 4-6 p m_ was cho-
sen because It represents the most cnu-
cal traffic penod for adjacent street traf-
fic. It IS also the onlv time other than
Saturdays that retail establishments pre-
sent a pocentIal traffic problem Opemng
tImes for grocery stores and discount
scores are usually after the momlOg rush
hour, and comparatIvely little business IS
conducted dunng the weekday lunch
hour This tIme period is also commonlv
used for traffic Impact analvsls The re-
sults of che interviews are shown In
Table 1.
Data Analysis
Regression analysis was used to develop
a series of equations that can be used to
predict pass-by tnp percentages_ The de-
pendent vanable (Y) in each equation
'.vas the pass-by trip percentage_ The 10-
depe:1dent variables studied for each
type of retail development are as follows.
z Percentage of residentIal land
use within I-mile radius,
Percentage of office land use
Wlthm I-mile radius,
x) = Percentage of commerClal land
use wlthm I-mile radius
_, = Total adjacent street A..DT,
Total adjacent street vie ratio
Major adjacent street ADT
Major adjacent street Vie, and
Gross leasable square footage
(GLSF)
x.
.~
z,
..,
....,
The GLSF variable was not used 10 the
fast-food restauranc analysis because the
Size or each store was almost Identlc::!.1
Linear Regression
lmually, multlvanable ltnear regressIOn
models were conSidered for testing the
relacionshlp berween pass-by trips and
mdependent vanables_ The Statistic:!1
Analysis System (SAS) program' was
used throughout thiS study to determme
regression coefficlents_
The [mear regressIOn equation that
was developed for predicting the pass-bv
trIp percentages for fast-food restaurants
IS as follows
Y = 34 0 +- 0.39x 1
.;.. 04Ld .;.. 2_69x~, (1)
CUotsuon No. ,
TriO Type
Figure L Decision tree for identifying trip characteristics.
~
where
Y = Pass-by tnp percentage,
XI = Percentage of residential land
use withm I-mile radius,
x) = Percentage of commercial land
use within I-mile radius, and
x, = Total adjacent street vie ratio
The R2 value showed a very high rela-
tionship (0 997) berween pass-by trip
percentages and residential land use.
commerClal land use, and adjacent street
vie ratio Examming the results closer, ic
was apparent that the highest relation-
shIp eXIsts between the percentage of
reslde:1oal land use and pass-by tnps.
The Illgher the percentage withm a I.
mile r.ldius, the higher the percentage of
pass-by trips. The same 15 true of com-
mer'Cial land use The results also mdi-
c::!.ted chat to a lesser degree pass-bv
tnps are pOSItIvely innuenced by the
level of congestIon (vie) ratio It would
appe:J.r to make sense that such relation-
ships would eXIst. People tend to do most
of theIr purchasing close to home and
attempt to get out of the work envIron-
ment before le:J.vmg the street system It
IS also logIcal that pass-by trips would be
affected by congestlon_ The higher the
traffic volume, the Illgher one would ex-
p~ct congestion levels. An unsettling as-
pect or the linear regressIOn analYSIS
however, IS the fact that the equation In-
dicates that with a zero vie rario (no tr.lr-
fic), the Intercept would still show ex.
pected pass-by tnpS_
The IIne:J.r regression equatIOn chat
was developed for predicnng the pass-bv
mp percentages for grocery stores IS as
follows:
Y=644
.;... 0 11 x,-l 07x,-1l 4&,-0 71-r,
.;... 0 1&, (2)
where
Y = Pass-by tnp percentage,
XI = Percentage of reSIdential land
use Within I-mile radius,
X, = Percentage of commercial land
use within i-mile radius,
x, = Total adjacent street vie,
x. '" Major adjacent street ADT, and
x. = GLSF (lOOOs)
The R2 value showed a very high rela-
tionship (0 996) between pass-by tnp
percentages and major street ADT. per-
centage of commercia! land use. adJa-
cent street vie. GLSF, and percentage of
14 IrE JOURNAl MAY 1992
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-,.
