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Vehicle Speed Considerations in Traffic Management:
Development of a New Speed Monitoring Program
Darren L. Jorgenson Ernst and Young
Matthew G. Karlaftis* National Technical University of Athens
Kumares C. Sinha Purdue University
Abstract
Since the passage of the National Maximum Speed Limit
(NMSL) of 55 miles per hour (mph) in 1974 through its repeal in 1995, the
federal government has mandated speed monitoring programs. The speed monitoring
program was primarily intended to provide reliable data for inclusion in states’
annual certification for Federal Aid Highway Projects. The repeal of the NMSL in
1995 not only authorized states to set their own speed limits but also allowed
them to develop their own speed monitoring programs. This paper develops a
seven-step framework for a speed monitoring program tailored to meet the needs
of individual agencies using speed monitoring data at the state level. The
proposed speed monitoring plan distributes speed monitoring stations to highway
classes according to three primary criteria: spatial distribution, crash
distribution, and daily vehicle-miles traveled (DVMT) distribution. The proposed
plan is also compared with the existing speed monitoring program.
Introduction
The objective behind the design of any engineered public
facility is to satisfy the demand for service in the safest and most efficient
manner. As such, speed is one of the traveler’s foremost concerns when selecting
alternate routes or transportation modes. Directly related to its speed,
convenience and economy largely determine the value of a transportation facility
in carrying people and goods.
At the same time, speed relates to travel safety. The
National Crash Severity Study (NCSS), an investigation of approximately 10,000
crashes from 1977 to 1979, revealed that the possibility of fatality increases
dramatically as the change in velocity during the collision increases (Flora
1982). This study showed that a driver crashing with a change in velocity of 50
miles per hour (mph) is twice as likely to be killed as one crashing with a
change in velocity of 40 mph.
Vehicle speed contributes to crash probability, and an
exceptionally important factor is the
variability in speed on the same segment of highway.
Speed variance, a measure of the relative distribution of travel speeds on a
roadway, relates to crash frequency in that a greater variance in speed between
vehicles correlates with a greater frequency of crashes, especially crashes
involving two or more vehicles (Garber 1991). A wider variability in speed
increases the frequency of motorists passing one another, thereby increasing
opportunities for multi-vehicle crashes. Because vehicles traveling the same
speed in the same direction do not overtake one another, as long as the same
speed is maintained, they cannot collide. There have been several notable and
exhaustive literature reviews in the area of speeding and crash probabilities,
covering both the U.S. and abroad, worth consulting. See Transportation Research
Board (TRB) (1998).
An important determinant of traffic safety is effective
speed enforcement. While enforcement techniques have changed over the years, the
principal reasons for controlling vehicle speeds, protection of life and
property against the hazards of highway travel and efficient use of street and
highway systems, have not. Speed monitoring data allow agencies to set up
enforcement strategies, which will reduce speeds and, consequently, increase
safety. Vaa (1997) conducted a field experiment in which a 35-kilometer stretch
of road was subjected to an increase in police enforcement. Speed was measured
in 60 and 80 kilometer per hour (km/h) speed limit zones before, during, and
after enforcement withdrawal and compared with another stretch of road. Average
speeds were reduced in both speed limit zones for all times of day. For some
time intervals, the average speed and the percentage of speeding drivers were
reduced for several weeks after the period of enforcement, demonstrating a
time-halo effect1 of eight weeks.
The present study discusses the necessary steps in
developing a speed monitoring program and uses data from the state of Indiana to
adjust the program to the needs of the state. Several factors warrant the
present study. First, the existing speed monitoring program is designed to meet
federal requirements and does not necessarily address the particular needs of
state agencies. Second, speed monitoring stations are distributed to highway
classes based solely on daily vehicle-miles traveled (DVMT), while states may
find it appropriate to use additional criteria for monitoring station
distribution. Finally, the existing program does not account for geographic gaps
between stations where no monitoring occurs. The remainder of this paper is
organized as follows: the second section discusses the existing, federally
mandated, speed monitoring program and the current speed monitoring practices in
various states. The third section identifies the speed monitoring needs for the
state of Indiana and provides an overall strategic framework for the proposed
speed monitoring plan. The fourth section presents the proposed speed monitoring
program along with a comparison of the existing program, and the last section
offers some concluding remarks and recommendations.
