# 2.0 Overview of STEAM {#overview-of-steam .unnumbered}

In 1995, the Federal Highway Administration (FHWA) developed a corridor
sketch planning tool called the Sketch Planning Analysis Spreadsheet
Model (SPASM) to assist planners in developing economic efficiency and
other evaluative information needed for comparing cross-modal and demand
management strategies[^1]. When more detailed analysis is required,
however, SPASM cannot be used directly, owing to several simplifying
assumptions. For example, all trips are assumed to be of an average trip
length, made between the two ends of the corridor. Also, it is difficult
to use SPASM for system wide analysis. To allow more detailed analysis,
FHWA has developed an enhanced version of SPASM, called the Surface
Transportation Efficiency Analysis Model (STEAM).

There are several significant improvements in STEAM. First, the software
accepts input directly from the four step travel demand modeling process
or from off-model software such as FHWA's TDM software[^2]. Second, it
post-processes traffic assignment outputs from conventional four-step
planning models, in order to get more accurate highway travel speeds,
especially under congested conditions. Third, it performs risk analysis
to clearly describe the level of uncertainty in the results of the
analysis, so that the debate can shift from unproductive technical
controversy to compromise and action.

The software is based on the principles of economic analysis, and allows
development of monetized impact estimates for a wide range of
transportation investments and policies, including major capital
projects, pricing and travel demand management (TDM). Impact measures
are monetized to the extent feasible, but quantitative estimates of
natural resource usage (i.e., energy consumption) and environmental
impacts (i.e., emissions) are also provided. Net monetary benefits (or
costs) of alternatives can then be used to evaluate trade-offs against
non-monetizable benefits, including sustainability and community
livability.

STEAM is highly flexible in terms of transportation modes, trip
purposes, and time periods analyzed. It provides default analysis
parameters for seven modes (auto, truck, carpool, local bus, express
bus, light rail, and heavy rail) and allows the user to deal with
special circumstances or new modes by modifying these parameters. The
user also provides Base Case and Improvement Case trip tables for
different trip purposes, which will be analyzed separately by the model.
Regarding time periods, STEAM can be applied to average weekday traffic
or to peak and off-peak traffic with different definitions of the peak
periods.

##  2.2 Objectives of the System

The primary objectives of STEAM are to provide a framework for
estimating impacts of multimodal transportation alternatives and
assessing their overall merits.

The STEAM analysis provides estimates of the following measures of
effectiveness for proposed actions:

-   Benefits and costs to transportation users

-   Annualized cost to public agencies

-   Effect on total transportation cost

-   Change in emissions for particulates, hydrocarbons, carbon monoxide,
    and nitrogen oxides

-   Change in energy use

-   Change in noise and other external costs

-   Change in fatal, injury, and property damage only accidents

-   Revenue transfers due to toll or fare changes, and due to changes in
    fuel tax revenues as a result of fuel consumption changes.

##  2.3 Overall Structure of the Software

As shown in Exhibit 2.1, STEAM consists of 4 modules:

1.  A User Interface Module, which includes on-line help files;

2.  A Highway Network Analysis Module, which reads a file containing
    vehicular traffic volumes, highway segment lengths, highway
    capacities, and other link data and produces zone-to-zone travel
    times and distances based on minimum time paths through the network.

3.  A Trip Table Analysis Module, which produces estimates of user
    benefits based on a comparison of Base Case and Improvement Case
    conditions. It also produces estimates of emissions, noise costs,
    accident costs, energy consumption, and other external costs
    associated with highway use.

4.  An Evaluation Summary Module, which calculates net annual worth and
    a benefit-cost ratio for the improvement under consideration. It
    also provides summary information on individual benefit and cost
    items.

##   {#section .unnumbered}

##  2.4 Transportation Analysis Analytical Procedures

This section describes the procedures used by STEAM for:

-   estimating user benefits

-   congestion analysis

-   analysis of accident costs

-   emissions analysis

-   fuel consumption analysis

-   analysis of other external costs

-   analysis of capital costs

-   estimating revenue transfers

-   risk analysis

### Estimation of User Benefits

The analysis of user benefits in STEAM focuses on how proposed
improvements affect travel conditions for a set of "market sectors"
defined by the model user. Market sectors can be defined to represent
different modes, trip purposes, and time periods, e.g., peak period auto
work trips.

