Comparison of Office/Business-Related Trip Generation Rates

Comparison of Office/Business-Related
Trip Generation Rates
Howard S. Steini
and Cary D. Vick2
The 1987 ITE Triu Generation Report (4th Edition) contains several
improvements to the earlier report including a larger data base, more land
uses, and plotting of actual data. Most important is the derivation of trip
generation (regression) equations that allow the calculation of trips based
on the actual development size (e.g. square feet, acres, or employees).
This feature is critical because trip rates (e.g. trips per 1,000 square feet)
can vary significantly, by the size of the development, particularly for large
retail or office-related uses. The basic rationale for this is that the larger
the use, the less an additional square foot of building area adds to the trip
generation rate. Larger developments have may have multiple uses
allowing for multi-purpose trips.
Office Related Land Uses
The 1987 ITE Trip Generation Report includes the following officerelated
land use categories:
Light Industrial (110): TypicaI industries included in this category were
printing plants, material testing, and assemblers of data processing
equipment. The emphasis of this land use ~tegow is not manufacturing.
It should be noted that all the sites studied by ITE were free-standing
facilities devoted to one use. The average gross floor space per employee
was 440 square feet or 2.3 employees per l,OtM square feet.
At one extreme might be a fast-food restaurant whose mere presence will
generate a number of trips, but it size would not be a good indicator of
the actual number of trips it would generate. Most people go to a fast
foodrestaurant basedon hs location (andwhetherornoth hasa drivein
window) not whether it has 4,500 square feet rather than 4,0CClsquare
feet. To some extent the importance of location and access are also
better indicators of trips to smaller retail shopping centers, particularly
during rush hours. Larger centers, where a shopper wiB have a larger
selection of merchants, will attract more traffic. However, above some
size the addition of a small amount of retail space will general have a
negligible effect on the peak hour trips.
With office uses the phenomena is not quite the same. With smaIl
buildings the tenants may be somewhat unrelated and each office will
require basic support staff (e.g. receptionist) regardless of the number of
employees. As the building size increases, the support ratio of support
staff to other workers wiB decrease and more space might be devoted to
equipment. At some size, the building may attract a single tenant and/or
be located in a office park or CBD area where transit use and ridesharing
would be a factor.
This paper will deal with the trip generation rates of office-related uses.
In addition to the issue of the change in trip rates as the size of the
development increases, there are also several types of land use categories
that have components that may be general office buildings, industrial,
commercial, production and warehouse components. Many business
developments are now being designed to accommodate these different
types of uses within the same development or even building structure.
Additionally, many business developments are being designed in a campus
type setup so that the buildings are related and may contain support retail
(e.g. deli, office supplystore). Consequently, a largenumbertrips maybe
internal tothesiteandthenumberoftrips tothedevelopment thatcome
fromexternal sources wouldbe significantly lessthanthe sum of the
individual buildings. Some external trips may only enter the site once, but
visit many of the buildings (e.g. postman, deliveries, salesmen, etc.).
The objective of this paper is to demonstrate how trip generation rates for
officelmsiness-related land use change as the size of development
increases. Comparisons will be made for the magnitude of trips and trip
rates among the different land use categories.
1. AsSociate
Callow Associates, Inc.
Reston, Virginia
2. Vice President
Callow Associates, Inc.
Reston, Virginia
Industrial Park (130): These contain a number of industrial of related
facilities and are characterized by a mix of manufacturing, service, and
warehousing with a wide variation in the proportion of each use. Some
industrial parks may contain one or two dominant tenants while other
contain many different tenants. The average gross floor space per
employee was 529 square feet or 1.89 employees per 1,000 square feet.
The ITE Report lists a transit trip factor of 0.038 weekday transit trip
ends per 1,000 square feet of gross ffoor area,
General Oflice Building (710): This category contains all different types
of office building(s). These may have one or more tenants and include
professional services, insurance companies, company headquarters, and may
include services for tenants such as banks, restaurants or cafeterias, and
service retail facilities. This category contains the most data points. The
average number of employees per 1,000 square feet was 4.17, but ranged
from 2.16 to 8.00 and generally decreased with increasing building size.
No overall transit trip rates are given.
Ofllce Park (750): These are generally subdivisions of planned unit
developments containing office buildings and support services such as
banks, restaurants, and service stations arranged in a park or campus-like
setup. No employee density data is given. The ITE Report lists a transit
trip factor of 0.13 weekday transit trip ends per 1,000 square feet of gross
floor area.