Background
In 1973, Congress established a National Maximum Speed
Limit (NMSL) of 55 mph, initially as a temporary energy conservation measure. In
1974, Congress made the national maximum speed limit permanent. The Federal Aid
Amendments of 1974 made annual state enforcement certification a prerequisite
for approval of federal aid highway projects. Summary data from state speed
monitoring programs were a part of these annual certifications. The “Procedural
Guide for Speed Monitoring,” issued September 1975, provided the first federal
guidelines for speed monitoring (USDOT FHWA 1975). The original speed monitoring
procedures were designed to collect data and produce statistics for each of five
highway types in a state on level, tangent highway sections under “free-flow”
conditions. The methods for calculating statewide statistics, however, varied
among the states, making the value of state-to-state comparisons
questionable.
Slowly declining compliance with the 55-mph speed limit
and increasing accident and fatality rates prompted the U.S. Department of
Transportation (USDOT) to recommend, and Congress to approve, significant
changes in the speed limit legislation in 1978 (USDOT FHWA 1978). The Highway
Safety Act of 1978 provided for both withholding of federal aid highway funds
and awarding incentive grants based on annually submitted speed compliance data.
An estimate of the percentage of motor vehicles exceeding 55 mph became the
major reporting requirement. Further changes to the speed monitoring program
included that “free-flow” would no longer be the only condition monitored. Speed
statistics must be representative of all travel; thus, all vehicles passing a
monitoring station during the observation period had to be measured.
Furthermore, speeds could be monitored on other than level, tangent sections of
highway.
In 1980, further changes were made when the "Speed
Monitoring Program Procedural Manual" (SMPPM) (USDOT FHWA 1980) was issued. Some
of the most important points include the following: 1) sampling sessions were to
be 24 hours long in order to account for varying daily traffic conditions
affecting speeds; 2) highways were stratified into 6 categories based on Federal
Highway Administration (FHWA) classifications instead of the 5 categories based
on geometry as they were previously defined; 3) sampling sessions were allocated
among highway categories based on the statewide DVMT, subject to the 55-mph
speed limit in each highway category; 4) within a category, locations were
picked using simple random sampling with probabilities proportional to mileage,
commonly known as probability sampling; and 5) the target sampling accuracy of
the annual statewide value of percentage of DVMT over 55 mph was 2.0% at a 95%
confidence level. The number of sampling locations was established as the
greater of the numbers needed to meet the target sampling accuracy and the DVMT
subject to the 55-mph limit divided by two million.
On April 2, 1987, the Federal Aid Highway Act of 1987
gave “the states the authority to increase, without the loss of Federal
Aid-funds, the maximum speed limit to no more than 65 mph…on Interstate Systems
located outside an urbanized area of 50,000 (population) or more.” Also, “Any
state choosing to increase the speed limit from 55 mph…will have to adjust the
speed sampling and analysis plan in effect for the fiscal year in which the
limit is raised.” A memorandum the FHWA distributed advised states choosing to
increase the speed limit on eligible sections of rural interstate highways that
DVMT represented by mileage in areas where the speed limit had been raised above
55 mph would not figure into the calculation of 55-mph-speed-limit compliance
statistics for fiscal year (FY) 1987. In essence, DVMT factors would be adjusted
to exclude all rural interstate locations where the maximum speed limit had been
reposted to 65 mph. Even though a process of redistribution of DVMT weighting
factors would exclude the requirement of monitoring and reporting statistics for
rural interstate highways, the same number of locations would continue to be
distributed among the (remaining) functional groupings in the same proportion as
before.
