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function to act as a leading edge in identifying, marketing, and evaluating new products and ser-
vices. This action morphed Metro into more of a "mobility broker," packaging and selling both ser-
vices and assistance appropriate to the marketplace. Included were a wide range of transit services
and High Occupancy Vehicle (HOV) facilities, service contracts with major employers, and a giant
university pass program (U-PASS at the University of Washington), as well as carpool matching,
vanpool program management, and telecommute program assistance. To make these programs
as effective as possible, Metro worked extensively with local communities, TMAs, and with indi-
vidual employers to design the programs and ensure maximum flow of information down to com-
muters (Comsis, 1991).
Perhaps one of the most interesting elements of Metro's program activities was the HOV/TSM
evaluation study, a 2-year study to evaluate the effectiveness of a set of transportation programs,
incentives, and promotional techniques applied in four project areas in King County. Beginning
in 1987, Metro began monitoring the performance of TDM programs at 52 suburban employment
sites, each having received some program assistance from Metro to establish a TDM program.
These sites, along with a group of regional control sites where no special actions were taken,
were tracked using employee surveys. The results were used to ascertain employee awareness of
programs and incentives available to them, and to track changes in mode shares as influenced by
the programs.
As a primary tactic, transportation coordinators were hired for each project area to provide per-
sonalized assistance to employees in planning their commute and taking advantage of marketed
transportation programs and incentives. The initial evaluation surveys, as previously discussed in
connection with Table 19-21, demonstrated a surprisingly low level of employee awareness of par-
ticular programs and incentives that were available to them. The most visible effect of the trans-
portation coordinators' (TCs) efforts was on measurable increases in employee awareness.
Between 6 percent and 19 percent of employees in the four project areas changed commute mode
between 1988 and 1989, and overall, 5.5 percent of employees in these four areas stopped driving
alone and began using an HOV mode. However, the same proportion discontinued their HOV use
and began driving alone, with the result being no net change in mode split. Meanwhile, at the con-
trol sites, 4.7 percent of employees shifted from driving alone to use of an HOV mode, but a slightly
greater percentage, 6.0 percent, changed from HOV to driving alone.
The interpretation of these results by Metro was that in an environment where there are few good
alternatives, plenty of free parking, and considerable staff turnover with new hires, the TDM outreach
activities helped prevent net losses in HOV mode use in the managed areas. For those employees
who shifted from driving alone to an HOV mode, 48 percent of those individuals cited one or more
of the TC activities as affecting their choice, whereas 39 percent of employees at the control sites
cited TC activities. Among employees who switched from HOV to driving alone, only 18 percent of
those working in the four project areas cited TC activities as affecting their choice, and 11 percent
of employees in control areas cited TC activities (Municipality of Metropolitan Seattle, 1990).
Modeling Studies
California Air Resources Board Survey and TDM Program
Critical limitations in the travel and impact data compiled and released in the large-scale state
and regional employer TDM programs have been outlined in the "Analytical Considerations"
subsection of the "Overview and Summary." In appreciation of these shortcomings, CARB spon-
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sored a research study, in 1993, centered on the collection of original travel survey data from an
acceptably large and diverse employer sample in the Los Angeles and Sacramento air quality
management areas.
The objective was to develop a mathematical tool that would be credible for designing or testing
the capability of employer TDM programs to meet mandated trip reduction goals. Detailed travel
data were acquired from 2,437 employees at 45 different employers, along with sufficient informa-
tion to permit geocoding of trip origin and destination, which was used to append corresponding
travel time and cost of available travel alternatives from the respective regional travel models. An
attempt was then made to evaluate the effectiveness of particular TDM strategies through the esti-
mation of logit-type mode choice models. These models were structured to predict the probability
of choosing a particular travel option in relation to individual travel characteristics and the strate-
gies applied by the respective employer (Comsis, 1993a).
The results of this modeling exercise are summarized in Table 19-30, presenting parameter estimates
for those variables that were found to be statistically meaningful in selection of the respective travel
mode. The first group of variables in the table represents the travel time and cost variables typically
at the heart of mode choice models. The modeling analyst compared the coefficients for in-vehicle
time, transit out-of-vehicle time, auto operating cost, transit fare, and parking cost with the esti-
mates from ten other metropolitan area travel models and found them to be satisfactorily compa-
rable in both relative and absolute magnitude.