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residential land use ExaminIng the
equation more c1oselv, it is apparent that
major street ADT, percentage or com-
mercial land use, and total adjacent
street vie have the most impact on pass-
by trip percentages. The other factors,
GLSF and percentage of residential land
use, had a mInimal effect and only Im-
proved the R2 values irom 0 928 to
I) 996. The coeffiCient for major street
ADT is negative, which means that as
ADT increases, the percentage of pass-
by trips decreases. This does not make
sense and is contrary to the findings of
other researchers. 2
The linear regressIOn equation that
was developed for predicting pass-by cnp
percentages for discount stores is as fol-
lows:
Y = 58.70 + 0 60 x~-O.8Ix)
-+ 65x1 (3)
where
Y = Pass-by trip percentages.
z~ = Percentage of office land use
wlthm I-mile radius.
X, = Percentage of commercial land
use withm I-mile radius
X1 = Total adjacent street vIe.
The R' value showed a very high rela-
uonshlp (0 999) between pass-by trIp
percentages and adjacent street vie, per-
centage of commerCial land use, and
percentage of office land use In thiS
model, it is suggested that pass-by trips
to discount stores incre:lSe with the per-
centage of office space wuhm a I-mile
radius and IS Inversely related to per-
cenrages of commetc1alland use and the
vie of the adjacent streets. While these
relallonships are lOgIcal. there is still the
problem With the Intercept yielding a
pass-by percentage even when the vlc IS
zero
logarithmic Regression
Bec:Iuse of the shortcomings of the lin-
ear equations, it was deCIded to test the
relationship between the pass-by trip
percentages and independent variables
as loganthmlc models. These models ap-
peared to yield better results.
The logarithmic regression equation
that was developed for predicting pass-
by trip percentages for fast-food r::stau-
rams is as follows:
Y = 1696 X x,"..1<O
x x, u.o" X X.;'oU41
(4)
fTE JOURNAL MAY 1992 . 15
where
Y = Pass-by trip percentages,
-~t = Percentage of residential land
use within I-mile radius.
.~. = Percentage of commercial land
- use within I-mile radius, and
.~~ = Total adjacent street vie.
According to the analysis, fast-food res-
taurant pass-by trIp percentages are in-
fluenced by the percentage of residential
land use, the percentage of commerCIal
land use, and tbe tota! adjacent street
'lIe. Usmg tbese variables, an R' of 0.999
is achieved. The other five variables are
not significant at the 0.150 level. The re-
lationship between all variables is posi-
nve, indicating that increases in anyone
of the factors would tend to mcre:J.se
pass-by trip percentages.
The loganchmlc regression equation
[hat was developed for predictIng pass-
bv <np percentages for grocery stores IS
as follows.
Y = 91 84 x
where
"'" -O.uJI
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(j)
04227 The relationship is negative,
which would indicate that grocery stores
would have a lower pass-by trip percent-
age in highly commercialized areas. It
could be explained that highly commer-
cial areas generate multiple or diverted
trips, thereby reducing the pass-by tnp
percentages. It is troubling that neither
adjacent street ADT nor vie appeared to
have a statistically significant affect on
trip characteristics.
Tne logarithnuc regression equation
that was developed for predicting pass-
bv trip percentages for discount stores is
as ioHows'
Y = 48 42 x '::1 -0.15) (6)
where
(5)
Y = Pass-by trIp percentage. and
-=1 = Total adjacent street vie.
Discount store pass-by nip percentages
are gre:J.uy mfluenced by the total adJa-
cent street vie. Using this one variable
alone, an R1 of 0.998 was achieved. No
other independent vanable was Signifi-
cant at the 0.150 level. The relationship
is negative, indicating that the higher the
congestion level, the less likely people
are to stop at a discount store during the
~vening peak penod. It is interesting that
no relationship was found between pass-
by trips and GLSF or adjacent street
ADT as had been ::'''Cpecred. The rela-
nonship between pass-by tnp percent-
ages and vie appears to be more logical
for vie ratios of 0 4 to 1.5 When the vie
Y = Pass-by trip percentage, and
X, = Percentage or commercial land
use wlthm I-mile radius.
Grocery store pass-by tnp percentages
appe:J.r not to have a strong relationship
WHh any of the factors. The most impor-
tant variable according to the data gath-
e~ed. is percentage or commercial land
use surrounding the site. The R1 value IS
Table 1. Summary at Interviews.