In December of 1991, the Intermodal Surface
Transportation Efficiency Act (ISTEA) was signed into law. FHWA and the National
Highway Traffic Safety Administration (NHTSA) subsequently published
modifications governing the National Maximum Speed Limit (NMSL). The revised
procedures established speed-limit compliance requirements on both 55-mph and
65-mph roads. This statute assigned greater weight for speed limit violations in
proportion to the degree motor vehicles exceed the speed limit. Additionally,
the ISTEA compliance formula was tied more closely to the relative risk of
fatality and to a measure of crash severity. On November 28, 1995, new federal
legislation repealed the National Maximum Speed Limits, ending two decades of
mandates. Effective December 8, 1995, states were again authorized to set their
own speed limits and speed monitoring policies.
Jorgenson (1998) conducted a survey of the current speed
monitoring practices from May of 1997 through July of 1998 in all 50 states.
Since the repeal of the NMSL, 30 states have elected to change their speed
monitoring programs. Of those 30 states, 8 states currently have more monitoring
stations than previously mandated by the FHWA. Of the 22 states that reduced the
number of monitoring stations, 11 dropped the speed monitoring program
altogether (table 1).
Identification of Speed Monitoring Needs
After the repeal of the NMSL, the most important
question for the state of Indiana became if the speed monitoring program should
be continued. A simple questionnaire was distributed to parties interested in
speed monitoring: the Indiana State Police, the Indiana Department of
Transportation (INDOT) Planning Division, INDOT Roadway Management Division,
FHWA safety engineers, and others. As table 2 shows, the respondents almost
unanimously supported a speed management program. The respondents considered it
important to continue speed monitoring following the repeal of the NMSL in order
to devise suitable enforcement measures, ensure safety on the state road
network, provide speed information to various public and private agencies, and
have reliable data readily available for design, operational, and research
needs.
Once the need for and desire to participate in a new
speed monitoring program was established, the second question became the
criteria by which monitoring sites were to be distributed among highway classes.
After discussions with the participants in the preliminary study, three
considerations for site allocation were chosen: 1) spatial distribution, 2)
relative DVMT distribution, and 3) relative crash distribution. The crash
distribution criterion was further broken down into four types of crashes: all
crashes, all fatal crashes, speed-related crashes, and fatal speed-related
crashes. The six highway classes chosen were rural interstates, urban
interstates, rural U.S. roads, urban U.S. roads, rural state roads, and urban
state roads. Sites have historically been distributed by functional highway
class. In the proposed plan, two factors influenced the decision to consider a
different highway classification scheme. First, all supporting data used in the
present study, such as vehicle-miles traveled and crash data, were available for
the new classification scheme. This consistency allows any agency using speed
data to easily investigate causal relationships. Second, there was evidence of a
statistically significant difference in the mean speed of these highway classes
(Jorgenson 1998).
We used a Delphi study to ensure that the allocation of
speed monitoring stations be consistent with the requirements of those using the
resulting data by ranking and rating the site distribution criteria. The Delphi
process allows a group with varying opinions to come to a consensus. In the
present study, the objective was to rank and rate speed-monitoring station
distribution criteria. The Delphi process replaces direct confrontation and
debate with a carefully planned, orderly program of sequential discussions,
carried out through an iterative survey (Dalkey et al. 1969). Typically, a
presentation of observed, expert concurrence in a given application area where
none existed previously results (Sackman 1974). In this portion of the survey,
participants were first asked to allocate 100 points among the three
distribution criteria. The higher the number, the more important that criterion
was deemed. For the next step, participants allocated 100 points among the four
crash categories. Again, the higher the number, the more important that crash
type was deemed.
Table 3 provides the results of the Delphi process.
Following the first round, DVMT was the highest rated distribution criterion
with 36 points. Crash distribution was second with 33 points, and spatial
distribution was third with an allocation of 31 points. The crash results showed
speed-related crashes to be the most important crash distribution criterion with
an average 29.3 points. Fatal speed crashes followed with an average of 28.6
points. All crashes came in third with an average of 24.7 points, and all fatal
crashes was fourth with an average of 20.0 points. In the second round, the
order of importance for both the distribution criteria and crash types changed.