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Table 19-30 CARB TDM Model Parameter Estimates
Coefficient Values
Drive
Variable Alone Carpool Vanpool Transit Bike/Walk
Mode-Specific Constants -1.517 -7.070 -3.048 -2.153
Travel/Transportation-System Variables
In-Vehicle Time (IVT-minutes) -0.0399 -0.0399 -0.0399 -0.0110
Out-of-Vehicle Time (OVT-min.) -0.0165 a -0.0441
Operating Cost/Fare (cents) -0.0034 -0.0034 -0.0034 -0.0061
Parking Cost (cents) -0.0086 -0.0086 -0.0086
Availability of Bike Lanes 1.220
Employee Characteristics Variables
Laborer (1=Yes) 0.3999 0.9367
Professional (1=Yes) -0.2666 0.9054
Manager (1=Yes) -1.064
Gender (1 = Male) 0.8727
Elderly (1=Yes) 0.5262 0.4355 b 0.9089
Midday Business Travel (1=Yes) -0.7745
Staggered Work Hours (1=Yes) 0.8148
Part-Time Worker (1=Yes) 0.5377 b
Single Worker Household (1=Yes) -1.027
Married (1=Yes) 0.9944
Worksite Characteristics Variables
Parking Spaces per Employee -0.4155 b
Campus/Institution (1=Yes) -0.8150
No. Adjacent Retail Land Uses 0.1069 0.1069
TDM Strategy Variables (1=Yes)
ETC and Rideshare Matching 0.0777 c 0.0777 c
Preferential Rideshare Parking 0.1214 b 0.1214 b
Transit Info Center and Pass Sales 1.083
Bike Racks or Showers/Lockers 0.4056 b
Guaranteed Ride Home 0.4476 0.4476 0.4476 0.4476
Modal Subsidy 0.0125 0.0125 0.0125
Prizes, Meals, Awards 0.0826 d 0.0826 d 0.0826 d
Use of Company Vehicles 0.7861 0.7861
Company-provided Vans 2.586
Notes: Unless otherwise noted, all coefficients are significant at 95 percent confidence level.
a Value constrained to 1.5 times IVT coefficient. b Significant at 80% confidence level.
c Not significant at 80% confidence level. d Coefficient derived from other sources.
Source: Comsis (1993a).
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The second group of variables represents employee characteristics. The estimates imply that work-
ers who are laborers are more likely to choose carpool or transit, especially transit, while profession-
als and managers are among the least likely to choose those modes. Professionals are most likely to
choose vanpooling as a commute alternative. Males are the most likely to bike or walk, while the
likelihood of carpooling, vanpooling or taking transit is clearly greater for older workers. Persons
who must make midday business trips are fairly unlikely to want to carpool, but for some reason
that effect doesn't seem to carry over to vanpooling or transit.
A modeled relationship that seems inconsistent with other findings is that employees on staggered
work hours are much more likely to take transit. Part-time workers are more likely to opt for transit as
an alternative, not surprising since other ridesharing modes require daily commitment. Employees
who are the sole worker in the household are less likely to carpool, but do not seem to be discouraged
by transit or vanpooling, while married employees are most likely to choose vanpooling.
A limited set of worksite characteristics constitutes the third group of variables. Testing of the work-
site variables suggests that transit use is likely to be less where parking space ratios are higher, that
carpooling is less likely at campus locations or institutions, and that as the number of adjacent retail
land uses increases, the rate of carpooling and transit use will increase (Comsis, 1993b).
The above relationships helped set the stage for evaluating the contributions of TDM strategies
(labeled "incentives" in the study). The TDM strategies shown in the lower portion of Table 19-30
were the only "incentives" that could be quantified as strategy-specific variables in the models.
Their relationships with the respective mode choice observations were strong enough that statisti-
cal significance could be demonstrated. This does not mean, however, that the value of individual
coefficients is regarded as wholly plausible in all cases.
