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VI. PROPOSED MODEL ENHANCEMENTS

A. OVERVIEW

This section suggests VDANL and TWOPAS model enhancements that will improve the IHSDM

design process. These enhancements relate to the characteristics of heavy vehicles (i.e. beyond light

passenger vehicles) which are marginally powered for maintaining speed on upgrades, and may have

brake overheating problems on long downgrades. The power-to-weight ratio and gearing of a given

vehicle will determine the steady speed it can maintain on given upgrades. On downgrades, vehicles must

dissipate the change in potential energy due to decreasing altitude through a combination of aerodynamic

and rolling drag, engine braking, wheel brakes and retarders. Vehicles that experience brake overheating

may lose control over speed and the driver may then have to take advantage of available escape ramps.

Speed selection for downgrades depends on grade length, steepness and vehicle weight as discussed in

Appendix E.

A mathematical model for driver speed and gear selection on downgrades has been

developed and validated for 18 wheel tractor/trailer rigs as developed in References [4] and [5] and

summarized in Appendix E. Neither VDANL nor TWOPAS currently have such a speed selection model,

and this is an area of commonality of modeling that would greatly benefit both programs. The suggested

model enhancements include enhancements that are necessary to the upgrade and downgrade analysis

software described in Appendices C and D, as well as enhancements that are not necessary to the

IHSDIM software, but will improve the accuracy and utility of the models. Appendices C and D specify

which improvements are essential to the upgrade and downgrade software.

B. VDANL

1. Wheel Brake Systems

Section M in Appendix A of reference [2] describes the VDANL_IHSDM braking model. The

VDANL_IHSDM model takes a composite approach to the entire brake system. Rather than model each

component of the system individually, the entire system is modeled as a whole, and the model parameters

describe the overall brake system performance.

For vehicles with air brake systems, brake torque is set as a linear proportion of brake pedal force.

Two gain terms are used, and control the brake-torque-to-pedal-force ratio for wheels on the steer axle,

and the drive and trailer axles. The assumption is made that the brake gain for the drive and trailer axles

are the same.

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VDANL_IHSDM models the dynamics of the brake-torque-to-pedal-force ratio using separate first

order times constants for the steer axle, the drive axle(s), and the trailer axle(s).

Vehicle brake adjustment is modeled in VDANL_IHSDM using the brake torque multiplier

parameters to reduce the brake torque gain for a particular wheel. This is a per-wheel brake torque

multiplier that can be used to increase or decrease the brake torque for each wheel individually above or

below what is computed from the basic brake model. For brake system adjustment modeling, this can be

used to have an equivalent effect at a particular pressure, but will not model the behavior at other

pressures without changing the torque multiplier parameter.

A brake thermal model is included, which is based on the model developed in [4]. The model uses

an energy balance equation for the brakes on each axle. The energy balance equation is of the form:

Rate of conversion

RateRate

F of change of I F of mechanical I F of heat IGJ− G fromJ

G energy J= Genergy to heat J G

internaltransfer

G brake system J G

Hin

K Gin brake system J H system J

J brake K

In VDANL_IHSDM, the model assumes that both brakes on the steer axle are at the same

temperature, and that all brakes on the drive and trailer axles are at the same temperature. All of the

parameters for the thermal model are hard coded in the program, and are taken from reference [4].

VDANL_IHSDM contains a brake fade model which linearly reduces brake torque based on the brake

temperature above 90 degrees F (assumed to be the ambient temperature). There is a single parameter for

all brakes that sets the reduction in brake torque.

2. Rolling and Aerodynamic Drag

VDANL needs additional drag terms to complete the total drag (Fdrag) formula given earlier, and

possible additional terms that prove to be significant in recent SAE RR (tire rolling resistance)

expressions. This may require the addition of terms associated with inverse and linear forward velocity

and the product of weight and velocity. A tire pressure term may be important for passenger vehicles, but

may not be as critical for heavy trucks. Additional research will be required to determine the most

significant terms to be included. This research will require data on modern long haul trucks as discussed

further on. The treatment of the inverse velocity term as velocity approaches zero also needs some

additional work to determine the zero speed limit value of this term. VDANL currently has a simple drag

term that is proportional to weight, and an aerodynamic term that is a function of the square of forward

velocity.