As table 2 shows, DVMT continued to be the most important distribution criterion
with an average 34.8 points. This was closely followed by spatial distribution,
up from third place in the first round with an average value of 34.2. Crash
distribution was last with a mean value of 31.0. While this result may seem
counterintuitive to some in that crash distribution would be deemed the least
important, it demonstrates the power of the Delphi approach: criteria importance
is based on collective results rather than on single opinions.
The order of importance for crash types also changed.
Speed-related crashes remained in first place with an average 28.8 points. All
crashes moved up from third to second with an average 27.9. Fatal speed crashes
dropped from second to third place with an average of 24.3. Finally, all fatal
crashes remained fourth with an average of 19.0. Because the Delphi process
deliberately manipulates responses toward minimum dispersion of opinion in the
name of consensus (Martino 1972), there is no advantage to continuing beyond two
rounds (Dalkey 1970). Therefore, the survey stopped at that point. Having
identified both the desire and need for a speed monitoring program and the
criteria to develop it, we then developed the basic procedure to define the
number, location, and monitoring time of the new program.
Design of the Speed Monitoring Plan
In order to maintain, as much as possible, compatibility
with the data collected under the FHWA program, the new program’s design,
follows the statistical requirement of a 2.0 mph maximum error of estimate, with
95% confidence, as used in the federal program (USDOT 1992). This requirement
determined the following seven core components of the proposed program: 1) the
number of monitoring sessions per year, 2) duration of monitoring period for
individual sampling sessions, 3) monitoring speed by direction of travel, 4)
monitoring speed by vehicle length, 5) the minimum number of statewide sampling
locations, 6) monitoring station site distribution, and 7) selection of
monitoring locations. Finally, the proposed program has speed monitoring
stations allocated by highway class based on the distribution criteria discussed
in the previous section. We also discuss here a procedure to help determine
locations of monitoring sites utilizing existing speed monitoring,
weigh-in-motion (WIM), and automated traffic recording (ATR) stations.
Number of Monitoring Sessions per Year
The federally developed monitoring program collects
speed data every quarter (USDOT 1992). However, while it is well documented that
traffic volume varies by time of year (McShane and Roess 1990), the variation in
mean speed by time of year may not be significant. The present study examines
the need for quarterly speed monitoring. The existence of a significant
difference in mean speed by quarters and of a significant difference between
each quarterly speed distribution could determine the necessity of quarterly
speed monitoring.
A three-stage, nested factorial design (Montgomery 1997)
serves as the statistical model used to analyze the number of monitoring
sessions per year. A nested, factorial design was chosen because levels of one
factor are similar but not identical for different levels of another factor.
This means, for example, that highway class one in district one of year one is
similar to, but not identical to, highway class one in district one of year two.
Therefore, highway class is nested under district one in year one. This analysis
used the historical 1983 through 1997 speed monitoring data collected in
Indiana. The database covered 15 years, 4 annual quarters, 6 districts, and 6
highway classes. The total of 320 stations represented different monitoring
locations used over the 15-year period. Appendix one shows the model for the
three-staged, nested factorial design used in this experiment representing the
main effects and their associated interactions. The model was estimated with SAS
(1998) in order to test for significant main and interaction effects. The
Student-Newman-Keuls (SNK) multiple range test was used on all main effect means
(Everitt 1992). The SNK method compares all pairs of treatment means in an
effort to discern which means differ.
The experiments of interest in this analysis were
variation by quarter, variation by quarter by class, variation by quarter by
district, and variation by quarter by district by class.
Table 4 shows the
significance probabilities associated with each main effect and interaction this
analysis used. From this table we can determine the significance of the relevant
main effects and their interactions. The probability associated with the main
effect of quarter, denoted by
γ
m (0.9054), indicates that no significant difference in mean speed existed between
quarters. Mean speeds stratified by quarter, presented in
table 5, demonstrate
that the mean speed only varied from 58.8 mph in quarter 1 to 58.9 mph in
quarter 4, further showing that mean speed was not significantly different by
quarter. The probability associated with the quarter by class interaction
effect, denoted by
χγ
km (0.8790), indicates that
mean speed is not significantly different by quarter and highway class. The
probability associated with the quarter by district interaction effect, denoted
by
βγ
jm (0.5505), indicates that mean speed is not
significantly different by quarter and district. The probability associated with
the quarter by district by class interaction effect, denoted by a
αβχγ
ijkm (0.6947), indicates that mean speed is
not significantly different by quarter within each highway class and district.