Having an employee transportation coordinator (ETC) and offering rideshare matching is esti-
mated to have a moderately positive influence on employee choice of carpooling or vanpooling
(the two measures were only significant when combined), as does the offering of preferential
parking. Predictably, maintaining a transit information center and offering on-site transit pass
sales has a fairly solid positive influence on choice of transit, while offering bike racks or showers/
lockers has a moderately positive effect on the decision to bike or walk. Offering use of company
vehicles has a clearly positive effect on carpooling or vanpooling choice, while providing com-
pany vans has a very substantial effect on vanpooling choice.
A dilemma arises in the estimates shown for Guaranteed Ride Home (GRH) and the monetary
incentives (modal subsidies). GRH has a surprisingly large effect in the model on commuter choice
of all alternative modes, at a level (model coefficient of 0.4476) that exceeds most other strategies.
This finding stands in contrast to the relatively modest effect of providing modal subsidies
(0.0125). The offering of prizes, free meals, and awards also garners a higher coefficient value
(0.0826) than does provision of modal subsidies.
There are several possible explanations for this apparent anomaly. First, GRH has been demonstrated
to be a very popular and highly demanded feature of an employer TDM program, so it is possi-
ble that it actually does carry a higher value in employee choice of alternative commute modes.
Second, the survey did not ask what the monetary value was of either the financial incentives or
the cost of parking at the worksite, so a wide range of dollar magnitudes was presumably lumped
together. Third, it is probably useful to note that, while the data collected explicitly for this study
were both more reliable and less aggregated than those found in the regulatory agencies' data-
bases, they do represent only a single point in time--when the survey was conducted. They reflect
behavior inside an employer that was applying certain combinations of TDM strategies, but how
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long it had been since the strategies had been implemented could not be known, nor could there
be empirical identification of the degree to which introduction of any of the individual strategies
directly affected employee travel choice.
Another matter of interest is the degree to which individual employees were aware of the availabil-
ity or implication of particular TDM strategies. Because employees were asked to identify particular
TDM strategies made available by their employer, it was possible to cross-check against what strate-
gies were actually available. As described and tabulated in the "Underlying Traveler Response
Factors" section, under "Individual Behavioral and Awareness Considerations"--"Awareness and
Comprehension of Options" (see Table 19-21), the program awareness of employees ranged from
77 percent for preferential parking down to only 17 percent for bus pass discounts and 15 percent
for transportation fairs.
Awareness was seen as such an important issue that it was decided to attempt to model the per-
cent awareness for the eight TDM incentives that were included in the mode choice model. Sub-
models were calibrated to accompany the mode choice model, with separate equations calculated
for each incentive. Test plots led to the use of annual ETC marketing plus administrative cost per
employee as offering the best explanation for variations in awareness.
The implication of this relationship is that higher levels of marketing, outreach, and information
exchange lead to a more informed, and hence more "aware" employee, which translates to the TDM
strategies having greater use and impact. Ultimately, this chain of mode choice-TDM Incentive-
awareness relationships provided the basis for a software tool known as the Travel Demand
Management Program, which was pilot tested by SCAQMD in Los Angeles (Comsis, 1993a). The
software's Users Guide summarizes the default coefficient values for each model (Comsis, 1993b).
Center for Urban Transportation Research Worksite Trip Reduction Model
A more recent attempt to model the effects of TDM strategies on travel behavior was undertaken by
the University of South Florida's National Center for Transit Research Program at CUTR. This
research set out to use as its primary data source the employer plan data compiled under California's
Regulation XV/Rule 2202 program, as well as comparable data from Washington State's Commute
Trip Reduction Law, and the Pima Association of Governments in Tucson, Arizona (CUTR, 2004).
The CUTR researchers quickly recognized the same challenges in working with these data that
had been experienced by the research efforts mentioned earlier, namely problems of aggregation,
missing or incomplete employer plan records, and insufficient information on the nature or mon-
etary value of key incentive actions. The strength of these data are in the large number of records
(submitted plans), the diversity of programs and settings, the derivation of employee travel from
a 5-day work week cycle, and the ability to compare the same employer over time. Nevertheless,
the nature of the data makes it difficult to assess the effects of individual TDM strategies through
statistical methods like multiple regression.
