MODELING AND EVALUATING THE SAFETY IMPACTS OF ACCESS 1
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MANAGEMENT (AM) FEATURES IN THE LAS VEGAS VALLEY
by
Timur Mauga
Graduate Research Assistant
Department of Civil and Environmental Engineering, and Transportation Research Center (TRC)
University of Nevada Las Vegas (UNLV) 4505 Maryland Parkway, Box 454007
Las Vegas, NV 89154-4007 email: [email protected]
Mohamed Kaseko, PhD, Corresponding Author
Associate Professor of Civil Engineering
Department of Civil and Environmental Engineering, and Transportation Research Center (TRC)
University of Nevada Las Vegas (UNLV) 4505 Maryland Parkway, Box 454015
Las Vegas, NV 89154-4015 Ph (702) 895-1360 Fax (702) 895-4401/3936 email: [email protected]
Submitted to the Transportation Research Board For
Presentation at the 89th Annual Meeting, January 2010, and Publication in the Transportation Research Record
August 1st, 2009
Pages 16 Words 5,430
Abstract
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Introduction
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Urban growth usually leads to new land uses abutting arterials requiring driveways for their accessibility. Uncontrolled number and locations of such driveways has been reported to cause safety and mobility problems to arterials at the same time degrading accessibility to land uses (1). The solution to these problems is access management (AM) whose objectives are to improve safety and mobility by controlling the number and location of driveways while balancing the need for accessibility. Several studies have reported various benefits of carrying out AM programs. As an example of the effectiveness of AM programs, before and after studies have reported reduction in crashes by an average of 40%; increase in level of service during peak period (2, 3, 4), and positive economic impacts (2, 4, 5) on corridors where AM programs were carried out.
Access management (AM) is defined as the systematic control of location, spacing, design and operation of driveways, median openings, interchanges and street connections to a roadway (1). Most new projects incorporate AM aspects in their planning and design. Old roads that did not consider AM in their design are subject to retrofit programs in order to improve their service to the community. Most common AM features considered in retrofit projects are raised medians (RM), median openings, driveways, and turn lanes.
RM consist of physical barriers or slabs, at least six inches from pavement surface, that are installed between traffic bounds for the purpose of reducing conflict points resulting from turning maneuvers across a length of an undivided road or a two way left turn lane (TWLTL). The RM hence cut down many conflict points associated with jogging (overlapping left turns to-and-from offset driveways) and crossing (for aligned driveways) maneuvers.
For street segments with RM, median openings are used to provide access for turning vehicles. These median openings are usually aligned with unsignalized cross roads or driveways into and out of adjacent land-uses. Cross roads are access roads (roads low in functional classification of road that serves more accessibility than mobility) that connect other traffic zones to major roads under.
AM programs or studies vary from dealing with one technique to a combination of techniques. With respect to replacing two-way-left-turn lanes (TWLTL) with raised medians (RM), Maze and Plazak (2) reported a decrease in crash rates by 36.5% and 41.7% in the cities of Ankeny and Clive in Iowa, respectively. Gluck et al. (6) summarized finding of sixteen studies comparing crash rates by median type. Some of the studies were before-and-after and others were cross section studies. The safety improvement reported ranges from -15% to 57% with an average of 27% reduction in crash rates. The author also reported six studies that had a decrease in side-swipe, angle, and head-on crashes averaging to 31%, 40%, and 54%, respectively; The percent decrease in rear end crashes ranged from -15% to 50% with an average of 27%. The implication from the literature is that the RM has mixed impacts and the real marginal effect is either not known or varies between locations.
injury crashes. Lewis (9) explained the reason for increasing rear-end crashes after installing RM as short turn pockets at intersections but shorter signal spacings of approximately 0.25 miles, some of which had median openings, might be the suspects.
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With respect to driveways, Maze and Plazak (2) reported a decrease of 33.3% in crash rates in Fair Field City, Iowa, through closing 8 driveways in a 0.6 mile along with adding signals and improving side streets. Bonneson et al. (11) conducted a median specific regression analysis and found that driveways and unsignalized public approaches had the same safety impact regardless of median type. Gluck et al. (6) reported that addition of a driveway in a mile increases crash rates by 4%. The data in their report shows that driveways on roadways having RM had lower impact on crashes than those on TWLTL. The authors also reported data showing the adverse safety effects of driveways in segments with high signal density. Eisele and Frawley (8) reported that driveways on roads with TWLTL have bigger impact on crashes than those on roadways with RM.