Nevertheless, it should be noted that there is preliminary evidence that
although the mean speed was found not to be different by quarter, the speed
distributions may be. This
hypothesis was tested using Fisher’s
c
2-test (Jorgenson 1998). Consequently, it may be
desirable to continue to monitor speed every quarter.
Duration of Monitoring Period for Individual Sampling
Sessions
Under the original FHWA program, a 24-hour monitoring
period was selected for the following reasons. First, it accounted for the
varying traffic conditions affecting speeds within a day. Second, the
within-cluster (daily) variation would not allow for a reduction in the number
of locations required even if much longer periods were used. The 24-hour
monitoring period minimized cost in terms of the combination of sampling
locations required and the need for equipment. For the proposed program, the
Indiana State Police wanted to test whether day of week was a significant factor
in determining mean speed. If so, it would be necessary to monitor speeds for a
longer period, thus the need for this analysis. With a two-stage, nested
factorial mixed effects model with data from 27 WIM stations distributed
throughout the state, it was concluded that, at the 95% level of significance,
the effect of day on mean speed was not a significant factor in explaining the
variation in mean speeds in Indiana; thus, the future program should continue to
monitor speeds 24 hours a day.
Monitoring Speed by Direction of Travel
The survey of state-wide speed monitoring practices
revealed that half of the states that continue to monitor speeds do so in both
directions of travel. Consequently, INDOT wanted to see if it was necessary for
Indiana to measure speed by direction. Also, the Indiana State Police felt speed
by direction could be important for enforcement purposes. A two-stage, nested
factorial mixed effects model determined at the 99% level of significance that
mean speeds were different by direction of travel. Based on this finding, speed
should be monitored for each travel direction, particularly for divided
highways.
Monitoring Speed by Vehicle Length
Since trucks are much heavier and have slower
acceleration and deceleration rates than passenger vehicles, there is an
increased potential for severity in cases of crashes between trucks and smaller
vehicles. Higher speeds add to the severity of these crashes. At the same time,
speed variance increases when trucks travel at a different speed than other
vehicles. In Indiana, the speed limit for trucks on rural interstates is 60 mph,
while for passenger vehicles it is 65 mph. Representatives from Indiana State
Police, INDOT, and the Department of Revenue requested that an analysis
determine if a difference existed in mean vehicle speed based on vehicle length,
not only on rural interstates but also on other roads. A two-stage, nested
factorial mixed effects model was estimated with station nested under highway
class. Station is nested under highway class because different levels of station
are similar but not identical for different levels of highway class. As the
federal program suggested, speed by vehicle class was not monitored. A special
data collection effort was made during the four quarters of 1997 to record speed
data separately for trucks at randomly selected existing monitoring stations.
Three vehicle classes were considered. Class 1 consisted of passenger cars 20
feet long or less; class 2, medium sized trucks between 21 and 40 feet long; and
class 3, large trucks 40 feet long or greater.
Of interest in this experiment was whether vehicle class
and the interaction between highway class and vehicle class were significant.
Results show that highway class, vehicle length, and the interaction between
highway class and vehicle length were all significant with probability (Pr >
F ) values of 0.0001. Because
Indiana currently employs differential speed limits on rural interstates, the
interaction between highway class and vehicle class could be significant. It was
found that mean speeds for the three vehicle classes considered were
significantly different from each other. Passenger cars had a mean speed of 60.2
mph; single unit trucks and buses had a mean speed of 58.2 mph, and combination
trucks had a mean speed of 59.4 mph. The results are somewhat surprising because
one would expect single unit trucks to travel at a higher speed than combination
trucks.