A principal reason for this difficulty is the inability to construct a framework around those key
determinants of travel choice that are largely peculiar to the individual--such elements as trip
length, availability and quality of travel alternatives like transit, nature of occupation, and even
income and auto ownership. With the regulatory program data, the conceptual framework must
be limited to analyzing changes in aggregate employer vehicle trip rate in relation only to the iden-
tified strategies in the trip reduction plan.
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In response, the researchers opted for a different analytic approach, based on the neural network
concept. Neural networks are described as "a group of highly interconnected and relatively sim-
ple computational units" (representing a structure of links and nodes). "Each of these computa-
tional units performs processing of its inputs to produce a single output. The neural network
connects the output of each unit to the inputs of many other units through different weights." The
process of calibrating the different units and paths is characterized as "learning," in the context of
"training," in which one of several learning techniques is used to modify the weights in an orderly
fashion. Since it is not the purpose of this summary to appraise the theory or accuracy of the neural
network approach, the reader with an interest in better understanding this approach is advised to
consult an authoritative source, starting with the project report (CUTR, 2004). This discussion will
focus primarily on the findings from the CUTR application.
Standard linear regression models were used by CUTR in an early exercise to sort through and
rank the relative importance of the numerous incentive measures. Because many measures were
found to be quite similar, it was decided to group them into 12 categories, consisting of the follow-
ing (CUTR, 2004):
· Facilities and Amenities: Passenger loading areas, facility improvements, preferential parking
areas, bike racks and lockers, showers, and changing facilities.
· Guaranteed Ride Home: TMA provided program, company vehicle use, emergency guaranteed
ride, rental car guaranteed trip, and taxi guaranteed trip.
· Flexible Schedules: Flextime for ridesharers including work shifts and grace periods.
· Marketing Programs: Information center, transportation fairs, focus groups, posted materials,
new-hire orientation, personal communication, company recognition, special interest clubs,
TMA membership, written materials, and zip code meetings.
· Rideshare Matching: Regional commuter matching agency and employer-based matching system.
· Financial Incentives: Transportation allowances, walk or bike-to-work subsidies, carpooling
subsidies, and other direct financial subsidies.
· Parking Management: Increased parking fees for drive-alone commuters and subsidized park-
ing fees for rideshare units.
· Telecommuting and Telework: Work at home and work at satellite center.
· Compressed Work Weeks: 9/80, 4/40, 3/36, or any related arrangement.
· On-Site Services: On-site childcare, cafeteria, ATMs, postal facility, fitness center, transit infor-
mation, or pass sales.
· Non-Financial Incentives: Auto services (fuel, oil, tune-up); gift certificates; free meals; catalogue
points; time off with pay; drawings; and awards.
· Commuter Tax Benefit Incentives: Introductory or ongoing transit passes or subsidies, subsi-
dized vanpool seats, and ongoing vanpool subsidization.
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The researchers used these 12 categories of strategies to sort and analyze the types of programs that
were being implemented in the employer sample. They found 1,671 distinct strategy combinations
among the sample plans, and out of these, identified the 50 most common applications. These are
pictured in Table 19-31, in declining order of occurrence, from the most common combination--
found in 1,036 plans--to the least, found in 76 plans. Together, this set of plan combinations
accounted for 9,886 of all 21,267 plan records, or about 46 percent.
One feature of interest that can be observed in Table 19-31 is the frequency with which particular
strategy types are employed in these most common programs. The most frequently applied strate-
gies are shown in the leftmost column while the least frequent are on the right. The key trends
shown here are the inclusion of marketing, facility amenities, rideshare matching, and guaran-
teed ride home in 90 percent or more of all programs. Non-financial incentives were present in
39 of the 50 programs, and on-site services were present in 33 of 50. Commuter tax benefit strate-
gies, consisting of financial incentives that offer tax advantages (basically alternative mode sub-
sidies) were present in a surprising 30 of 50 programs. Non-exempt financial incentives were only
offered in 23 cases and parking management strategies showed up only in 1 of the 50 categories.