Research Center (760): These are groups of buildings devoted to research
and development activities and include offices and light fabrication areas.
The average gross floor area per employee was 450 square feet or 2.2
employees per 1,000 square feet of floor area. The ITE Report lists a
transit trip factor of 0.43 weekday transit trip ends per 1,000 square feet
of gross floor area.
Business Park (770): These consist of a group of flex-type buildings
served by a common roadway system similar to an office or industrial
park. The buildings generally are single level, but occasionally are two
stories. Typically 20 to 30 percent of the space in the buildings are
offices, but the majority is warehousing or light industrial uses. The
buildings are designed to be flexible to house different types of tenants
which vary from start-up companies to established small companies. The
backside of many buildings is served with a garage door.
The ITE Report contains limited guidance about using these land use
categories. The 1987 Report notes that the General Office building
category (710) overlaps with other office-related categories [i.e. Office
Park (750)], but more data is needed to better define the differences
among office-related land use categories. It is suggested that the
General Office BuildIdg category Deused when estimating trip generation
for one or more office buildings in a single development. Furthermore.
402 ITE 1989 Compendium of Technical Papers

ifthebuildings are isolated it is suggested that the buildings be estimated
individually. When the buildings are interrelated the ITE Report suggests
that the total area of all buildings be used to calculate trip generation.
If other uses such as banks or restaurants are included in the
development, then the trip generation should be treated as a mixed-use
development. Finally the ITE Report states that the Office Park category
should be used when the breakdown of the individual buildings is not
known.
ComparisonofITE ReportTripGenerationData
Tables 1, 2, and 3 present peak hour trip generation data and equations
by several trip generation variables for office-reIated land use categories
from the 1987 ITE Trip Generation Report. (It should be noted that
some summary data was not presented in the 1987 Report and was take
from the 3rd Edition, 1983.) Table 1 presents this data based on the
building gross floor area which is probabIy the most common form of the
data used. Most often the parcel size and FAR (ratio of gross floor area
of buildings to square footage of the site) are known and the building
sizes can be estimated. Table 2 presents the data on an employee basis.
This data is more accurate than the other categories in predicting trip
generation, but in planning new development the actual number of
employees and employee densities are not known. Table 3 compares these
data by number of acres in the site. Afthough the number of acres in a
site are known, these data are not directly comparable because building
FARs and employee densities significantly differ among office-related land
uses and these factors would significant affect trips to the site.
Consequently, this paper will explore in detail only the relationship of
these rates on a building gross floor area basis because these rates are the
most used and comparable basis for calculating trip generation.
Irrcspcctivc of the bask for calculation, it is reassuring thatthe
directional splitoftripstooffke-related sitesduringAM and PM peak
hours is generally the same, although theOffimParksappeartohavea
s]ighlly higher percentage of their trips in the peak direction than the
other categories. It is also interesting to note that, in general, the average
trip generation rates in the AM peak are roughly equal to the PM peak
(with the PM peak sometimes slightly higher) regardless of the trip
generation variable. However, it is disturbing that the actual number of
studies in any of the categories is small and the range of trip generation
ra(es observed was quite large within each land use category, often an
order of magnitude (10 times) apart.
,% might be expected the peak hour trip generation rates per employee
arc very similar among the different land use categories when computed
on an employee basis because it is primarily employees accounting for
trips to the site during the peak hours. Vehicle occupancy does no differ
significantly among these land use categories. Comparing the other trip
generation variables indicates that, in general, the General Office Building
(710) category had the highest average trip generation rates. There
seems to be a pattern among these data in that categories Light Industry
(110) and Industrial Park (130) have significantly lower rates (about half)
than the other categories. Next highest would be the Research Center
(760) followed by the Business Park and Office Park categories.
Finally, Table4 presents theaveragedailytraffic tripgeneration estimates
andregression equations forthevariousofficerelated landusecategories
basedon thesamesetoftripgeneration variables. The datainthistable
indicate different trendsthanthepeakhourdata.Specifically, therek
Icssdifference betweenthe industrial usesand the ResearchCenter
catcgoty.More importantly, thereisalsolessdifference betweenthe
GeneralOfficecategory andtheBusinessandOfficeParkcategories. In
fact, the Business Park category has the highest daily trip rate per l,OtXt
square feet of gross building space.