For each market sector to be analyzed, the STEAM user provides a Base
Case and an Improvement Case trip table, specifying the number of trips
from each analysis zone to other zones. User benefits will be calculated
for each zone pair and then summed over all zone pairs. Zone-to-zone
travel times for the Base Case and Improvement Case for highway modes
are estimated by STEAM's network analysis module, using the speed-volume
relationships described below (under Congestion Analysis). Motor vehicle
operating costs are calculated as the sum of fuel and non-fuel costs by
applying unit (per vehicle mile) fuel consumption rates (as a function
of speed) and costs to zone-to-zone distances. Optionally, these costs
may be included under the "external" cost category. As discussed later,
user-perceived accident costs (as defined by the software user) are also
used in the estimation of user benefits. The STEAM user can also specify
other changes in zone-to-zone travel times (in-vehicle and walk/wait)
and out-of-pocket costs for each zone pair. The difference in the total
cost of travel for each zone pair is calculated as the sum of changes in
out-of-pocket cost, operating cost, user-perceived accident costs, and
the dollar value of changes in travel time. Motor vehicle operating
costs and other out-of-pocket costs for highway modes are assumed to be
specified on a per vehicle basis, and are converted to a per person
basis by dividing by vehicle occupancy. Transit out-of-pocket costs are
assumed to be specified on a per person basis.

STEAM uses the concept of "consumer surplus" to measure user
benefits(see Exhibit 2.2). User benefits(B) are calculated as follows:

B = (P~b~ - P~i~ )(T~b~ + T~i~ ) / 2

where P~b~ and P~i~ are price per trip and T~b~ and T~i~ are the number
of trips in the Base and Improvement Cases. In Exhibit 1, the
rectangular area represents benefits to current users and the triangular
area represents benefits to new users.

STEAM calculates benefits for a specific analysis year by comparing Base
Case and Improvement Case conditions for that year. Capital costs are
annualized so that they can be related to analysis year benefits in a
benefit-cost ratio. If conditions are expected to vary greatly over the
life of the proposed improvements (due to, for example, growth in
traffic over time), it may be appropriate to perform several STEAM
analyses (representing different analysis years) and average the
results. Alternatively, based on the various analysis year results, a
stream of annual benefits can be estimated, and these benefits can be
converted to a present value of the cost stream.

### Congestion Analysis

Cambridge Systematics has developed new speed models for use in STEAM
(see Exhibit 2.3). The models are designed to be used as part of a
post-processor of average weekday traffic assignments. They produce
peak, off-peak, and average weekday speeds for

freeways and arterials as a function of (1) free-flow speed, (2) average
weekday traffic, and (3) capacity (in vehicles per hour).

The new models were developed by conducting hour-by-hour simulations of
traffic volumes and queuing for facilities with different levels of
congestion. The simulation program kept track of the growth and
dissipation of queues over the course of the day. Queuing was assumed to
occur if the volume attempting to use the facility exceeded its
capacity, or if there was a standing queue at the end of the preceding
hour. The hour-by-hour results were then combined to produce average
speeds for a six-hour peak period (7-10 AM and 4-7 PM), a 18-hour
off-peak period, and an average weekday. Finally, sets of curves were
fit to simulation outputs, producing the results shown in Exhibit 2.3.

As discussed in the following paragraphs, the new speed models have a
number of features that make them especially useful in the evaluation of
transportation system improvements.

-   The models account for delays due to incidents, using data on the
    frequency, severity, and duration of incidents compiled by Ball
    Systems Engineering[^3]. Incidents account for a large share of
    total travel delays due to congestion, especially on freeways. For
    example, Lindley[^4] estimates that over 60 percent of the
    congestion delay experienced on urban freeways is due to incidents
    rather than recurring congestion. Hence, ignoring incidents could
    grossly understate actual congestion delays.