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3. Engine Braking Systems and Retarders

The VDANL_IHSDM engine drag model is sufficient to model the retarding force from an engine

with no exhaust flap and/or throttle valve. A separate model should be added to the engine model that

computes the retarding force from an engine braking system. This empirical model should be of the same

form as the engine drag model; however, it should be turned on/off by the driver model or a user-

specified control file. The equation for the engine braking system should be:

2Tbrake = K E7 + K E8 ù E + K E9 ù E

where the K EL are polynomial coefficients and ùE is engine speed in rad/sec. Tbrake should be added to

engine torque, TE, when the engine brake system is activated.

Modeling of electrodynamic or hydrodynamic retarders can be broken down into four issues. First,

what is the form of the retarders torque versus input shaft speed relationship. Secondly, what is the effect

of temperature on this relationship and how should the temperature be computed/modeled. Third, where

is the retarder installed: between the engine and transmission or between the transmission and drive axle.

This will determine the input speed for the retarder and where the retarder torque should be applied. The

final issue is how is the retarder controlled: by the driver model, or by a user-supplied control file.

The literature review indicates that the torque/speed curves of both types of retarders are similar to

those shown in Figure 20. For the purposes of IHSDM, modeling the exact shape of these curves is not

critical, and a simple empirical model can be used. This will keep the number of parameters in the

VDANL_IHSDM data set manageable. The curves in Figure 20 were generated using an equation of the

form:

Tretarder = Tmax 1 − e − Sretarderù R

(

)

where Tmax is the maximum retarder torque, ùR, is the input shaft speed in rad/sec, and Sretarder is the

shaping parameter that determines how quickly the retarder reaches full torque. It is proposed that this

empirical model be used in VDANL_IHSDM.

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120

100

Horsepower Drag (% Max)

Cold

Hot

4060

Shaft Speed (% Max)

100

Figure 20. Generic Retarder Horsepower Dissipation Both Hot and Cold

The thermal model for the retarder is of the same formulation as the brake system thermal model.

The proposed brake thermal model for each will use the following equations:

mR CR

∂TR

= HPR − hR AR ( TR − T∞ )

∂t

where:

HPR

T∞

Effective mass of the retarder (lbm)

Effective specific heat capacity of the retarder (Btu/lbm-°F)

Temperature of retarder (°F)

= Power input into the retarder (Btu/sec)

= Effective heat transfer coefficient of the retarder (Btu/sec-ft2-°F)

Effective surface area of the retarder (ft2)

Ambient temperature (°F)

The power input into the retarder is the product of retarder torque, Tretarder (ft-lb),

and retarder speed, ùR (rad/sec), given by:

HPR = Tretarderù R 1.285 ⋅ 10−3

æ Btu ö

ç÷

è sec ø

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The effective heat transfer will be modeled as a linear function of vehicle speed, u (ft/sec) by:

æöBtu

hR = K hRo + K hR1 u ç÷2oè sec⋅ ft ⋅ F ø

Where KhRo and KhR1 are model parameters.

is:

TR ( t ) = ò

The model is numerically integrated using the

VDANL_IHSDM integrator in the same way as the brake thermal model. The equation to be integrated

HPR − hR AR ( TR − T∞ )

mR C R

∂t

The reduction in retarder torque as its temperature increases is an important issue. A linear reduction

in torque with temperature rise is proposed. Using the same form as the brake fade model, the proposed

model is:

Tretarder = Tretarder 1 − K Rfade TR − T∞

Where KRfade is the coefficient that controls the reduction in retarder torque with temperature rise.

The retarder input speed and output torque are treated differently for Primary and Secondary

retarders. For Primary retarders, mounted between the engine and transmission, the retarder input speed,

ùR, is set equal to the transmission input speed, ùC. The output torque, Tretarder, is added to the

transmission input torque, TC.

For Secondary retarders, mounted between the transmission and

differential, the retarder input speed, ùR, is set equal to the transmission output speed, ùT. The output

torque, Tretarder, is added to the transmission output torque, TT.

