Project Papers - amguideproject
1 MODELING AND EVALUATING THE SAFETY IMPACTS OF ACCESS
2 MANAGEMENT (AM) FEATURES IN THE LAS VEGAS VALLEY
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9 Timur Mauga
10 Graduate Research Assistant
11 Department of Civil and Environmental Engineering, and
12 Transportation Research Center (TRC)
13 University of Nevada Las Vegas (UNLV) 14 4505 Maryland Parkway, Box 454007
15 Las Vegas, NV 89154-4007 16 email: [email protected]
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18 Mohamed Kaseko, PhD, Corresponding Author
19 Associate Professor of Civil Engineering
20 Department of Civil and Environmental Engineering, and
21 Transportation Research Center (TRC)
22 University of Nevada Las Vegas (UNLV) 23 4505 Maryland Parkway, Box 454015
24 Las Vegas, NV 89154-4015
25 Ph (702) 895-1360
26 Fax (702) 895-4401/3936 27 email: [email protected]
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31 Submitted to the Transportation Research Board
32 For
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33 Presentation at the 89 Annual Meeting, January 2010, and
34 Publication in the Transportation Research Record
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38 August 1 , 2009
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43 Pages 16
44 Words 5,430
45 Tables 6
46 Total 6,930
1 Abstract
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3 This paper presents results of a study that developed statistical models that relate access 4 management (AM) features with traffic safety in midblock sections of street segments. The 5 objective of the study was to evaluate and quantify the impact of the main AM features on traffic 6 safety in midblock sections of street segments. It is anticipated that the results of this study 7 would assist the local jurisdiction in the Las Vegas valley in developing AM policies and 8 programs. Models were calibrated for two main types of median treatments for street segments, 9 namely, raised medians (RM) and two-way-left-turn-lanes (TWLTL). Other than the median 10 type, other AM features considered were signal spacing and the densities of driveways, median
11 openings and unsignalised cross roads. Separate models were developed for the impacts on total 12 crash rates, types of crashes and severity. Results confirmed the intuitive expectation that all 13 these AM features do have fairly significant impact on safety. They reveal that high densities of 14 driveways, cross roads and median openings are positively associated with increased crashes 15 rates and severity. Use of raised medians as opposed TWLTL has very significant impact on 16 reduction of crash rates and severity.
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Introduction
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46 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.
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Parsonson et al. (7) reported two studies comparing TWLTL with RM in the State of Georgia, USA. The author reported that the segments with RM had smaller crash rates by 36% and 45%, and injury rate by 38% and 48%. Eisele and Frawley (8) reported a decrease of 17% and 58% in crash rates on two sites in Texas after RM replaced or TWLTL. Lewis (9) and Schultz, et. al. (10) conducted before and after analysis to evaluate the safety effectiveness of RM over TWLTL. Their studies reported mixed results. The authors concluded that the RM did not reduce the total crash rates but improved only specific crash types, namely, angle, fatal and 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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45 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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In this study, multivariate regression models were used to develop the relationships between AM features and crash rates by severity, types, and total. Twenty five representative straight urban roads classified by Nevada Department of Transportation (NDOT) AM guidelines as category five (principal arterials), six (minor arterials), and seven (collectors) in Las Vegas were selected. The selection was based on obtaining samples of street segments covering a variety of traffic, geometric, and land use characteristics. The selected roadways consisted of 337 street segments. A segment is a section of a road bounded by two consecutive signalized intersections. Since Las Vegas has only a few undivided roadways only roadways with RM and/or TWLTL are included in the study. Of the 337 segments, 145 had RM and 192 had TWLTL.
β β ……………...…………………….(1)
3.70 Speed limit (mph) TWLTL
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40.30 45 5.595.91 Density of cross roads (number per mile) TWLTL 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 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
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5.13 RM 30
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25 After evaluating and testing several models the exponential function of the following form was chosen due to the resulting high adjusted R
2
values:
M i i
X Y i N j ij j i e
,..., 3 , , 1 , 2 ,
.