Number of Statewide Monitoring Stations
Two concepts were used to determine the number of
statewide monitoring stations: reliability of statistical estimates and coverage
of population sampled (Miller et al. 1990). In the FHWA program, the standard
statistical requirements for determining sample size depend on the statewide
standard deviation of the percentage of vehicles exceeding the posted speed
limit rather than on mileage or vehicle-miles traveled (USDOT 1992). Since this
figure would be similar in most states, the resulting sample sizes would be
nearly the same, with the exceptions of very small states. This meant that,
statistically, the sizes of the speed populations of different states had very
little influence on the sample sizes required for estimation. Having nearly
equal samples for the different states did not provide data representative of
the widely varying travel characteristics found among the states. The concept of
"coverage of population sampled" instead provided balance to the work load among
the states and a margin of increased accuracy for the larger states with larger
mileages and DVMT.
The FHWA program determined the minimum sample size
needed for a state under each of the two concepts and then selected the larger
of the two numbers as the statewide minimum sample size. In this manner, the
reliability requirement can always be met, and the sample size can be sensitive
to the varying amounts of travel in the states. The present study adopted the
FHWA approach in determining the total number of stations in the proposed
program. To determine the number of locations required for the desired
precision, a preliminary estimate of the standard deviation was estimated. The
present study used the default value for this parameter, set by the FHWA at
7.0%, to determine the number of stations required. The formula to calculate the
number of monitoring stations follows.
Equation (1):
where
no = sample size,
z.95 = value of the normal distribution based on a one-sided 95% confidence interval,
S (Pst ) = standard deviation of the percentage of
vehicles exceeding the posted speed limit,
d = precision level required (2.0 mph).
For Indiana, the number of sampling segments required by
the reliability of statistical estimates criterion was 38.
The coverage concept was designed to allocate locations
based on the amount of travel, DVMT, subject to the posted speed limit in the
state. This concept served various purposes: 1) to provide a balanced sample
size; 2) to compensate for the additional variation possibly present due to
larger volume or larger mileage; and 3) to account for the potential variation
in speed enforcement activities of different police departments, districts, or
jurisdictions within a state. With DVMT data from the 1997 Highway and Pavement
Management System (HPMS) (USDOT 1995) database, the number of monitoring
stations required for Indiana under the coverage concept is 26 (Jorgenson 1998).
Therefore, the greater of the reliability criterion and the coverage criterion
require 38 stations in the proposed program.
Monitoring Station Site Distribution
Concept
With the statewide number of necessary speed monitoring
stations determined, the next step was to distribute them by highway class. As
mentioned in the previous section, the three distribution criteria adopted in
the present study are spatial distribution, DVMT distribution, and crash
distribution. The crash distribution criterion was further broken into four
crash types: all crashes, all fatal crashes, speed related crashes, and fatal
speed related crashes. The expected site distributions were first computed for
each criterion and crash type. The individual distributions were then combined
into a composite distribution based on the individual criterion’s importance.
Spatial Distribution
The procedure used to distribute the speed monitoring
stations by highway class according to the spatial criterion considered the six
INDOT districts as separate geographical areas. The HPMS database served to
calculate the number of lane-miles in each highway class for each district,
giving the percentage of lane-miles by highway class by district. This
percentage was then multiplied by the total number of stations, yielding the
number of stations by highway class by district. The number of sites in each
highway class was then summed over the district, giving the expected number of
stations in each highway class for the state, as shown in
table 6.
DVMT Distribution
To determine site distribution based on the DVMT
criterion, the HPMS database was used to compute DVMT for each highway class.
The DVMT for each highway class was then divided by the total DVMT subject to
the 55-mph or greater speed limit, giving the percentage of DVMT for each
highway class. That percentage was then multiplied by the total number of
stations, giving the expected number of stations by highway class for the DVMT
criterion. These calculations are shown in
table 7.