Perhaps also surprisingly, alternative work schedule strategies--flexible work hours (13 programs),
telecommuting/telework (eight programs), and CWW (16 programs)--were among the least fre-
quently offered strategies. Overall, these findings have a basic similarity to those presented ear-
lier in Table 19-27 from a 1992 study, supporting the assertion that marketing and other "soft"
TDM strategies are the most commonly found in TDM programs, even those conceived under reg-
ulatory circumstances. The more influential "economic incentive" strategies are generally much
less common.
The new Worksite Trip Reduction Model was then used to estimate the vehicle trip reduction
impact of each of the 50 most common programs. To do this, the model was run parametrically,
with different baseline conditions in terms of starting transit mode share and starting vehicle trip
rate level.13 Results are presented in Table 19-32, organized according to starting ranges of transit
mode shares and vehicle trip rates (CUTR, 2004).
13 Note that VTR as used in the source document stands not for vehicle trip reduction, but rather for vehicle
trip rate. The vehicle trip rate in the source and in Table 19-32 is computed in the form of vehicle trips per
100 employees, and is the inverse (times 100) of Average Vehicle Ridership (AVR).
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Table 19-31 CUTR Worksite Trip Reduction Model--Ranking by Frequency of Occurrence
of the 50 Most Common Program Combinations
Facility Ride- Guar. Non- Comm. Comp. Flex. Parking
Package Market- Amen- share Ride Financial On-Site Tax Financial Work Work Tele- Manage- Total
Number ing ities Matching Home Incentives Services Benefit Incentives Week Hours Work ment Cases
1 X X X X X X X X 1,036
2 X X X X X X X X X 689
3 X X X X X X X 554
4 X X X X X X X 503
5 X X X X X X 466
6 X X X X X X X 337
7 X X X X X X X 304
8 X X X X X X X X X 290
9 X X X X X X 267
10 X X X X X X 264
19-113
11 X X X X X X 234
12 X X X X X 233
13 X X X X X X X X 232
14 X X X X X X X 228
15 X X X X X 223
16 X X X X X X X X X X 211
17 X X X X X X X 205
18 X X X X X X X X 184
19 X X X X X X X X 157
20 X X X X X X 147
21 X X X X X X X X 134
22 X X X X X X X 125
23 X X X 124
24 X X X X X X X 124
25 X X X X X X X X X 117
26 X X X X X X X X 117
(continued on next page)
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Table 19-31 (Continued)
Facility Ride- Guar. Non- Comm. Comp. Flex. Parking
Package Market- Amen- share Ride Financial On-Site Tax Financial Work Work Tele- Manage- Total
Number ing ities Matching Home Incentives Services Benefit Incentives Week Hours Work ment Cases
27 X X X X 116
28 X X X X X X X X X 115
29 X X X X X X 113
30 X X X X X X X X 111
31 X X X X X X 111
32 X X X X X X 108
33 X X X X X X 107
34 X X X X X X X X X 106
35 X X X X X X X 106
36 X X X X X X 105
19-114
37 X X X X X X 101
38 X X X X X X X X X X 96
39 X X X X X X X X X X X 96
40 X X X X 92
41 X X X X X 89
42 X X X X X X X 86
43 X X X X X X X 81
44 X X X X X X X X X 81
45 X X X X X 80
46 X X X X X X 78
47 X X X X X X X X X 78
48 X X X X X 78
49 X X X X X X X X X 78
50 X X X X X 76
Total 50 46 46 44 39 33 30 23 16 13 8 1 9,793
Source: CUTR (2004).