4. Engine

The basic form of the VDANL_IHSDM engine model is appropriate for modeling truck upgrade and

downgrade performance. There are some engine torque nonlinearity’s in actual engine performance that

can not be accounted for in VDANL_IHSDM’s empirical engine torque function. To overcome this

limitation, it is proposed that the throttle position in the engine torque equation be made a nonlinear

function of the throttle specified by the driver model. The proposed equations for engine torque are:

è =1− e

− KT èT T2

TE = K E1 + K E2 ù E + K E3 ù E è T' + K E4 + K E5 ù E + K E6 ù E 1 − è T' )

(

)

(

)(

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where è 'T is the adjusted throttle used in the engine torque equation and KT1 and KT2 are shaping

coefficients for the adjusted throttle versus throttle input function. To maintain compatibility with

existing VDANL_IHSDM data sets, if KT1 and KT2 are not specified in a parameter set, then è 'T should be

set equal to è T and the engine torque will change linearly with throttle input at a constant engine speed.

5. Transmission and Differentials

Heavy truck transmissions often have a high and low range, and a five speed transmission with two

ranges would have ten forward gear ratios. Some truck transmission options (e.g. offered by Peterbilt for

Fuller and Rockwell transmissions, http://www.peterbilt.com/pb/trukfram.htm) allow for 9, 10, 13, 15 and 18 gears.

For the purposes of VDANL_IHSDM, there is no difference between a ten speed transmission and a five

speed with two ranges. Therefore, there is no need to add multiple ranges to the transmission model.

However, the current limit of eleven forward gears may be insufficient for some trucks.

It is

recommended that the upper dimension of the transmission variables be changed from eleven to twenty.

If it is desired to allow secondary retarders to be simulated on trucks with multiple speed

differentials, then VDANL_IHSDM must be upgraded to allow multiple speed differentials. Parameters

KDF and KDB are the front and rear differential ratios (lines 2 and 3 of the drivetrain parameter file). KDF

and KDB will need to be changed from single variables to arrays and multiple ratios will be specified.

Logic for changing differential ratio will have to be added to the driver model (described in section of

driver modeling).

6. Downgrade Speed Control and Gear Selection Driver Model

Both VDANL and TWOPAS can use a downgrade speed selection model as defined in [4] and [5]

that give the complete equations and tips for applications. The equations are quite nonlinear, and will

require a solution procedure to be added as discussed in Appendix E. The inputs to the solution

procedure will be the vehicle type and weight and grade description. Figure 21 gives example maximum

speeds for an 80,000 lb five-axle truck in terms of a simple description of length and percent grade. It

should be noted that the implementation of this grade severity speed selection model would also provide a

direct measure of grade severity. As noted earlier, the slope of the constant grade lines in Figure 21 is a

direct indication of grade severity. The speed selection model can be implemented to provide this slope

as an indication of grade severity:

Grade Severity Metric =

dVmax

grade = const .

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Figure 21. Maximum Safe Downgrade Speed for Five Axle Trucks with an 80,000 lb Load

(Adapted from Ref. 6)

This metric is a direct measure of the maximum descent speed sensivity to length. Grade severity is

directly related to this metric, and a grade with a smaller absolute value would be a less severe grade.

The procedures for determining maximum speeds for multiple grade hills are discussed in detail in

[4] and [5] and summarized in Appendix E. Different parameter sets are required for vehicles other than

five-axle trucks, and will require future research. The effect of a retarder should be accounted for as an

equivalent wight decrease [6]:

∆W

375 • ∆HPR

è •V

where

∆HPR is the horsepower absorbed by the retarder, è is the grade slope and V is vehicle speed.

This procedure will have to be expanded for multiple grade hills along with the speed selection algorithm.

For downgrade descents VDANL will also need logic for gear selection that will result in maximum

engine RPM (i.e. maximum engine braking) at the desired maximum speed.

7. User Interface

The user interface is critical for programs that will be used as applications by users not familiar with

their intricacies and underlying theory. The program interface should allow the user to select reasonable

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operating conditions through menus that clearly present meaningful options necessary for desired

analyses. Results from running the program should also be presented in a clear, meaningful fashion.