Studies reviewed report varying methodologies of data collection and analysis. Furthermore, the studies report mixed results and do not provide the overall consistent impacts of AM features with certainty that can be transferred to other geographical areas. Bonneson et al. (11) explained the reason for variability of results as partly due to regional differences in accident reporting which might also be due to differences in driver predisposition or motivation to report accidents. The literature is still useful in providing some trends or expectations but the need for site specific study is still there. This paper presents the study that evaluated the impacts of AM features in Las Vegas valley and discusses the findings in terms of crash rates by total, severity and type. The study was part of the project for developing AM guidelines in the area
Study Objective
The objective of this study was to model the relationships between AM features in midblock street segments and total crash rates, as well as crash rates by type of crash and severity. From the results of these models, the impacts of these AM features on crash rates are identified and quantified. Five common AM features considered are type of medians, signal spacing, and the densities of median openings, unsignalized cross roads, and driveways. Their relative impacts are discussed with respect to development and implementation of AM techniques in new and retrofit projects.
Study Methodology
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Crash data covering five years from 2002 to 2006 were obtained from the Nevada Department of Transportation (NDOT) database in Geographic Information System (GIS) format. The crashes were summarized by total, type and severity. For each segment, the data was partitioned into midblock and intersection crashes. Although some studies have designated a radius of 200-250 ft around intersections as their influence area (9, 12, 13, 14, 15, 16), this study used a 200 feet radius. Intersection crashes were thus all crashes that occurred within 200 ft of signalized intersections and hence were not considered in the analysis.
An inventory of existing AM conditions for the selected segments was conducted in the laboratory using satellite imagery from Google Earth/Maps and the GIS database provided by the Regional Transportation Commission (RTC) of Southern Nevada. It was assumed that no significant changes in the AM features on the study sites for the period of analysis since the sites selected were already developed. Site visits were conducted to supplement the laboratory inventory for the sake of correcting misinterpretation of aerial photos in the case they were not clear. Traffic information including the average annual daily traffic (AADT) and speed limit were obtained from NDOT annual traffic reports. Table 1 summarizes the descriptive statistics AM features and traffic characteristics.
TABLE 1 Descriptive Statistics of AM and Traffic Characteristics
Variable Dataset Minimum Average Maximum Standard deviation
All 621.2 2,171 7,091 1,029
RM 621.2 1,955 5,345 842
Signal spacing (feet)
TWLTL 646.5 2,331 7,091 1,124
All
RM 0 5.00 10.80 2.48
Density of median openings (number/mile)
TWLTL
All 0 4.89 28.88 5.39
RM 0 4.42 28.88 5.91
Density of cross roads (number per mile)
TWLTL 0 5.24 24.01 4.95
All 0 41.32 104.52 20.94
RM 0 41.06 94.45 20.37
Density of driveways (number per mile)
TWLTL 0 41.51 104.52 21.40
All 4,883 37,865 96,080 15,037 RM 29,320 47,566 96,080 12,383 AADT
TWLTL 4,883 30,681 71,280 12,616
All 30 41.68 45 5.13
RM 30 43.54 45 3.70
Speed limit (mph)
TWLTL 30 40.30 45 5.59
20 21 22 23
24 25
After evaluating and testing several models the exponential function of the following form was chosen due to the resulting high adjusted R2 values:
M i
i X
Y i
N
j j ij
i
e
, , 1,2,3,...,.
0
= ∀ +
∑ =
+
ε β
β
1 2
Where
Yi is the crash rate for segment i in crashes per million vehicle-miles, computed as
6
CRi is the average number of crashes/year for segment i Li is the length of the segment i, in thousands of feet
ATi is the average AADT over the 5 years of the study period β0 is a constant term
βj is the coefficient for variable j
Xij is a the value of variable j for segment i εi is the error term
M is the total number of segments N is the number of variables
The dependent variable was crash rate in crashes per million vehicle-miles. The explanatory variables were AADT per lane, signal spacing in 1000’s of feet, median type (binary variable, 1 for RM and 0 for TWLTL), density of driveways (number per mile), density of cross roads (number per mile), density of median openings (number per mile), speed limit, number of main through lanes, and land use, measured as the proportion of driveways serving residential land uses.