It is satisfying that the general trends noted above follow what would be
expected by examining the definitions of these office-related land use
categories. The industrial categories are generally less intense in
development and their employee per square foot densities and have fewer
trips during the peak hours and throughout the day. The other campusstyle
larger devckrpments such as Office Park and Business Park have less
peak hour traffic than the GeneraI Office Building category, but have
comparable or even higher daily traffic perhaps due to their other uses in
the development such as banks and restaurants. The Research Center
category had peak hour trip rates similar to the Parks due to ifs Iower
employee density, but they typically do not include the other uses and
consequently had an average daiiy trip rate similar to the industrial
categories.
Comparison of Trip Generation Equations
The previous Tables also presented the trip generation equations that
were derived from the data to predict trips based on the three variables
(i.e. building floor a:ea, site acres and employees). The best-tit regression
equations were dertved from the employee data with most equations
having an R-squared value greater than 0.9. However, as explained above,
our analysis will deal with the equations for trips per 1,000 square feet
gross building area. it is important to note that almost alI of the best-fit
regression equations derived to estimate peak hour or ADT trips are loglinear
equations reflecting the following format:
Trips = A x (variable)B, where B is less than 1
(typically 0.75- 0.90).
This relationship illustrated the scenario detailed in the introduction that
for an addition unit of development a decreasing number of additional
trips are generated.
Tables5 and6 presenthetripgeneration estimates foreachoftheland
usecategories basedon thebuilding areaequations50,030and500,0Ml
squaref@ respectively. lnaddition! theeffective tripgeneration ratesfor
theGeneralOfficeBuilding (710)category werecomputedbydividing the
numberoftripsderivedfromthetripgeneration equationbythebuilding
area(i.e., 50,000or500,000). Fortheotherlandusecategories, theirtrip
ratesweredividedagainby theGeneralOfficeBuildingratestodevelop
theirequivalence index.Forexample,a 50,000ResearchCenterbuilding
would generate 52 percent of the trips compared to an equivalent size
General Office building. These equivalency indices directly compare the
lrip generation of these other office-related land use categories with that
of the General Office category. From comparing these two tables it is
evident that the trip generation rates dramatically decrease as building size
increases, particularly the average daily trips. Specifically, compared to a
50,000 square foot General Office building, a 500,003 square foot
building(s) will have trip generation rates 28 percent less for AM peak
hour trips, 32 percent less for PM peak hour trips and 44 less percent for
daily trips.
Comparing the office equivalent indices indicate many of the trends noted
earlier about the average trip rates; the industrial uses generate about half
the traffic of the General Office category. However, the results
concerning the campus style office developments are different. Based on
these equations, as the size of the development increases, the Office Park
category will generate more AM peak hour trips than the General Office
category as well as more daily trips. The Business Park category will
generate more peak direction traffic than the General Office category at
the larger development sizes.
To better illustrate these relationships Figures 1, 2, and 3 show the AM
peak, PM peak and ADT trip generation rates derived for many different
building sizes as a function of square feet and the office equivalency
indices for tbe other land use categories. From these figures, the decline
of trip generation rates as the development size increases is quite clear.
The changes in the other office-related trip rates relative to the General
Office category are also displayed. It should be noted that a decreasing
trend in office equivalency indices means that the trip rate for the other
office-related land use category decreases more than the General Office
(710) category. Conversely, an increasing trend in the office equivalency
index means that the other land use category does not decrease as much
as the General Office category indicating that the trip rate for this land
use is less affected by increasing size of development.
Ingeneral, thetrendsoftheofficeequivalency indices fortheindustrial
landusecategories (i.e., 110and 130)aremixed, but generally decrease
ITE 1989 Compendium of Technical Papers 403

as the size of the development increases. This indicates that the trip rates
for these land use categories decrease more as the size of development
increased compared to the General Office category. In contrast, the office
equivalency indices for the Research Center category increased as
development size increases. The office equivalency rates for Office Park
category remain constant for AM peak hour trips, but decrease for PM
peak trips and increase for ADT. The office equivalency indices for the
Business Park category increased by about 50 percent for AM peak trips,
but decreased slightly for PM peak hour trips and increased slightly for
the ADT. Overall, the most significant changes occurred for ADT, where
all the office equivalency indices for almost all the other office-related
land use categories increased. That is, the decline in ADT volumes that
occurs for the General Office category is more dramatic than other land
use categories, particularly the campus-style office developments.