-   The models account for peak spreading that occurs when facilities
    become more congested. The traffic temporal distributions used in
    developing the models were based on data from 579 urban automatic
    traffic recorders (ATRs) across the nation[^5]. Separate temporal
    distributions were developed for freeways and arterials with low,
    moderate, and high ratios of average daily traffic to capacity.

-   The models account for day-to-day variations in traffic. The
    relationship between delays due to congestion and traffic volumes
    are highly non-linear in nature, especially when the ratio of demand
    volume to capacity is close to 1.0. Hence, using average volumes,
    rather than explicitly accounting for day-to-day variations in
    traffic volumes, can result in a significant overestimate of average
    speeds. To address this problem, the new speed models were developed
    using Monte Carlo simulation of traffic volumes, based on estimates
    of day-to-day variations in traffic compiled from urban ATR counts
    by SAIC.

**Exhibit 2.3**

**STEAM Speed Models**

$S = \frac{1}{\frac{1}{F} + D}$

$D = c_{1}x^{c_{2}}e^{c_{3}x}$

for x \<= c~0~

$$D = c_{4}\left( 1 - c_{5}x^{c_{6}}e^{c_{7}x} \right)$$

for x \> c~0~

where

-   S is average speed in miles per hour

-   F is free-flow speed[^6] in miles per hour.

-   D is congestion delay in hours per vehicle mile

-   x is the ratio of average weekday traffic to hourly capacity for the
    section (AWDT/C)

-   c~0~ to c~7~ are constants, given in the following table.

  --------- ----------- ----------- ----------- -- ------------ ----------- -----------
            Freeways                               Signalized               
                                                   Arterials                

            Daily       Peak        Off-Peak       Daily        Peak        Off-Peak

  C0        1.05E+01    1.21E+01    1.11E+01       9.74E+00     9.62E+00    1.26E+01

  C1        2.39E-08    2.35E-07    1.13E-07       5.62E-04     8.44E-04    4.35E-04

  C2        3.75E+00    3.29E+00    2.52E+00       8.62E-01     6.15E-01    9.37E-01

  C3        2.87E-01    2.35E-01    2.59E-01       7.39E-02     1.24E-01    5.16E-02

  C4        5.00E-02    5.00E-02    5.00E-02       1.66E-01     1.66E-01    1.66E-01

  C5        1.494E-02   2.865E-04   1.058E-03      1.313E-01    8.591E-03   1.177E-02

  C6        3.42E+00    7.00E+00    4.91E+00       1.61E+00     3.80E+00    2.91E+00

  C7        -3.72E-01   -7.97E-01   -4.49E-01      -1.73E-01    -4.07E-01   -2.37E-01
  --------- ----------- ----------- ----------- -- ------------ ----------- -----------

-   The models account for the decrease in highway capacity that occurs
    after demand volumes exceed capacity. The 1994 *Highway Capacity
    Manual* notes that observations of freeway queue departure rates
    range from 1,500 to 2,000 passenger cars per hour per lane. In
    contrast, freeway capacities for 12 foot lanes with no lateral
    obstructions are 2,200 to 2,300 passenger cars per hour per lane.
    Not accounting for the fact that queue departure rates are generally
    lower than freeway capacities can result in a large understatement
    of the delays due to queuing.

### Analysis of Crash Costs

STEAM applies per vehicle mile rates and unit costs for fatal, injury,
and property damage only crashes. It includes default rates for
limited-access and non-limited-access highways. Alternatively, STEAM
users can specify crash rates for up to six user-defined highway
classes.

STEAM allows the specification of "internal" and "external" unit costs
for fatal, injury, and property damage only crashes. "Internal" costs
are viewed as incident upon and perceived by highway users and are
treated in the same way as travel time and out-of-pocket costs to
highway users in the benefit-cost analysis. "External" costs are viewed
as incident upon other elements of society, or incident on the user and
not recognized by him or her, and are treated the same way as emissions,
noise, and global warming costs in the benefit-cost analysis (i.e., it
is assumed that these costs are not taken into account in traveler
decisions).