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Table 19-32 CUTR Worksite Trip Reduction Model--Estimates of Vehicle Trip Rate Reductions
for the 50 Most Common Employer TDM Program Packages
Starting Transit Shares (shown as percentage ranges) and Starting Vehicle Trip Rates (in italics as ranges)
Package Transit Share = 0 - 5% 5 - 15% 15 - 25% 25 - 35% 35 - 45%
Number 100-90 90-80 80-70 70-60 60-50 100-90 90-80 80-70 70-60 60-50 80-70 70-60 60-50 70-60 60-50 60-50
1 -5.3 -2.9 -0.6 0.6 2.2 -6.8 -3.2 -1.5 -0.1 3.7 -3.2 -0.7 2.4 -1.9 -0.5 -1.8
2 -3.7 -1.6 0.4 2.5 -1.9 -2.6 -1.1 -0.2 1.7 0.4 -1.9 -0.1 0.9 -1.9
3 -6.5 -3.7 -1.0 -0.1 3.1 -5.8 -4.5 -1.4 1.5 2.4 -3.5 -0.4 -0.3 -3.0 0.6 -2.1
4 -6.1 -4.2 -1.6 1.3 -3.3 -4.3 -2.2 0.3 -3.3 -2.0 -1.9
5 -5.3 -2.1 -0.1 1.5 3.5 -4.6 -1.9 0.1 1.9 0.7 -1.6 2.0 2.3 -1.4
6 -6.3 -3.2 -0.4 -3.0 -7.5 -3.7 -1.0 0.5 -3.5 -1.1 -2.9
7 -4.3 -1.9 0.8 2.7 2.1 -1.8 -1.5 -0.7 2.9 6.3 -2.4 1.0 4.4
8 -2.8 0.0 1.6 4.5 6.6 0.5 1.1 -1.1
9 -6.8 -4.9 -2.1 0.2 1.6 -5.4 -4.5 -2.5 0.4 -1.0 -6.3 -0.8 0.2 -1.8 1.3
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10 -3.2 -0.5 1.7 3.9 4.8 -1.2 0.8 1.1 6.0 -1.0 2.0 1.5
11 -6.4 -4.2 -1.6 1.3 -3.3 -4.3 -2.2 0.3 -3.3 -2.0 -1.9 1.5
12 -6.9 -4.1 -1.0 1.5 1.1 -2.4 -0.2 1.8 4.1 1.3 3.3 -3.3
13 -4.9 -2.9 -0.6 1.0 7.6 -2.3 -1.1 1.1 -2.0 -0.2 -3.1
14 -5.7 -2.6 -0.1 1.5 -2.7 -1.4 2.1 1.3 -5.6 -1.0 -2.5
15 -5.7 -3.7 -1.1 1.1 3.1 -3.4 -0.6 1.7 5.2 -1.7 0.4 4.2 -0.5 1.4
16 -1.5 0.6 2.5 4.3 5.9 0.5 1.4 3.7 0.4 0.8
17 -5.8 -3.3 -2.1 -2.4 2.6 -3.9 -2.2 -1.0 -2.7 -1.4 5.3
18 -3.1 -0.9 1.2 3.4 2.4 -1.8 0.2 2.7 4.4 3.3 -1.9
19 -4.3 -3.4 -1.6 1.7 -1.6 -1.8 0.7 -1.0
20 -5.7 -3.6 -2.1 -1.0 -4.4 -2.1 -1.3 1.3 -3.7 0.5
21 -3.2 0.2 2.8 5.0 3.5 0.1 1.7 2.1 4.8 -1.8
22 -5.5 -4.4 -3.5 -0.6 -3.7 -3.2 -0.9 1.0 -2.5 2.4
23 -6.0 -3.1 -1.4 2.3 4.0 -4.8 -1.3 1.5 4.5 1.3 3.2
24 -4.3 -1.4 -0.3 -0.4 -1.5 -0.2 0.4
25 -0.9 -0.2 2.0 2.8 0.0 1.1 3.1 -0.7 3.4
26 -4.8 1.3 1.0 0.9 -3.2 0.8 2.6 1.1 -1.6 -0.1 -3.2
(continued on next page)
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Table 19-32 (Continued)
Starting Transit Shares (shown as percentage ranges) and Starting Vehicle Trip Rates (in italics as ranges)
Package Transit Share = 0 - 5% 5 - 15% 15 - 25% 25 - 35% 35 - 45%
Number 100-90 90-80 80-70 70-60 60-50 100-90 90-80 80-70 70-60 60-50 80-70 70-60 60-50 70-60 60-50 60-50
27 -5.1 -2.7 -0.4 1.5 2.9 0.5 -2.8 -0.7 2.2 0.7 1.0 2.3
28 -4.4 -0.6 1.8 3.1 8.8 -1.1 0.2 2.7 -0.3
29 -6.9 -3.7 -1.2 -4.8 3.0 -3.6 -0.9 2.3 0.4 -0.8 -0.2 -2.4 -2.0 -1.6
30 -3.2 -1.6 0.9 3.7 2.0 -0.2 -1.3 3.0 -0.9 -0.3
31 -4.3 -2.0 0.0 1.2 4.2 -1.2 1.7 1.8 4.3 -1.1 0.4 3.2 -1.4 2.1 -0.9
32 -4.8 -3.3 -1.2 0.5 -1.6 -1.4 1.9 3.3 -3.6 -0.9 -2.6
33 -5.3 -4.1 -0.9 0.5 -3.7 0.1 0.6 4.1 -5.1 0.1 -0.2
34 -1.7 0.3 2.7 -0.4 1.1 2.5