Crash Distribution
To allocate stations according to crash criteria, an
average crash distribution was computed for each of the four crash types. The
1991–1995 crash data from the Indiana State Police Crash Information System
Crash Master Files is a database containing records on all reported crashes in
Indiana. Table 8 shows the average crash distributions for all crashes; this
process was repeated for all crash types. Once the average crash distribution
for each crash type and for each highway class was computed, the percentage
value was multiplied by the total number of stations, giving the expected number
of stations by highway class for each crash criterion. This procedure was
repeated for each of the four crash types, and the results for all crashes are
shown in table 9.
Composite Site Distribution
After obtaining six separate site distributions schemes,
we then combined them into a composite distribution. The importance ratings
provided by the Delphi study played a role at this stage. A weighted average
site distribution scheme was devised by multiplying the associated weights with
the respective site distributions and summing them over each highway class. The
goal was to have a composite site distribution that statistically satisfied each
site distribution criterion: the proportion of sites in each highway class for
each distribution criterion should be equal to the proportion of sites in each
highway class for the composite distribution. Because it would be almost
impossible to find a composite site distribution that statistically satisfied
all three distribution criteria, the present study attempted to satisfy the two
most important site distribution criteria, DVMT and spatial distribution.
In order to obtain a composite site distribution,
monitoring stations were allocated to highway classes, making the composite
distribution statistically close to both the DVMT and spatial distribution. The
proposed site distribution has 13 stations in rural interstates, 10 in urban
interstates, 7 in rural U.S. roads, 2 in urban U.S. roads, 4 in rural state
roads, and 2 in urban state roads.
Selection of Monitoring Station Location
The proposed program makes maximum use of the existing
speed monitoring, WIM, and ATR stations without affecting the statistical
reliability of the proposed monitoring plan. The three options considered for
this purpose vary by the level of use of existing stations: minor, moderate, and
major change.
The first option, minor change, uses existing stations
if they are in the same district and highway class of the proposed station. In
this option, existing stations receive priority in the site selection process.
If a certain highway class in an existing station is not available, a new site
is randomly selected. Cost savings is the benefit of this method because very
few new stations need to be installed. The main drawback is the reduction in
randomness of the site selection process. To select the monitoring location for
minor change, an iterative procedure helps allocate sites to highway classes
within districts according to a range of plus or minus one of the recommended
number of sites, based on the number of sites available. The recommended number
of stations was computed by taking the percentage of lane-miles in a given
highway class for a given district and multiplying that number by the total
number of stations in that highway class. This procedure ensures that sites are
distributed evenly throughout the state and minimizes the difference between the
actual and recommended stations per district and highway class.
The second option, moderate change, also utilizes
existing stations but in a different manner. The stations are first randomly
selected. Then, existing stations are chosen if they match the characteristics
of the randomly selected stations (DVMT, number of lanes, location, preferably
the same continuous highway, and so forth). This method has a moderate cost and
degree of randomness.
The third option, major change, relies totally on random
selection of sites. The benefit of this alternative is that sample segments are
completely random. The drawback is the high cost associated with installing new
stations. Moderate and minor change have the same number of stations in each
district and highway class; the difference between the two methods is in how the
highway segments for monitoring stations are selected. To allocate the
monitoring locations for moderate and major change, a procedure similar to the
iterative one used in minor change was followed, except that there was no
constraint requiring the use of available stations. For moderate change, the
randomly selected stations were substituted for existing stations, when
feasible. For major change, no such substitution took place. For this reason,
the actual locations of individual monitoring stations are different under
moderate and major changes, even if the distribution of stations remains the
same.
Based on the minor change option, 38 existing stations
would be used in the monitoring program. With the moderate change option, 22
existing and 16 new stations would be used. Based on the major change option, of
the 38 randomly selected segments, 37 would be new stations and only 1 would be
an existing station. It was a coincidence that this existing station was
randomly selected. Because the primary objective of the study was to utilize as
many existing speed monitoring stations as possible, the present study uses the
minor change option of 38 existing speed monitoring stations.