Number of Primary Diverted Pass-by
Site No_ Land Use Interviews Trip (%] Trip (%] Trip (%]
1 Grocery Store 57" 29 10 61
2 Grocery Slore 62 34 24 42
3 Grocery Slore 63 30 16 54
4 Grocery Slore 67 42 19 39
5 Grocery Slore 62 40 12 48
6 Grocery Slore 91- 34 17 49
7 Grocery Slore 60 25 17 58
8 Discount Store 57" 28 17 ~
9 Discount Slore 57" 32 22 A'l:.
10 Discount Slore 57" 29 22
11 DIscount Slore 57" 35 17 4q r.
12 Discount Slore 57" 21 29 50
13 Fast Food Res. 57" 22 12 66
14 Fast Food Res. 57" 32 12 56
15 Fast Food Res. 57" 13 18 69
16 Fast Food Res. 57" 2A 15 61
17 Fast Food Res. 57" 22 16 62
1nterviews l<med sflgntlV longer tnan two houn. In oraer to receive mlnlmum number tor sample..
""Two Inlervtewe<S worlced at lt1Is sl1e dUIlng <I p.m.~ p.m. period.
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ratio is less than 0 4, the predicted pass-
by trip percentage quicklv approaches
100 percent. This suggests that Equation
6 should be used carefully for only vlc
ratios of less than 0 4 until more re-
search is conducted and definitive rela-
tionships are established.
Summary and
Conclusions
This study has attempted to analyze all
quantifiable elements that may effect
pass-by trip percentages. They include
surrounding land use adjacent street
ADT and vlc ratio. and development
size These elements were applied to
three different types or developments at
17 study sites.
The results of this studv showed that
tbe pass-by trip percentages at fast-food
restaurants are positivelv affected bv tn-
cre3.Ses in the vlc ratio (congestion) per-
centage of residential land use. and per-
centage of commerCial land use The
only factor that appeared to affect gro-
cery store pass-by tnp percentages was
the percentage of commercial land use
surrounding the site_ This was a negative
relationship. suggestIng that grocery
stores in commercial are3.S receive more
diverted and multlple destination trips.
Discount store pass-by tnp percentages
were very closely related to adjacent
street vlc ratio The higher the ratio. the
smaller the pass-by tnp percentage. This
may be expla.med by the fact that tnps
to discount stores are not usually made
by necessity, as In grocery store or res-
taurant tnps. and people dunng evening
rush bows choose not to go through the
frustration of leaYlng and then re-enter-
mg a busy street.
ThIs study IS an imporum step toward
developing statistically valid procedures
for predicting pass-by tnI' percentages.
However. the study was !inuted in that it
only studied the afternoon peak hour for
17 sites within the CIty of Omaha, Ne-
braska. With such a small sample, it IS
possible that the conclusIOns drawn may
not apply to other CItIes. Certamly, the
information could not be used to predict
weekday off-peak or weekend-peak
dnving eharactenstics. In addition. the
regression analysis did not show a rela-
tionship between adjacent street ADT
and pass-by trip percentages for any of
the three types of development under
study. One would expect a relationship.
(!)
even if it was modified by a vlc rabo
factor.
Tne regression analysis did not pro-
vide a definitive answer as to what fac-
tors most affect pass-by trip percentages.
The study makes it apparent that there
are many factors that affect pass-by trip
percentages. and that more work is
needed to determine what the factors are
so tbat they can be quantified and used
in a straightforward manner. However.
tbe formulas developed in this study are
suggested for estimating pass-by trip
perce:ltages until more data are col-
lected along these lines and definitive re-
latIonships are established in the future.
References
1 Ray, A. Allen. SAS Usu s Guid~: Stam-
ria. Cary, NC. SAS Institute Inc.. 1982.
2. Mid-Allantic Section Institute of Trans-
portalion Engineers. Quanrijyin~ R~(ail
Pass-by Trips_ MASITE Technic:U Project.
O~ober 1989 I
Massoum ,Mous-
savi, P E., is an
assistant professor
of civil engineering
at the Universiry of
Nebraska-Lin-
coln. He received
his B.S. in civil engineering from West
Virginia Institute of Technology and
M_S. and Ph.D degrl!es from Virginia,
Polyeechnic InsritUJe and Staee Umversuv.
He IS an Associace Member oj ITE.
Micnad N. Gor-
man, P E.. is the
cuy traffic engineer
of Omaha. Ne-
braska He re-
ceIved a B S de-
gree in civil
engineering from Iowa Stare Universicy
and a M_S degree from Universicy of Ne-
braska-Lincoln. He is a Member of ITE.
16 . ITE" JOURNAL foMY 1992