35 -6.5 -2.9 0.1 0.3 -2.6 -0.9 1.6 4.8 -1.0 -0.8 3.8 -1.7
36
19-116
-5.9 -3.5 -1.4 0.1 1.3 -7.6 -3.8 -0.3 -1.1 0.5 -1.6 1.1
37 -4.8 -2.9 -0.5 2.3 -3.5 1.6 3.8
38 -3.1 0.1 2.4 4.9 5.3 0.8 3.4
39 -3.5 -1.0 2.1 1.0 -0.9 1.4 3.6 2.1
40 -5.1 -2.1 -0.9 0.9 2.8 -2.9 -2.5 0.9 0.3
41 -6.1 -2.4 1.0 3.2 2.5 -1.4 0.9 3.1 4.6 3.0 2.1 3.2
42 -2.9 -0.5 2.5 4.1 4.6 -2.9 2.0 0.8
43 -2.7 0.7 2.9 0.9 2.5 2.5 2.7
44 -5.3 -2.1 -0.6 1.5 -3.7 0.5 0.0
45 -2.8 0.3 0.8 6.4 -0.5 0.1 1.2 0.0 1.1 1.0
46 -3.0 -1.5 1.3 -1.8 -1.8 1.1 8.0 2.0
47 -2.6 -1.6 0.7 2.6 2.2 -1.1 -0.4 -2.2 -3.5
48 -4.4 -3.1 -0.9 -2.4 -3.6 -2.8 -1.7 -1.6 -3.7 -4.6
49 -5.5 -2.4 0.7 2.0 -3.5 -1.9 1.7 5.2 -1.2 -1.7 0.3 -1.5
50 -6.5 -3.9 -1.9 1.0 2.1 -4.9 -4.4 -1.5 0.3 1.9 -1.1 -0.4
Note: The vehicle trip rates shown as ranges, and also the trip rate change estimates, are in units of average vehicle trips per 100 employees.
Source: CUTR (2004).
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The reason it is necessary to specify the starting conditions in this manner is because the strategy
combinations in each package affect the distribution of mode choices differently depending on the
starting point. For example, if an employer currently has carpooling as the dominant mode among
employees using alternative modes, and the package of strategies is such that transit use is pre-
dominately encouraged, the effect would be to move some percentage of both SOV commuters and
carpoolers to transit. The proportionate shifts that occur among modes depend heavily on the start-
ing modal distribution and the corresponding vehicle trip rate. A similar approach was used in
Chapter 3 of the 1993 FHWA guidebook Implementing Effective Travel Demand Management Measures
(Comsis and ITE, 1993).
Studying the vehicle trip reduction estimates in Table 19-32 suggests in general that any given pro-
gram of strategies starting with a lower initial vehicle trip rate will normally have a lesser trip reduc-
tion impact than if it were to start with a higher vehicle trip rate. In other words, given the same set
of TDM strategies, employers whose starting vehicle trip rate is already down in the 80-70 vehicle
trips per 100 employees range will see less additional trip reduction than an employer whose start-
ing vehicle trip rate is in the 100-90 range. The same is more-or-less true of higher starting transit
mode shares, as it would appear that many of these strategies may appeal more to potential carpool/
vanpool users than to potential transit users, and may even begin to lure some employees away
from transit. At the extreme, the effect of the given TDM program may even be to begin increasing
vehicle trip making through non-optimal modal shifts. Such results are reflected in the non-negative
vehicle trip rate change entries in Table 19-32.