VDANL currently allows roadway design analysis through selection of one of twelve AASHTO

vehicles, a specified speed profile or a speed limit and desired cornering acceleration. Output options

allow the user to produce vehicle performance plots as a function of the roadway design station. Vehicle

performance measures include lateral acceleration, lateral lane position and variables directly related to

rollover including roll angle and lateral load transfer. A series of roadway safety metrics and station of

occurrence are also available including the maximum values of friction demand, roll angle, lateral load

transfer and lateral acceleration.

Given the enhancements discussed in this report, the specification of additional input options will be

required. These options will involve vehicle performance including horsepower, weight and retarder

availability. The options could be expressed as standard vehicle configurations such as are currently

defined for TWOPAS, or alternately the specific operating conditions could be individually specified.

Additional output options should include brake temperature which is directly related to downgrade

descent safety.

TWOPAS

Five recommended enhancements to the TWOPAS model have been identified to make the model a

more useful tool in evaluating traffic operations on upgrades and downgrades. These enhancements are:

Upgrades

•

Increase the number of truck types or permit specification of a range of

truck weight-to-power ratios for each truck type

•

Increase trucks speeds on approaches to upgrades

Update basic parameters in truck performance equations

Downgrades

•

Automate determination of crawl zone locations and crawl speeds of

specific trucks

•

Test and, if necessary, improve capability to simulate crawl speeds for RVs

Each of these recommended enhancements is discussed below. The research required to implement these

enhancements is discussed in the next section of the report.

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1. Increase the Number of Truck types or Permit Specification of a Range of Truck Weight to

Power Rations for Each Truck Type

Currently, TWOPAS allows 13 vehicle types to be specified, of which 5 are passenger cars, 4 are

RVs, and 4 are trucks.

The performance of the 4 truck types on grade is simulated using truck

performance equations that are distinctly different from the performance equations used for passenger

cars and RVs. Each truck type has a specified value of weight-to-power ratio and weight-to-frontal-area

ratio. As such, the performance capabilities of trucks are limited to four unique sets of values. On level

terrain, this is generally satisfactory because driver characteristics (e.g., normal desired speed) cover a

range of values, so the actual truck speeds will be spread over a range and are not simply limited to four

values. However, on a modest or steep grade, truck drivers will normally use maximum available power

and may still not be able to maintain their desired speed. In this situation, in the absence of other traffic

or horizontal curves with reduced speeds, all truck speeds will be reduced to precisely four values. This

not sufficiently realistic to permit assessment of upgrade traffic operations.

Figure 22 shows the cumulative distribution of 248 truck crawl speeds measured fairly recently by

MRI on a long 4.37% grade in California as part of NCHRP Project 3-55(3). They ranged from a low of

17 mph to a high of 65 mph, plus one outlier at 71 mph. From the truck speeds, using the TWOPAS truck

performance equations, the weight-to-power ratio can be deduced. Also shown on the figure are the

speeds of the four TWOPAS truck types, with maximum speeds on this grade of 22, 27, 33, and 48 mph,

respectively.

One possible means to enhance the TWOPAS model is to expand the number of truck types beyond

4, to better represent the distribution of truck performances shown in Figure 22. Conceptually, this would

seem to be a fairly simple change to program. However, familiarity with the TWOPAS model suggests

otherwise. There are many variables in the program subscripted by vehicle type (i.e., in arrays with a

dimension of 13) and other variables subscripted by vehicle category (passenger car, RV, and truck)

which can be determined from the vehicle type. Since vehicle types are central to the program, extensive

changes to the program logic would be necessary. This would require a major effort for programming

and debugging to make sure that the additional truck types were implemented without affecting the

operation of the program.

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Figure 22. Cumulative Distribution of Truck Crawl Speeds Measured a Long 4.37% Grade

An alternative approach to achieving the desired result of a greater variation in truck characteristics

would be to assign to each of the four truck types not a single value for weight-to-power, but rather to

assign, for example, a mean and a standard deviation, much as is done with desired speeds. That way,

when a truck is "created" at the beginning of a simulation run, its driver is assigned a desired speed from

the desired speed distribution, and the weight-to-power ratio is assigned from the weight-to-power ratio

distribution for that truck type. Desired speeds are assigned according to the normal distribution. Figure

22 suggests that a normal distribution is not appropriate for the distribution of truck weight-to-power

ratios. Therefore, instead of a mean and standard deviation of weight-to-power ratios, it might be more

desirable simply to specify points on the cumulative distribution curve of weight-to-power ratio for each

truck type.