The estimation of parameters in Equation 1 was done with Stata statistical software using the nonlinear command nl. In estimating nonlinear equations, the software uses an iterative modified Gauss–Newton algorithm in finding the minimum of the sum of squares error. The impacts of the AM variables on safety were evaluated and quantified using Equation (2) below, which is a relationship derived from equation (1) above. This equation gives an estimate of the percent change in the dependent variable as a function of a change in the value of the dependent variable is given as (17):
)
Analysis and Results
TABLE 2 Descriptive Statistics of Crash Rates by Collision Type and Severity
1 2
Crash rate (per MVMT)
Dataset Minimum Average Maximum Standard deviation
All 0 3.878 12.591 2.543
RM 0 3.610 11.089 2.405
Total
TWLTL 0.308 4.076 12.591 2.630
All 0 1.770 21.235 1.821
RM 0 1.391 6.318 1.313
Angle
TWLTL 0 2.061 21.235 2.089
All 0 1.546 10.617 1.296
RM 0 1.618 7.147 1.139
Rear-end
TWLTL 0 1.490 10.617 1.405
All 0 0.273 1.513 0.236
RM 0 0.264 1.303 0.207
Sideswipe
TWLTL 0 0.280 1.513 0.257
All 0 0.029 0.526 0.055
RM 0 0.015 0.165 0.030
Head-on
TWLTL 0 0.039 0.526 0.067
All 0 0.255 10.617 0.605
RM 0 0.195 2.091 0.205
Single Vehicle
TWLTL 0 0.301 10.617 0.783
All 0 0.014 0.433 0.041
RM 0 0.011 0.106 0.023
Fatal
TWLTL 0 0.017 0.433 0.051
All 0 1.657 7.465 1.192
RM 0 1.545 5.108 1.096
Injury
TWLTL 0 1.743 7.465 1.258
All 0 2.430 42.470 2.747
RM 0 2.092 6.915 1.376
Property Damage Only (PDO)
TWLTL 0.159 2.691 42.470 3.434
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Three models, one for all the segments combined, one for the RM subset, and the third for the TWLTL subset were calibrated to obtain the nonlinear multivariate regression coefficients for the explanatory variables. For each case, calibrations were done three times, one each for total crashes, crash type, and crash severity. The combined model was done for the purpose of estimating the isolated marginal effect of type of median. The researchers were aware that analyzing the combined datasets assumes that the marginal impacts of AM features are the same regardless of type of median. The analysis considered only variables significant at 10% level.
Total crash rate 1
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Table 3 summarizes the regression results for the three models with total crashes per million vehicle miles as the dependent variable. The table shows the estimated coefficients with their p-values reported in brackets. For the combined model, the results show that three of the four AM parameters, namely, signal spacing, driveway density and median type, have statistically significant impact on safety. The results further show that the longer the signal spacing, the lower the crash rate, meaning that longer segments are “safer” than shorter ones. In addition, the higher the density of driveways, the higher the crash rates. Both of these results appear to be intuitive. However, the biggest in impact on safety is median type, with the model showing that RM segments have a significantly lower total crash rate compared to TWLTL segments. The coefficient of -0.2638 implies that, for segments with similar AM features, the one with a raised median will have 23% lower crashes per million vehicle miles. The model further shows that the density of cross roads is not a factor.
TABLE 3 Impact of AM Features on Crash Rates
Variable All Raised
Median
TWLTL
Signal spacing (1000 feet)
-0.0798 (0.037)
-0.1276 (0.052)
-0.1144 (0.018) Cross road density
(per mile)
0.0126
(0.085) Driveway density
( per mile)
0.0100 (0.000)
0.0096 (0.000)
0.0090 (0.000) Median type
(1 for RM, 0 for TWLTL)
-0.2638 (0.001)
NA NA
Median opening density (per mile)
NA 0.0481 (0.030)
NA
Traffic per lane (in 1000 vehicles)
0.0453 (0.012)
0.1253 (0.000) Speed limit -0.0210
(0.001)
-0.0160 (0.033)
Through lanes 0.1679 (0.000)
0.1452 (0.074)
0.1726 (0.001) Land use
Constant 0.7910 (0.013)
-1.0664 (0.060)
0.8704 (0.016)
Samples 322 137 185
Adjusted R2 0.7684 0.7710 0.7778
NA= Not applicable 18
19 20 21 22 23 24
median openings are positively correlated to safety, meaning that the higher the densities, the higher the crash rates.