To some extent all this analysis may confuse the issue, but in the context
of what each category represent, the results follow our earlier
observations. Regardless of office-related land use category, larger
developments have significantly lower trip generation rates than smaller
developments. Furthermore, at small development sizes, although the
rates between the General Office (710) category and the other officerelated
land use categories differ substantially, the magnitude of the
difference in the number of trips may not be large enough to have any
practical significance. In fact, some of the cumpus-style categories really
don’t apply to small developments. At the larger development sizes, the
industrial land uses have a trip generation rate that is roughly 55 to 70
percent of the General Building Office category. This is also true of the
Research Center category. The results for the campus-style developments
are mixed, but both land use categories resulted in higher ADT volumes
than the General Office category. The Office Park category had a higher
(aPproxirnately15 percent) inboundAM peaktripratethantheGeneral
Officecategory, but theoutboundPM peaktripratewas lower(also
about15percent).The off-peak direction tripratesfortheOfficePark
category arelessthattheGeneralOfficecategory, 5 percentlowerinthe
AM peakhourand 32 percentlowerbtthePM peakhour. For the
BusinessParkcategorythepeakdirection ratesare10 to17 percentless
thantheGeneralOfficecategory, buttheoff-peak rateswerehigherthan
theGeneralOffkecategory.
Conclusions and Recommendations
Tripgenerationisone of thebasicassumptionsto determinetraffic
impactandthemostappropriate datashouldbe used. The ITE 1987 Trip
Generation Report presents trip generation data and regression equations
to apply to the planned development scenario. (Although, the 1987 ITE
Trip Generation Report provides convenient data, the possibility of taking
traffic counts at similar development sites to the one planned should not
be ignored.) Several categories are defined that describe office-related
land uses as detailed in this article. The choice of the most appropriate
land use category should be made by comparing the proposed development
scenario with those defined in the ITE Report. As demonstrated in
article, each of these categories has different trip generation rates and
characteristics.
In general, our analysis has demonstrated that as the size of the
development increases, the trip generation rate per square foot of
development decreases. The decision to model an office-related
development as one unified group of buildings that has shared trips among
its buildings is the most critical choice. Calculating trip generation for a
development by adding each individual building will result in significantly
higher (perhaps double) traffic volumes than treating them as one
development. Furthermore, when analyzing large developments, the
selection of the appropriate land use category and its trip rates are also
a critical factor in traffic forecasting process. This study found that the
trip generation rates for the various office-related land use categories were
consistent with their definitions and should be applied accordingly.
Finally, although the Research Center (760) category is grouped in the
700 series with General Office (710), Office Park (750) and Business Park
(770), its trip generation characteristics are more similar to the General