As noted above, TWOPAS uses not only the weight-to-power ratio, but also the weight-to-frontal-

area ratio, in modeling truck characteristics. Logic would need to be provided so that as each truck is

assigned a weight-to-power ratio, it is assigned a weight-to-frontal-area ratio that is consistent with (or, at

least, not inconsistent with) its weight-to-power ratio.

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If a distribution of truck characteristics is assigned to a truck type, as discussed above, then in theory

it might not be necessary to have four truck types; one could do for many TWOPAS applications.

However, it is recommended that the four truck types be retained because this makes it possible to

evaluate explicitly the traffic operational effects of incorporating unique truck types into the traffic

stream. In other words, if the distribution of existing trucks were to be represented by Truck Type 1, then

Truck Types 2, 3, and 4 could be used to analyze the effects of introducing heavier, lower-powered

trucks, such as turnpike doubles or triples, into the traffic stream.

2. Increase Truck Speeds on Approaches to Upgrades

It is commonly observed that many truck drivers will, as they approach an upgrade, accelerate to a

higher speed than they were using on the level alignment (their desired speed), so as to lessen the amount

of speed decrease on the upgrade. This practice is perhaps more common with shorter grades, as with

longer grades the truck speed will be reduced to a crawl speed anyway. TWOPAS presently does not

model this phenomenon, but it is recommended that this be added to the model. Incorporating this

phenomenon in the model may have some impact on determining where a climbing lane should start, or in

some instances, whether one is even needed.

3. Upgrade Basic Parameters in Truck Performance Equations

The mathematical modeling of truck performance in TWOPAS is believed to be conceptually sound.

However, the model contains a number of numerical parameters such as rolling resistance, aerodynamic

resistance, drive train losses, gear shift delays, and effect of altitude on engine performance, whose values

were established in the mid-1970's based on truck characteristics of that time. In the mid-1980's, some

minor revisions were made. However, with improvements in truck technology, it is possible that some of

these parameters may need adjustments. Therefore, it would be desirable to update these parameters, as

needed, to represent the current truck fleet.

4. Automativc Determination of Crawl Zone Locations and Crawl Speeds for Specific Truck

Types on Downgrades

TWOPAS currently has the capability to simulate trucks operating at crawl speeds on downgrades.

However, the current logic has a number of limitations:

•

The locations of downgrade crawl regions must be specified by the TWOPAS user. The

program lacks the capability to determine for itself which downgrades are long and steep

enough that drivers of heavy vehicles would use crawl speeds.

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•

The TWOPAS user must specify which heavy vehicle types (trucks or RVs) would use

crawl speeds within the specified crawl zones and which would not. The program lacks

the capability to determine for itself which heavy vehicles would need to crawl down

specific grades.

•

The TWOPAS user must specify the distribution of crawl speeds (mean and standard

deviation of an assumed normal distribution) for each individual crawl zone. The mean

and standard deviation of crawl speed can vary from one crawl zone to another, but

within any specific crawl zone all trucks that crawl have crawl speeds drawn from the

same distribution.

To remove these limitations, it is recommended that TWOPAS should be modified to

incorporate logic that evaluates each downgrade on the specified roadway for each type of heavy

vehicle that is present in the traffic stream and determines:

•

whether that vehicle type will crawl down that particular grade and

if so, what crawl speed (or distribution of crawl speeds) will that vehicle type use on that

grade.

The key parameters in making this determination would be the weight of the truck and the length and

steepness of the grade. Past research on grade severity ratings, together with the capability of VDANL to

simulate brake temperatures on downgrades, should provide sufficient data to improve the crawl zone

logic for trucks.

5. Test and, If Necessary, Improve Capability to Simulate Crawl Speeds for RVs

Not only trucks, but RVs (and even, in some extreme conditions, passenger cars) use crawl speeds

on some grades. TWOPAS has the capability to simulate downgrade crawl speeds for RVs and passenger

cars, as well as trucks, but only when the user specifies that particular types of RVs or passenger cars

should use crawl speeds and only when those crawl speeds are specified by the user. However, this logic

for RVs has never been fully tested. Furthermore, unlike trucks, there does not exist any research study

or data base of which we are aware that indicates whether, and at what speed, RVs are likely to crawl on

specific downgrades. Thus, improvement of the crawl zone logic will require substantially greater effort

for RVs than for trucks because field data collection is likely to be required.