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For the TWLTL model, all the three relevant AM parameters are statistically significant. Signal spacing and driveway density have trends similar to the RM segments in their impact to crash rates. However, as opposed to the RM segments, the density of cross roads is a significant factor for TWLTL segments, with a positive impact to crash rates.
Crash rates by type of crash
Table 4 summarizes the results of impacts of AM features on crash rates by type of crash for the RM segments, the TWLTL and for all the segments combined. The results are based on a total of 11,510 angle, 9,885 rear-end, 1,850 side-swipe, 185 head-on, and 1,526 single vehicle crashes. The single vehicle crashes combine run off the road and fixed object crashes. Crashes recorded in the database as other or unknown were not included in the analysis.
With respect to angle crashes, the model results show that the densities of median openings and driveways for both RM and TWLTL segments are the only statistically significant factors. The higher the driveway densities, the higher the angle crash rates. Signal spacing does not appear to be a factor for angle crashes.
For rear-end crashes, signal spacing and driveway density are significant factors for both RM and TWLTL segments. However, their marginal impacts are greater in TWLTL segments than in RM segments. In addition, the density of cross roads is a significant factor with TWLTL segments but not with RM segments.
As for sideswipe crash rates, the models show that only signal spacing and density of access roads affects the crash rates for RM segments, while for TWLTL segments, it is the densities of access roads and driveways that have impact on the sideswipe crash rates. The trends of the impacts are similar to other crash types previously discussed.
For head-on crashes, only the density of cross roads has impact on safety and only in TWLTL segments. This might be due to the fact that vehicles to or from driveways use the TWLTL for the U-turns and left turns while on segments with RM the driveways operate as right in right out (RiRo) unless they are aligned with median openings.
In the single vehicle models, the AM variables that have significant impact are signal spacing and density of driveways for TWLTL segments and the density of cross roads for RM segments. As opposed to all the other crash types, for single vehicle crashes, the model indicates that the longer the signal spacing the higher the crash rates. This could be due the fact that with longer segments, vehicles can be able to attain higher speeds, and hence a higher potential for loss of vehicle control resulting in higher crash rates. Although the signal spacing seems to have negative impacts with respect to this crash type, the reduction in other types of crashes such as rear end and side swipe far outweigh the increase in single vehicle crashes because there are more rear end crashes than single vehicle and that the absolute marginal effect with respect to rear end crash rate is bigger than that of single vehicles.
TABLE 4a Impact of AM Features on Crash Rates by Types of Crash 1
2
Angle Rear End
Variable All RM TWLTL All RM TWLTL
Signal spacing (1000 feet) Access road
(per mile) Median type
(1 RM, 0 TWLTL)
-0.4378 (0.000)
-0.2155 (0.011)
Median openings (per mile)
NA 0.0985 (0.000)
NA NA NA
Traffic per lane (in 1000’s)
Constant 0.2862 (0.001)
Adjusted R2 0.6977 0.6221 0.7493 0.7223 0.7514 0.7325
3 4
5 TABLE 4b Impact of AM Features on Crash Rates by Types of Crash
Sideswipe Head on Single Vehicle
Variable All RM TWLTL All RM TWLTL All RM TWLTL
Sig. spacing (1000 feet) Access road
(per mile) Median type
(1 for RM)
-0.2300 (0.026)
-0.6309
(0.021) Med openings
(per mile)
NA NA 0.1129
(0.049)
NA NA NA
Traffic/ lane (in 1000’s) Speed limit -0.0443
(0.000)
-0.0332 (0.014)
-0.0440 (0.000) Through lanes 0.2377
(0.000) Constant -1.4322
(0.001)
Crash rates by severity
Table 5 summarizes the results of impacts of AM features on safety by severity and by type of median. The database contained a total of 92 fatalities, 11,172 injuries and 15,057 property damage only (PDO). Since there were few fatal crashes, no analysis was done with respect to fatal crash rates.