The results shown in Table 19-32 have been abstracted from Chapter 5 of the CUTR model report
(CUTR, 2004), and should be taken as illustrative of the model's behavior in forecasting the
effects of typical TDM program packages over common background conditions as might be
encountered in the field. For actual user applications, the model is accessible via the internet at
http://www.nctr.usf.edu/worksite, allowing the user to input his/her own current conditions
and to test combinations of TDM strategies on reducing their vehicle trip rate.
To illustrate the nature and sensitivity of the model, the Handbook authors executed a series of
runs for a hypothetical employer of 100 employees, where as a starting condition 86 percent of
employees drive alone, 2 percent take transit, 10 percent rideshare, and the remaining 2 percent
bike or walk. These conditions correspond to a starting vehicle trip rate of 90.6. Using these con-
ditions, the authors investigated the trip reduction impact which would result from the applica-
tion of each of the 12 primary strategy groups--first individually, and then each paired with each
of the others. The results of this exercise are shown in Table 19-33 in terms of the percentage vehi-
cle trip reduction from the starting vehicle trip rate base of 90.6.
While readers will want to study the results in the table and develop their own conclusions, the
following general observations are offered on the nature of the estimated vehicle trip reduction
(VTR) results for this particular set of strategy conditions:
· The highest-impact strategies are the commuter tax benefit incentives (5.0 percent VTR), finan-
cial incentives (4.1 percent), and facilities and amenities (4.1 percent). The lowest-impact were
guaranteed ride home (1.5 percent VTR) and non-financial incentives (1.3 percent), while
rideshare matching (-0.3 percent) and on-site services (-0.7 percent) were projected to actually
increase vehicle trips for this particular set of starting conditions.
19-117
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Table 19-33 Vehicle Trip Reduction Estimates for TDM Programs and Combinations Based on CUTR TDM Model
VTR Estimated Starting with a Base of 90.6 Vehicle Trips per 100 Employees
TDM Program Type F&A GRH FLEX MRKT RSMP FIN PMT TELE CWW ONS NONF CTB
Facilities & Amenities 4.1%
Guaranteed Ride Home 3.1% 1.5%
Flexible Schedules 3.5% 2.1% 2.5%
Marketing 3.3% 3.2% 3.4% 3.5%
Rideshare Matching 0.8% -0.9% 0.1% 2.0% -0.3%
Financial Incentives 4.9% 3.7% 4.0% 4.9% 1.1% 4.1%
Parking Management 4.2% 1.7% 2.8% 4.5% 0.2% 4.8% 3.0%
19-118
Telecommuting 3.5% 1.8% 1.7% 2.4% -0.4% 2.3% 2.1% 1.9%
Comp. Work Week 5.7% 1.8% 2.9% 1.7% 0.8% 3.2% 2.9% 2.5% 3.8%
On-Site Services 2.1% -0.8% 1.1% 1.3% -3.2% 1.6% -0.3% 0.4% -0.1% -0.7%
Non-Financial Incent's. 3.1% 1.0% 1.3% 2.6% -1.5% 3.0% 2.3% 0.1% 1.3% -1.3% 1.3%
Commuter Tax Benefit 6.8% 3.6% 4.3% 2.7% 2.4% 4.7% 4.4% 4.2% 7.0% 2.6% 2.4% 5.0%
Incentives
Notes: Hypothetical employer of 100 employees, with starting employee mode shares of 86 percent drive alone, 10 percent rideshare, 2 percent transit,
and 2 percent bike or walk (90.6 Vehicle Trips per 100 Employees).
A negative value indicates that the program or combination will actually increase vehicle travel under the conditions assumed.
Source: Illustrative model application prepared by the Handbook authors utilizing the CUTR Worksite Trip Reduction Model available at
http://www.nctr.usf.edu/worksite (now superseded -- see Footnote 14). Center for Urban Transportation Research. (Website accessed
Winter/Spring 2007.)