= ∀ + ∑ =
28.88
5.39 RM 0
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19 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
- ε
1 Where
2 Y i is the crash rate for segment i in crashes per million vehicle-miles, computed as
CR i
3 Y i = 6 ( 365 L AT ) /
10 × × i i
4 CR i is the average number of crashes/year for segment i
5 L is the length of the segment i, in thousands of feet
i
6 AT is the average AADT over the 5 years of the study period
i
7 β is a constant term 8 β j is the coefficient for variable j
9 X is a the value of variable j for segment i
ij
10 i is the error term ε
11 M is the total number of segments
12 N is the number of variables
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14 The dependent variable was crash rate in crashes per million vehicle-miles. The 15 explanatory variables were AADT per lane, signal spacing in 1000’s of feet, median type (binary 16 variable, 1 for RM and 0 for TWLTL), density of driveways (number per mile), density of cross 17 roads (number per mile), density of median openings (number per mile), speed limit, number of 18 main through lanes, and land use, measured as the proportion of driveways serving residential 19 land uses.
20 The estimation of parameters in Equation 1 was done with Stata statistical software using 21 the nonlinear command nl. In estimating nonlinear equations, the software uses an iterative 22 modified Gauss–Newton algorithm in finding the minimum of the sum of squares error. The 23 impacts of the AM variables on safety were evaluated and quantified using Equation (2) below, 24 which is a relationship derived from equation (1) above. This equation gives an estimate of the 25 percent change in the dependent variable as a function of a change in the value of the dependent 26 variable is given as (17):
27 β . Δ
X j j
28 % Y 100 (
1 ) ………………………………………………………..(2) Δ = e −
29 Where % Δ Y is the percentage change in crash rate
30 Δ
X is the absolute change in the value of a the predictor
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33 Analysis and Results
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35 The dataset was divided into two subsets, one containing segments having RM and the other 36 containing segment with TWLTL. Table 2 summarizes the descriptive statistics of crash rates for 37 the segments combined and by type of median. The average, maximum, and standard deviation 38 values for segments with TWLTL are bigger than those for segments with RM except the means 39 of rear-end crash rates. The subsections below present the results of the analyses and discuss 40 them qualitatively.
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TABLE 2 Descriptive Statistics of Crash Rates by Collision Type and Severity
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15 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.
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Only (PDO) TWLTL 0.159 2.691 42.470 3.434
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
MVMT) Dataset Minimum Average Maximum Standard deviation All 0 3.878 12.591 2.543
2 Crash rate (per
The following sections provide a summary and discussion of the model results.
23 Similarly, the regression results for the model for RM segments show that three of the four AM parameters, namely, signal spacing, driveway density, and the density of median openings, have statistically significant impact of crashes per million vehicle miles. The trends for the impacts are as expected, with signal spacing being negatively correlated to safety, meaning
0.1726 (0.001) Land use
Constant 0.7910
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)
(0.030) NA Traffic per lane (in 1000 vehicles)
0.0453 (0.012) 0.1253 (0.000)
Speed limit -0.0210 (0.001)
(0.013)
TABLE 3 Impact of AM Features on Crash Rates Variable All Raised Median
NA= Not applicable
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TWLTL Signal spacing (1000 feet)
17 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.
Total crash rate
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- 0.0798 (0.037)
- 0.1276 (0.
- 0.1144 (0.018) Cross road density (per mile)
- 0.2638 (0.001) NA NA Median opening density (per mile) NA 0.0481
- 0.0160 (0.033) Through lanes 0.1679 (0.000) 0.1452 (0.074)
- 1.0664 (0.060) 0.8704 (0.016) Samples 322 137 185 Adjusted R 2 0.7684 0.7710 0.7778 median openings are positively correlated to safety, meaning that the higher the densities, the higher the crash rates.
-0.1639
(0.001)- 0.1222 (0.