TABLE 5 Impact of AM Features on Crash Rates by Severity
Injury Property damage only
Access road (per mile) Median type
(1 for RM, 0 for TWLTL)
Median openings (per mile)
Traffic per lane (in 1000 vehicles)
0.0456
Speed limit -0.0224
(0.003)
Through lanes 0.1476
(0.003) Constant 0.2063
(0.575)
Based on the combined model, the results show that overall, injury are lower in RM segments than in corresponding TWLTL segments. The coefficient of -0.2390 means that, everything else being equal, RM segments have a lower crash rates by 21.3%. The results further show that driveway density is the only significant factor in both types of medians. The TWLTL segments also have the density of cross roads as a significant factor.
Discussion of Results
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This section summarizes the impacts of individual AM features on safety of midblock sections by crash types, crash severity and total crashes. Comparison of the findings with some of those obtained in the past studies reported in literature review of this paper.
Density of driveways
To quantify the impact of driveway density on crash rates, Equation 2 is used. For example, from the models for total crash rates (Table 3) decreasing driveway density by 1 driveway per mile will result in the following percent decrease in total crash rates, i.e.
∆y% = 100(
e
0.01*(-1)-1) = 1%The impact of driveway density is further evaluated based on a typical segment. Table 1 shows a typical segment used in this study has a driveway density of about 41.23 per mile (for all driveways on both sides of the roadway), this translates into a driveway spacing of approximately 5280/(0.5*41.23) = 264 ft. If a decision were made to increase the driveway spacing by 50% to about 400 ft, this translates to a driveway density of 26.4 per mile, which is a reduction of about 15 driveways per mile. In this case the model predicts a resulting reduction of crash rates of
∆y% = 100(
e
0.01*(-15)-1) = 13.9%
This is a very significant improvement in safety. With respect to crash types, the density of driveways is the only AM feature that is a significant factor in all crash types. However, quantitatively, its impact is more significant on angle crashes for both RM and TWLTL segments, and on rear-end crashes on TWLTL segments only. Based on the example above, a reduction of 15 driveways per mile would result in reduction in angle and rear-end crash rates of 12% and 11%, respectively for TWLTL. The impact on the other crash types for a similar reduction in driveway density will be in the rage of 4% to 5%.
With regard to severity, the marginal effect of driveway density on segments with TWLTL is smaller than that of RM in the injury models. Following the previous example, decreasing the driveway density by 15 would decrease injury crash rates by 3.8% and 8.5% on segments with TWLTL and RM, respectively. For property damage only crashes, the corresponding reductions in crash rates would be 13.2% and 10.1% for TWLTL and RM segments, respectively.
Overall, the models indicate that significant reductions in crash rates can be achieved by reducing the density of driveways.
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Density of cross roads
According to the models developed in this study, the density of cross roads has impact on total crash rates only on TWLTL segments. The coefficient of 0.0126 indicates that a reduction of one cross road per mile would result in a reduction of 1.3% in total crashes rates, not a very significant impact quantitatively. When analysis is done by crash types, cross roads have an impact on rear-end, sideswipe and head-on collisions on TWLTL segments, with percentage reduction in the crash rates of 2.3%, 2.5%, and 3.3% respectively for a reduction of 1 cross road per mile. For RM segments, cross roads has impact on sideswipe and single vehicle crashes, with crash rate reductions of 1.7% and 2.7% respectively.
With respect to crash severity, the models show that the density of cross roads has a significant impact only on the injury crash rates on TWLTL segments. With a coefficient of 0.0207, it means that a decrease of 1 cross road per mile would reduce injury crash rates by about 2%. With respect to property-damage only crash rates, the density of cross roads appears to be significant only when the combined model is used. When median specific models are used, the impact of the cross roads becomes too small to be significant on either type of median.
Signal spacing
The model results show that with respect to signal spacing (i.e., lengths of segments), the longer a segment is for both types of medians, the lower the crash rates. The coefficients of -0.1276 and -0.1144 for RM and TWLTL segments, respectively. Given the average signal spacing of 2,200 feet, increasing signal spacing by an average of 550 feet (25%) would result in total crash rate reductions of 7.3% and 6.5% for RM and TWLTL segments, respectively.