### Emissions Analysis

In STEAM, emissions for autos, trucks, and carpools are calculated as
the sum of (1) mileage-based emissions on the highway system (calculated
under the assumption that vehicles are already warmed up) and (2) added
emissions due to cold starts.

Mileage-based emissions are calculated using emission rates as a
function of speed. Specifically, emission rates are input for speeds of
5, 10, 15, \..., 60, and 65 miles per hour, and the spreadsheet
interpolates to get emission rates at intermediate speeds. The average
speeds are trip-based (i.e., calculated as origin to destination
distance divided by origin to destination mileage). The added emissions
due to cold starts are calculated on a per vehicle trip basis and
combined with mileage-based emissions. Appendix D describes how the
default emission rates (for 2010) were computed. The STEAM user should
use Appendix D methodology to estimate local emission rates.

Transit emissions are calculated by applying emission rates to changes
in transit vehicle miles specified in the inputs to STEAM.

### Analysis of Other External Costs

At the user's option, STEAM can treat fuel consumption and vehicle
operating costs as "external" costs, i.e., costs not recognized by
highway users. In this case, external costs will be computed differently
than with the consumer surplus approach. External cost changes are
computed as the difference between total costs for fuel or vehicle
operation under the Base Case and the Improvement Case. With reference
to Exhibit 2-2, the change is computed not as a trapezoid (i.e., the
consumer surplus) but as the difference between the areas of the
rectangles whose diagonals are P1-V1 and P2-V2. STEAM's global warming
cost estimates are based on carbon dioxide emission estimates calculated
from fuel consumption estimates. STEAM applies per vehicle mile unit
costs for noise, and other external costs. STEAM inputs also include an
estimate of external costs incurred during the construction period.
These costs are annualized in the same way as capital costs (discussed
below).

### Revenue Transfers

STEAM calculates revenue transfers occurring as a result of changes in
fares, tolls, and other out-of-pocket costs and fuel taxes paid by
transportation system users Increases in fares and tolls implemented as
part of a package of transportation system improvements reduce the
benefits of the improvements to system users. If the fare and toll
increases are very large, the net effect of the package on
transportation system users may be negative. However, fare and toll
increases do not, in themselves, necessarily result in a loss to society
as a whole, since they cause revenues to be transferred from system
users to the agencies that collect tolls and fares.

Conversely, decreases in fares and tolls increase benefits to users
because revenues are being transferred to them from the collecting
agencies. Hence, in calculating the net benefits of a package of
actions, revenue transfers to or from collecting agencies must be added
to user benefits to capture the full effect of the package of actions on
society.

STEAM calculates revenue transfers at the zonal interchange level for
each market sector . The equation used to calculate revenue transfer
(RT) is as follows:

RT = (OPTC~i~) (T~i~ ) - (OPTC~b~) (T~b~)

where T~i~ and T~b~ are the number of trips in the Improvement and Base
Cases and OPTCi and OPTCb are the out-of-pocket costs in the Base and
Improvement Cases.

In addition to the revenue transfers which occur as a result of fares
and tolls and other out-of-pocket costs paid by users, revenue transfers
occur as a result of changes in motor fuel taxes collected. If
additional motor fuel is consumed in an improvement case versus a base
case, transportation users are paying additional costs for the motor
fuel. However, some of the additional cost for fuel is State and Federal
tax which is simply a revenue transfer from transportation users to
government agencies. The STEAM model calculates the amount of revenue
transfer resulting from changes in motor fuel consumption based on an
average State and Federal motor fuel tax rate. The combined State and
Federal tax rates for gasoline is 37.48 c/gal and 42.6 for diesel.

Revenue transfer changes due to transit ridership changes must be
computed separately by the software user, based on ridership changes.
STEAM does not compute these changes currently.