Comparison of Proposed with Existing Site Layout
A comparison of the proposed site layout with the
existing site layout indicated if the proposed site layout would be an
improvement over the existing program. The underlying assumption in the present
study’s sample size calculation was that the relative precision of the estimates
would not exceed 2.0 mph. The relative precision can be calculated using the
sample size and standard deviation of the percentage of vehicles exceeding the
posted speed limit. The calculation of relative precision for the existing
program used data from existing sites. For the proposed program, the standard
deviation of the percentage of vehicles exceeding the posted speed limit had to
be estimated using historical data.
Table 10 shows the proposed and existing site layouts
with the expected number of stations for each of the site distribution criteria.
The probability-values (p)
under the expected values indicate the probability that the given site
distribution will be similar to the distribution occurring from the listed site
distribution criteria. A low p-value (<.05) indicates significant evidence
of dissimilarity between the distributions. From this table, we can see that the
proposed distribution is similar to the distribution yielded by the DVMT and
spatial criteria. This means that the proposed distribution is not significantly
different from those distributions based on the DVMT and spatial criteria. The
existing distribution, however, is only similar to the distribution yielded by
the crash criterion. In other words, the proposed station distribution satisfies
two of the three distribution criteria, while the existing site distribution
only satisfies one distributional criterion.
Conclusions
The present research reviews the federal speed
monitoring program from its inception in 1956 through the repeal of the NMSL in
1996. A survey of relevant agencies in Indiana indicates that Indiana should
continue to monitor speeds under a formal program. Also, the present study
analyzes the core components of the FHWA program and presents a new methodology
to allocate speed monitoring stations based on three criteria: spatial
distribution, DVMT distribution, and crash distribution. The present study
evaluates three different approaches to select sampling locations throughout the
state. Finally, the proposed station distribution is compared with the existing
station distribution.
We have shown the need to continue a formal monitoring
speed program at the state level. The present study uses statistical models to
demonstrate that mean speed does not vary by quarter but that daily speed
distributions do. As such, Indiana may wish to monitor speeds every quarter. The
results indicate that day of week is not significant, while direction of travel
is. The state of Indiana should monitor speeds for a 24-hour period in both
directions of travel. Also, a statistical model was developed and shows that
speed varies by vehicle class, suggesting that Indiana should monitor speeds
based on vehicle class. Finally, Indiana should utilize a site layout which
incorporates 38 existing speed monitoring, WIM, and ATR stations.
References
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Address for Correspondence and Endnotes
*Matthew G.
Karlaftis, National Technical University of Athens, Iroon Polytechniou 5, 157 73
Athens, Greece. Email: mgk@central.ntua.gr.
1 The time-halo effect is the length of time
during which the effect of enforcement is still present after police activity
has been withdrawn.
The statistical model for the three-stage, nested factorial design used in the number of monitoring stations per year experiment follows (similar two-stage models were developed for the other experiments as well):
Equation (2):
γijklm = μ + αi + βj + χk + αij + αik + βχjk +
αβχijk + δ(ijk)l + γμ + αγim + βγjm + αβγijm +
αχγikm + βχγjkm + αβχγijkm + δγ(ijk)lm
where
μ is the overall sample mean, αi is the effect of the ith year, βj is the effect of the jth district, χk is the effect of the kth highway class, αβij is the interaction between the ith year and jth district, αχik is the interaction between the ith year and kth highway class, βχjk is the interaction between the jth district and kth highway, αβχijk is the interaction between the ith year jth district and kth highway class, δ(ijk)l is the effect of the lth station within the kth highway class within the jth district within the ith year, γμ is the effect of the mth quarter, αγim is the effect of the interaction between the ith year and mth quarter, βγjm is the effect of the interaction between the jth district and mth quarter, χγkm is the effect of the interaction between the kth highway class and mth quarter, αβγijm is the effect of the interaction between the ith year the kth highway class and the mth quarter, αχγikm is the effect of the interaction between the ith year the kth highway class and the mth quarter, βχγjkm is the effect of the interaction between the jth district the kth highway class and the mth quarter, αβxγijkm is the effect of the interaction between the ith year the jth district the kth highway class and the mth quarter, and δγ(ijk)lm is the effect of the interaction between the lth station within the kth highway class within the jth district within the ith year and the mth quarter.
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