Similarly, the models for crash types show that signal spacing has impact on rear-end crash rates for both RM and TWLTL, sideswipe crash rates for RM and single vehicle crash rates for TWLTL segments. For rear-ends, the impact of signal spacing is about twice as much for TWLTL segments compared to RM segments. An increase in average signal spacing of 550 feet would results in 6.5% and 12.8% reduction in crash rates for RM and TWLTL segments, respectively. However, for single vehicle crash rates, increased signal spacing results in increased crash rates on TWLTL segments. An increase of 550 feet in average signal spacing for TWLTL segments would results in a 5.5% increase in these crash rates. However, it should be noted that the proportion of single vehicle crashes in the data based was relatively low. Hence, the combined effect over all crash types would a reduction in crash rates.
With respect to crash severity, signal spacing has impact only on property-only-damage crash rates on TWLTL segments. Increasing signal spacing by 550 feet will results in increase of property-damage only crash rates by 6.4%.
Density of median openings 1
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Median openings is a feature that is in RM segments only. Generally, the models show that the lower the density of median openings, the lower the crash rates. For the total crash rates, the model results indicate that for each reduction of 1 opening per mile would results in a total crash rate reduction of 4.9%. Most of this comes from a reduction in angle crashes (10.4%) and head-on collisihead-ons (12%). These reductihead-ons can further be disaggregated to 5.3% reductihead-on in crash that result in injury and 4.7% in crashes that result in property damage only.
Raised medians
Perhaps the single most effective way of reducing crashes is to convert a TWLTL segment to a RM segment. The coefficient of -0.2638 for median type dummy variable in the total crash rates model indicates that, everything else being equal, an RM segment has 23.2% lower total crash rates than a TWLTL segment. This saving is within the range 3-57% obtained in the 15 studies reported by Gluck et al. (6). The models further indicate that these reductions come from all crash types except single vehicle crashes. For example, for rear-end and sideswipe crashes, the segments with RM have lower rates by 19%, and 21%, respectively. The percentage reductions in these rear-end and sideswipe rates are smaller than the averages of percentage reduction in crash in synthesis documented by Gluck et al. (6). The reductions in angle, and head-on crash rates are larger compared to rear-end and sideswipe; segments with RM have lower rates by 34% and 50%, respectively, compared to segments with TWLTL. The percentage reductions in head on and angle crashes are comparable to the averages of percentages reported by Gluck et al. (6). Regarding crash severity, segments with RM have 21% and 33% lower injury and PDO crash rates than those with TWLTL. .
Implications of these results on effectives of AM techniques
This study, similar to several other cited previous studies, has clearly demonstrated the importance of the three of the most important AM policies, namely, choice of median type (RM vs. TWLTL), signal spacing, and densities of driveways and median openings for RM segments. This study has quantified the safety advantages of RM versus TWLTL segments, longer signal spacing and lower densities of driveways and median openings. However, implementing such a strategy designed to improve safety might lead to poor accessibility and may be even higher travel times, if a significant proportion of drivers are forced to travel longer routes to access their land-use destinations. Therefore a careful balance is required between safety considerations, mobility and accessibility. Another AM feature that needs proper planning is the cross roads that collect traffic and feed major roads. These cross road most of the time are aligned with median openings and might actually be signalized in the future upon meeting safety signal warrants. The implications of the potential future growth of these cross roads on AM have to be carefully considered.
Conclusions
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This study developed models that relate access management (AM) features to safety in the Las Vegas Valley. The AM features considered were median type, signal spacing and the densities of median openings, unsignalized cross roads, and driveways. These features are significantly correlated with traffic crash rates. Densities of driveways, cross roads, and median openings are positively correlated with crashes while signal spacing and raised medians are negatively correlated with crashes. Other factors such as posted speed limit and land use are negatively associated with crashes. The roadways with abutting residential land uses have fewer crashes than the commercial ones.
The results indicate that road segments with raised medians RM have lower crash rates than segments with TWLTL. The AM features also have more impacts on segments with TWLTL than those with RM. The major safety advantage RM over TWLTL is the reduction in severe crash types such as head-on and angle crashes. The difference in injury rates between the two types of medians can hardly be eliminated just by closing unsignalized access roads or driveways on segments with TWLTL. Due to huge safety savings that RM has over other types of medians, it is recommended that RM should be given priority in retrofit projects before other AM techniques such as consolidation of driveways are considered.
It is anticipated that the results of this study will assist the local jurisdiction in the Las Vegas valley in developing new access management strategies.
References
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