Industrial (110) and Industrial Park (130) categories.
TABLE 1: 1987 TRIP GENERATION DATA AND EQUATIONS FOR OFFICE RELATED LANO USE CATEGORIES PER 1000 GROSS SQUARE FOOTAGE
lTE CCOE #
AM PEAK
PM PEAK
t# STIXI1ES
An(PH)]
LANO
USE
AVERAGE
RATES
RANGEOF F1TTEO CURVE OIRECTICUAL
RATES EWATION OtSTRISUTIC+l
AVERAGE
RATES
RANGk OF F1TTEO CURVE DIRECT ICUAL
RATES EWATt ON OISTRISUTION
——
——
— —
110
[23(20)1
GENERAL LIGHT
lNOUSTRIAL
o.%
0.17-4.0 Ln(T)=0.70Ln(sf )+l.54 8S% inter
12% exit
1.0.4
0.14-4.5 Ln(T)=0.71 Ln(sf)+l .47 12% enter
88x exit
130
[21(21)1
I NOUSTRlAL PARK 0.93
0.31-2.28 Ln(T).O. S4Ln(sf)+0.89 82X enter
18% exit
0.97
0.30-2.85 T= l/((1.04/sf )+ O.00026) 21% enter
79% exit
71O*
[25(24)1
GENERALOFFICE 2.23
RUILOING
1.2-5.9 Ln(T)=O.86Ln(sf)+l .34 87% enter
13% exit
2.2
0.8-6.4 Ln(T)=0.83Ln(sf )+l.46 16% inter
84!4●xit
750
[25 (27)1
OFFtCE 1.88
PARK
1.25-5.89 Ln(T)=0.86Ln(sf )+l.46 89?4 enter
11% exit
1.47
0.17-4.5 Ln(T).O. ~Ln(sf)+l .91 13%enter
87S exit
760
t17(18)l
RESEARCH 1.24
CENTER
0.37-2.90 Ln(T)=0.98Ln(sf )+0.21 91% enter
9S ●xit
0.94
0.40-4.13 Ln(T)=0.91Ln(sf)+0.52 15%enter
85%exit
770
[12(13)1
SUSINESS 1.54
PARK
0.7.4-2.62 Ln(T)=l .WLn(sf)-O.17 85% enter
15% exit
1.35
0.57-2.68 T=l .22(sf)+48 21%enter
79%exit
● AVERAGE TRIP RATE AND RANGE FOR GENERALOFFtCE BUILOt NG CATEOCRYTAKEN FR~ 3R0 EOITtON
404 ITE 1989 Compendium of Technical Papers

TABLE2: 1987 TR1P GE”CWT[C+(CMTAAHD EWA1ICUS FCROFF,CE RELbTED LANDUSE CATEGORIES8ASE0 CU NW8ER OF EMPLOYEES
AM PEAK
PM PEAK
1TE cmE #
w 5TUDIES
An(w)]
LAW
USE
AWRAC3
RATE
E.Am3 OF FITTED ~RVl DIREC1lWAL
RATES EWA1lC+I 01STR12U11rX
RATE
RAMC2OF
RATES
FITTED CUR=
EOl!A1lC#
——
170
[16(14)1
G2MERALLIGHT
!ti0us7RtAL
0.45
0.22.1.02 Ln(l).0.201n(e) .50 .93X enter
17X ●xit
0.43
0.28-0.+% Ln(l).0.83L”(e) +0.34
22X cm.,
72X ex, t
130
lNCUSIRIAL PARC
0.$2
0.28-1.13 L.(T) .0.86Ln=0.973Ln[e) -0.60
76X em,.
[18(18)1
0111LOIW
13X exit
84X ●xit
750
OFFICE
0.63
0.31.0 .2.3 T= 1/[(1 .97/,).0.000053) 92X ente,
0.39 0.31- 1.0 Ln(T)=O .751n(e) .0.87
15X enter
[6(2.)1
PARK
2.%exit
85X ,X i t
760
RESEARCH
0.L7
0.20.1.39 Ln(T).O. &3L.(e).0. CM 94X enter
0.44 0.27-0.23 Ln(T).0.89Ln(e) -O.04
6X enter
[15(16)1
CENTER
6% exit
94X ,Xi t
770
BUSINESS
0.5.6
0.44.0.63 1. t/{(1.74/e)+O.0CQ18) 34% enter
0.47
0.31-0.63 T= 1/[(2. W/e) -0. CQ013) 18X enter
PARK
14X ●xit
22X ,“tt
● AV2RAGETRIP RATEAND RANGEFc$ G2NERALOFFICE 2UIL01MG CA1ECO17YTAKENFRCU3RD EDITICU
TABLE3: 1987 TRIP GENERATIWDATAANDEWA1l ONS FOROFFICE RELATEDLANDUSE CA1EGCX71ESBASEDW W+4BEROF ACRESIN THESITE
AN PEAK
W PEAK
1Te cmE #
[* $Tml Es
M(FW)l
LAW
uSE
AU2RAG2
RATE
RANGE OF F] llED WRV2
RATEs ECUATICU
BMG2 OF FITTED CIJRV2
D1STR12UTICU RATE RATES EWATIC#
D!$lRIWIICM
——
——
——
110
[17[1511
G2NERA1LIGHT
11UM2STR1AL
7,73
1.61.34.78 1.117.141.(.).101
83X enter
17X exit
7.43
1.32 -28.0 1=3.69 (a)+121
22X enter
22X ●xit
?30
Iw7RlAL
PARK
lo. >b
3.36.41.35 L.(T) .O.79L.($).3.C4 82X cm.,
10.65 3.24.59.38 Ln[l)=0.76L”(a) +3.22
21x enter
[27(21)1
1s% exit
m
●xit
71W
G2NERAL
OFFICE
19.2
9.0-&2. O
not
not
18.0
8.0.81.0 rat
mt
[14(15)1
2UILDIMG
.“Si (able
av. ilsb[e
.“.1 lab[e
,“.1 Ic.b[e
750
OFFICE
23.73
!6.39 -6.3.25 Ln(T)=O.MLn( a)+3.82 92X enter
24.0 15.25-64.67 L.(T) =0.76L. (.).4.15
!5% enter
[6(7)1
PARK
8!4 ●xit
85% ●. i t
764
RESEARCH
19.46
2.857- 300,0 L“(1 )=0.72 Ln(a).3. t.9 Wx enter
17.71 2.39.2.%.62 Ln(T).0.73Ln( e)+3. L1
6X tn~,,
[1&(14)1
CENTER
6X exit
94X ,X i t
770
[10(10)1
2US lNESS
PARC
20.13
8.49-35.62 L“(T).0.94L. (.)*3.14
26X ante.