### Analysis of Capital Costs

STEAM estimates benefits and costs for an analysis year. To determine
capital costs for the analysis year, total capital costs are converted
into a stream of annual costs starting in the year of opening and
extending over the useful life of the project. For simplicity in this
conversion, all capital costs are viewed as incurred in the year entered
by the spreadsheet user as the midpoint of the construction period. The
equation used to annualize capital costs is:

where:

> C is capital cost;
>
> y~o~ is the year of opening;
>
> y~m~ is the midpoint of the construction period;
>
> L is the useful life of the investment in years;
>
> d is the discount rate expressed as a fraction (e.g., 0.07); and
>
> a is capital cost annualized over the useful life of the investment.

### Risk Analysis

Uncertainty is a key attribute of both the physical and economic
components of transportation user costs. Risk analysis is useful to
reflect both what is known and what is uncertain in the effect of
transportation conditions and performance on user costs. Risk analysis
allows the planner to evaluate alternative investment options under a
variety of scenarios. The result of the risk analysis is a forecast of
future outcomes and the probability, or odds, of their occurrence. Risk
analysis provides the STEAM user with a sense of perspective on the
likelihood of future outcomes and a process which is easily
understandable and technically robust. This allows planners and
decision-makers to select the level of risk within which they are
willing to plan and make commitments.

The risk analysis process helps avoid the lack of perspective in
\"high\" and \"low\" cases used in sensitivity analysis by measuring the
probability or \"odds\" that an outcome will actually materialize. This
is accomplished by attaching ranges (probability distributions) to the
forecasts of each input variable. The approach allows all inputs to be
varied simultaneously within their distributions, thus avoiding the
problems inherent in conventional sensitivity analysis. The approach
also recognizes interrelationships between variables and their
associated probability distributions.

Probability distributions for the variables in-question are established
on the basis of both statistical analysis and subjective probability. In
the current version of STEAM, the variables subject to risk analysis are
assumed to follow a log-normal probability distribution. Future versions
of STEAM will provide more flexibility in selecting probability
distributions. Ranges need not be normal or symmetrical \-- that is,
there is no need to assume the bell shaped normal probability curve. The
bell curve assumes an equal likelihood of being too low and being too
high in forecasting a particular value. It might well be, for example,
that if projected inflation rates deviate from expectations, they are
more likely to be higher rather than lower. The software places no
restrictions on the degree of \"skew\" in the specified ranges and thus
maximizes the extent to which the risk analysis reflects reality.

Although the computer program will transform all ranges into formal
\"probability density functions\", they do not have to be determined or
presented in either mathematical or graphical form. All that is required
is the entry of upper and lower limits of an 90 percent confidence
interval. The software will then use numerical analysis to translate
these entries into a uniquely defined statistical probability
distribution automatically. This liberates the non-statistician from the
need to appreciate the abstract statistical depiction of probability and
thus enables administrators, stakeholders and decision-makers to
understand and participate in the process whether or not they possess
statistical training.

The model uses a monte carlo simulation process to generate the
distributions. During the simulation process, the unique probability
density function for each variable is sampled, and the resulting numbers
populate the mathematical equations making up the model. Values for each
result metric are calculated at the end of each simulation. After
repeated samplings the model generates a probability distribution for
each result metric. For each result there is both a forecast and a
quantification of the probability that the forecast will be achieved.

[^1]:

[^2]:

[^3]:
    > Edward Sullivan, Sam Taft, and James Daly; *A Methodology for
    > Measurement and Reporting of Incidents and the Prediction of
    > Incident Impacts on Freeways*; submitted to Federal Highway
    > Administration by Ball Systems Engineering Division; April 1995.

[^4]:
    > Jeffrey A. Lindley; "Urban Freeway Congestion: Quantification of
    > the Problem and Effectiveness of Potential Solutions"; *ITE
    > Journal*; January 1987.

[^5]:
    > Science Applications International Corporation and Cambridge
    > Systematics, *Roadway Usage Patterns: Urban Case Studies*;
    > prepared for Volpe National Transportation Systems Center and
    > Federal Highway Administration; June 9, 1994.

[^6]:
    > Free-flow speed is that which occurs when traffic volumes are very
    > low. On interrupted flow facilities, they include delays due to
    > traffic control devices but exclude any congestion-related delays.