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VII. RESEARCH REQUIREMENTS FOR MODEL ENHANCEMENTS

A. VDANL

A range of vehicle parameters are needed for the VDANL enhancements proposed herein as

summarized below:

1. Engine and Transmission Characteristics

A survey of modern engine and transmission characteristics would be appropriate to account for

recent trends in increased horsepower (this data is not ordinarily reported in the open literature). This

effort would probably require soliciting truck and engine manufacturers and organizations such as the

American Trucking Association.

Drag Modeling

Modern trucks also have improved drag properties, including aerodynamics and tires. Figure 23 gives

some reasonable data for aerodynamic coefficients. Rolling resistance is the area most in need of data for

modern vehicles. This data can be obtained with roll down tests at various loads. Data can be collected

with a speed sensor and longitudinal accelerometer. Data acquisition can be easily provided by a laptop

computer. Tire manufacturers also should be solicited for rolling drag data.

Figure 23. Aerodynamic Drag Coefficients for Various European Vehicle Designs

(Adapted from Ref. 45)

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Brake Thermodynamics

Braking thermodynamics are a key factor in overheating and fade which lead to runaways.

Thermodynamic tests can be conducted by instrumenting brake lining material with thermocouples or

using non-contact pyrometers. Additional instrumentation would include speed sensors and longitudinal

accelerometers. Coast down and downgrade braking tests are then performed as discussed in [4] and

Appendix E. Data acquisition can easily be provided with a laptop computer.

B. TWOPAS

This section discusses the research required for the five TWOPAS model enhancements identified

previously. Each individual enhancement is discussed below.

Increase the Numbers of Truck types or Permit Specification of a Range of Truck Weight-to-

Power Ratios for Each Truck Type

The recommended change to the model logic is to introduce an option for the user to specify a

distribution of weight-to-power ratios for a specific truck type rather than a single-value of weight-to-

power ratio. The weight-to-power ratio of each truck would then be generated randomly from that

distribution as each truck is “created” at the beginning of a simulation run. The development of program

logic to accomplish this is relatively straightforward and can be accomplished without additional field

data collection.

One issue that must be addressed is how the weight-to-frontal-area ratios of trucks would be

determined if the logic for assigning weight-to-power ratios is changed. The weight-to-frontal-area ratio

is important in modeling the effect of aerodynamic drag on truck performance. If weight-to-power ratio

for a specific vehicle type is represented by a distribution of values (which might cover a very broad

range), it would not be reasonable to retain a single value of weight-to-frontal area ratio for that vehicle

type. Two options are available:

•

Develop a “rule of thumb” for estimating the weight-to-frontal-area ratio for the value of

the weight-to-power ratio.

•

Specify a distribution of weight-to-frontal-area ratios for each vehicle type, just as the

distribution of weight-to-power ratios is specified. Use the same random number to

select both the weight-to-power ratio and the weight-to-frontal-area for each individual

truck. This will assure that the both the weight-to-power ratio and the weight-to-frontal-

area ratio for each truck represent the same percentile of their respective distributions.

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Each of these approaches would require collection of additional field data to implement successfully

because the available data on weight-to-frontal-area ratios for trucks are limited. It is recommended that

additional field studies like those used to develop Figure 22 be performed and that the data be used to

develop corresponding default distributions for weight-to-power ratio and weight-to-frontal area ratio.

One portion of the TWOPAS logic would have to be enhanced if distributions of truck

characteristics were implemented. It deals with passing on an upgrade. Current passing logic includes an

examination of whether a potential passer would gain significant advantage by performing the passing

maneuver, vs. following its leader at the leader's desired speed. If the leader and the potential passer have

nearly the same desired speeds, the potential passer will not be sufficiently motivated to pass, so will

follow. On an upgrade, however, it is not so much desired speed as vehicle capability that may govern

passing maneuvers.