14% exit
18.03
6.53-32.54 1= 1/{(0 .061.1.0.000273 18X enter
22X exit
. AWRAW TRIP RATEANDnAMGEFW cENERALOFFICE WtLOIMG CATEmY TAKENFRU4 3RD EDI rm
TABLE 4: 1987 ml P GEMERAT[w DATAAw EWATICUS m DA!Lr TRAfFIC m OFFICE RELATEDLAMOUSES
pEa 1000 swmc FEET mm AREA PER ACREAG2OF SITE ?3v. EMPLOYEE
AvmAG2
AV2PAGE
AV2RAGE
llE CME # LAM USE
RAIE
EWATICN
RATE
EU.JA1!CU
RATE
EUJAT[ON
——
110 G2uERALLIGHT
6.97
Ln(T)=O .79 Ln(sf )+2.87
5! .20
T42.22(a )+263.0
3.02 T.2.93(e) +31.0
I12WSTR1AL
130 lNOUSIR!A1
6.97
T.4. fi(sf ).7.66 63.90 T-47.91 (, J+598. O
3.41 L“(T).0..301”(e )+2.5b
PARK
710 G2uERALOFFICE
12.30
Ln(T).0.73Ln( sf).3 .77 240.1 Not Available
3.59 Ln(l)=0.87Ln(e ).2.06
2UILD!NGS
730 OFFICE
Put<
11.60
Ln(l).0.92Ln( sf).2 .90 W3.57 1.1/( (0. C043/a)+O, WWM2)
3.5 L“(1)=7.02L”(, ).1. LU2
7t4
RESEARCH
6.99
L“(ll=O .WL”(Sf)+2.3V 64.24 Ln( T ) =0 72LIU .)+4 .9?
2.45 Ln(T)=O .23Ln(e) .2.06
CEMIER
no
WSIIIESS
12.42
1=17.5397(sf ,.337.0 159. ?5 L“(T)=0..2SL”( .).5.46
4.58 1- llc(0.15/e)+0 .000073>
PARK
ITE 1989 Compendium of Technical Papers 405

TABLE 5: TRIP GENERATION TRAFFIC VOLUMES ANO RATES BASEO ON 1987 lTE EQUATIONS FOR 50,000 SQUARE FEEET OF DEVELOPMENT
VOLUMES VOLUMES EFFECTIVE RATES EFFECTIVE RATES
AM Peak Hour PM Peak Hour AH Peak Hour PM Peak Hour
I TE CATEGORY LANO USE IN fYJT TOTAL IN WT TOTAL AOT IN WT TOTAL IN LY.STTOTAL AOT
. ----- . . . . . . ------ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ------ ------ ---- . . . . . . . . . . . . . . . . . . . . . . . . . . . . ------ . . . . . . . . . . . . . . . . .
710 Genera[ Office 50,000 96 14 110 18 93 111 816 1.92 0.29 2.21 0.35 1.86 2.21 16.31
Office Equivalency Index*:
110 Light Indust. 50,000 63 9 72 8 62 70 388 0.66 0.60 0.65 0.47 0.66 0.63 0.48
130 Indust. Park 50,000 53 12 65 10 38 47 1,014 0.56 0.82 0.59 0.56 0.40 0.43 1.24
750 Office Park 50,000 111 14 125 15 102 117 665 1.15 0.95 1.13 0.S6 1.10 1.06 0.81
760 Research Ctr. 50,000 52 5 57 9 50 59 369 0.54 0.36 0.52 0.50 0.54 0.53 0.45
770 Bus i ness Park 50.000 51 9 60 23 86 109 913 0.53 0.63 0.54 1.29 0.93 0.98 1.12
* Office Equivalency Index equals trip generation rate for other office related category divided
by the trip generation rate for the General Office (710) category.
TABLE 6: TRIP GENERATION TRAFFIC VOLUMES ANO RATES BASEO ON 1987 lTE EQUATIONS FOR 500,000 SQUARE FEEET OF DEVELOPMENT
VOLUMES VOLUMES EFFECTIVE RATES EFFECTIVE RATES
AM Peak Hour W Peek Hour AU Peak Hour PM Peak Hour
LANO USE IN CAJT TOTAL IN WT TOTAL AOT IN UJT TOTAL IN CNJTTOTAL AOT
. ------- ------- . . . . . . . . . . . . . . . . . . . . . . . . -------- . . . . . . . ------- . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ---
710 General Office 500,000 696 104 800 120 629 749 4,587 1.39 0.21 1.60 0.24 1.26 1.50 9.17
Off ice Equi va ( ency Index*:
110 Light Indust. 500,000 318 43 361 63 316 359 2,391 0.46 0.42 0.45 0.36 0.50 0.48 0.52
130 Indust. Park 500,000 369 81 450 90 338 427 3,241 0.53 0.78 0.56 0.75 0.54 0.57 0.71
750 Office Park 500,000 803 99 902 82 549 631 5,527 1.15 0.95 1.13 0.68 0.87 0.84 1.21
760 Research Ctr. 500,000 496 49 545 72 409 481 2,931 0.71 0.47 O.&9 0.60 0.65 0.64 0.64
770 Bus i ness Park 500,000 627 111 738 138 520 658 6,102 0.90 1.06 0.92 1.15 0.83 0.88 1.33
* Office Equivalency Index equals trip generation rate for other office related category divided
by the trip generation rate for the General Office (710) category.
Figure 1: Comparison of ITE Trip Rates for Office-Related
Use Categories DuringAM Peak Hour Traffic
Land
Otnw T*
R, I,,
tix.to
Ui’umrat” *M Offk* do
—..—.——..——.
“—---~
Omw
E@W&..xmY
50000 100000 200000 300000 500000
13roas Floor Area of Development, Square Feet
Llghl Industrial Industrial Park office Park Research Center Business Park
(110) (130) (750) (760) (770)
......... . . .- . . ..-. -,-,-,-.-$-- . . . . . . . . . . . . . . . . . . .
406 ITE 1989 Compendium of Technical Papers

Figure 2: Comparison of ITE Trip Rates for Office-Related Land
Use Categories During PM Peak Hour Traffic
r 2.5
t
2.0
1.5
b740”.tOIrw rat,, 9?I* Om, rat.
I
*-. -.-. -o-,-o-.-,-, -.-, -,-. -.-, -,-. -l-, a,m,-,-, m,m, =.::
~1.0
““-..,:a,d,:,:
.,-.* ’- .::...,,..,.,,.,,.,,.,,.,..,..,..,,.,,.,..,,.,,. ... ..-..-..-,,-,.
. . ...... ...
Oftk.
. . . . . . . . . . ..- E@,hncy
. . . . . .. .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . awl
. . . . . . . . . .- ..-.-”” .
I0.5
50000 100000 200000 300000 500000
Gross Floor Area of Development, Square Feet
10.0
—
Light Industrial Industrial Park Office Park Research Center Business Park
(110)
(130) (750) (760) (770)
........ ......... . . . . . . . . -,-, -,-,----- .,, . ... ... ....4...!
Figure 3: Comparison of ITE Trip Rates for Office-Related
Use Categories for Average Daily Traffic
Land
L5
Off!.. TripR.tw
10
tix.l,o
Wwnratu ..?mI01fk9rd.
o.!
50000 100000 200000 300000 500000
Groin Floor Area of Development, Square Feet
0.(
Light Industrial Industrial Park Office Park Research Center Buslnes$ Park
(110)
(130) (750) (760) (770)
. .............”.. . - . . . . . I .,-, -,.., -,-,4 — .,,.,,.,,.,,..,... !
ITE 1989 Compendium of Technical Papers 407