If truck A has a maximum speed of 30 mph on a given grade, and truck B has a capability of 31

mph, the current logic may cause truck B to initiate a passing maneuver. With the small differential in

truck speeds, the two trucks would then essentially block both lanes to passenger vehicles capable of 60

or more mph. (In this example, if truck B has a flying start at the passing maneuver -- e.g. it is already

traveling at 31 mph -- it will require over 0.8 miles to complete the maneuver, assuming each truck is

about 60 ft long and 15 ft of clearance between trucks before and after the pass are required. If truck B

has to accelerate to 31 mph from 30 mph, a longer distance will be required because truck B can reach 31

mph only asymptotically.) It is expected that truck drivers do not normally create such situations; they

initiate passes on upgrades only if they believe they can complete them over a reasonable distance. Field

data would be needed to place realistic bounds on such passing behavior, and then the model would have

to be modified to incorporate appropriate logic.

Increase Truck Speeds on Approaches to Upgrades

It is known that truck drivers often increase their speeds on approaches to upgrades, but there are no

field data available of which we are aware that indicate the magnitude of the speed increase or the

distance in advance of the upgrade at which it begins. Therefore, a field study of truck speed profiles on

approaches to upgrades will be required to implement this improvement.

Once the field study is complete and the data have been analyzed, the development of the program

logic to implement truck speed increases on approaches to upgrades should be relatively straightforward.

This will require introduction of an approach region for each upgrade, within which trucks may exceed

their desired speed. The logic for such approach regions would be analogous to the approach regions for

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horizontal curves and crawl zones that are already in the program, except that trucks would increase

rather than decrease speed within the approach region to an upgrade. Testing of the new logic would be

needed to assure that lower speed features, such as horizontal curves, negated any effect on truck speed

that might be attributed to an approaching grade.

Update Basic Parameters in Truck Performance Equations

It is recommended that basic parameters in the TWOPAS truck performance equations -- such as

rolling resistance, aerodynamic resistance, drive train losses, gear shift delays, and effect of altitude on

engine performance -- should be updated from their existing values (determined in the mid-1970's and

updated during the 1980's) to values more representative conditions. Such an update will require a

thorough review of truck manufacturer's literature, and possibly field data collection. However, before

such data collection is undertaken, it is recommended that a sensitivity analysis be performed to

determine whether the likely changes in these parameters are sufficient to have a substantial effect on the

macroscopic output of the model.

To perform this sensitivity analysis, the parameters of interest should be adjusted some amount,

perhaps 10 to 20 percent, probably one at a time, and the changes in performance noted. Acceleration

performance on level terrain and reduced crawl speed on an upgrade should be examined. If the effect is

minimal, then perhaps no further work would be required. For example, for the heaviest trucks, crawl

speed on a significant upgrade will be so low that aerodynamic drag will be quite unimportant, although

aerodynamic drag will be very important in determining top speed on level terrain or on a downgrade.

For those parameters found to be of importance in predicting some aspect of the trucks performance,

efforts should be devoted to quantifying values representative of current conditions. Literature should be

of some assistance, as should contacts with vehicle manufacturers, and acquisition of manufacturers'

literature. As a last resort, experimentation and/or field data collection may be required.

Automatic Determination of Crawl Zone Locations and Crawl Speeds for Specific Truck Types on

Downgrades

It is anticipated that the determination of crawl zone locations and crawl speeds for specific truck

types in TWOPAS can be automated using existing data without the need for extensive field data

collection. The two primary resources that will be used for this effort are the downgrade severity rating

system developed for FHWA by STI. and the VDANL model which can simulate brake temperature as a

truck proceeds down a grade and can thus be used to determine the crawl speed (and corresponding gear)

required to avoid a runaway truck. We do not see any advantage in trying to incorporate the VDANL

TR-1326-1

VI. PROPOSED MODEL ENHANCEMENTS

日期：2008-6-22 点击：作者：bsqlunwen 来源：中国专业代写论文网

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VI. PROPOSED MODEL ENHANCEMENTS

日期：2008-6-22 点击： 作者：bsqlunwen 来源：中国专业代写论文网

【字体： 】

日期：2008-6-22 点击：作者：bsqlunwen 来源：中国专业代写论文网

【字体：】