- 0.2493 (0.000) Access road (per mile) 0.0168 (0.027)
- 0.4378 (0.000)
- 0.2155 (0.011) Median openings (per mile) NA 0.0985 (0.000)
- 0.0161 (0.035)
- 0.0418 (0.001) Through lanes 0.1456 (0.050) 0.1735 (0.007)
- 2.0371 (0.000)
0.9817
(0.012)
0.1424 (0.006) - 0.8579 (0.058) Samples 329 143 186 329 143 186 Adjusted R 2 0.6977 0.6221 0.7493 0.7223 0.7514 0
- 0.1684 (0.036) 0.1187 (0.000) 0.0977 (0.023)
- 0.2300 (0.026)
- 0.6309 (0.021) Med openings (per mile) NA NA 0.1129 (0.049) >.0944 (0.036)
- 0.1117 (0.>0.1369 (0.000)
- 0.1025 (0.000) Speed limit -0.0443 (0.
- 0.0332 (0.014)
- 0.0440 (0.000) Through lanes 0.2377 (0.000) 0.2910 (0.000)
- 0.8552 (0.120)
- 1.3446 (0.>3.0104 (0.000)
- 4.7681 (0.>2.9453 (0.000)
- 1.5645 (0.
- 1.9218 (0.000)
- 1.7306 (0.000) Samples 329 143 186 327 143 184 329 141 184 Adjusted R 2 0.6347 0.6897 0.6115 0.2974 0.2310 0.3178 0.9019 0.7183 0.6856
-0.0943
(0.038)- 0.1201 (0.038) Access road (per mile) 0.0159 (0.008)
- 0.2390 (0.009) NA NA -0.4014 (0.000) NA NA Median openings
-0.0192
(0.039)
- 0.0268 (0.
- 0.0157 (0.066) Through lanes 0.1476 (0.003)
0.1806
(0.003)
- 1.2043 (0.002)
0.1385
(0.756)
0.3913 (0.028) - 0.6527 (0.341) 0.6990 (0.076) Samples 328 143 185 328 143 185 R 2 0.6930 0.7091 0.6927 0.8315 0.7759 0.8648
- 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
- 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%.
- 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.
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45 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.
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A variable such as land use, though not an AM feature, has an impact on angle and single vehicle collisions. The results show that segments with more driveways serving commercial land-uses have more angle crashes than their residential counterpart. On the other hand, segments with more driveways serving residential land-uses have more single vehicle crashes than their commercial counterpart.
(0.003) 0.1385 (0.000)
NA NA NA Traffic/ lane (in 1000’s) 0.0741
Median type (1 for RM)
0.0132 (0.000) 0.0046 (0.029)
0.0030 (0.029) 0.0029 (0.077)
0.0269 (0.067) Driveways ( per mile)
0.0302 (0.025) 0.0333 (0.060)
0.0170 (0.032) 0.0254 (0.009)
Access road (per mile) 0.0177 (0.006)
Sideswipe Head on Single Vehicle Variable All RM TWLTL All RM TWLTL All RM TWLTL Sig. spacing (1000 feet)
5 TABLE 4b Impact of AM Features on Crash Rates by Types of Crash
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0.4826 (0.396)
Land use -0.3067 (0.092) Constant 0.2862 (0.001)
(0.076)
(0.000) 0.0444 (0.096) Speed limit -0.0171
(0.000) 0.0855 (0.000) 0.1210
NA NA NA Traffic per lane (in 1000’s) 0.1805
Median type (1 RM, 0 TWLTL)
0.0030 (0.099) 0.0077 (0.000)
0.0084
(0.000)
0.0076 (0.000)0.0086 (0.000) 0.0087 (0.002)
0.0230 (0.010) Driveways ( per mile)
Variable All RM TWLTL All RM TWLTL Signal spacing (1000 feet)
2 Angle Rear End
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TABLE 4a Impact of AM Features on Crash Rates by Types of Crash
0.0919 (0.094) Land use 0.6316 (0.058) Constant -1.4322 (0.001)
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Speed limit -0.0224 (0.003)
0.1291 (0.099) 0.1129 (0.042)
Land use -0.3068 (0.070) Constant 0.2063 (0.575)
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0.0456 (0.030) 0.1380 (0.000)
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22 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.
0.0381 (0.050) 0.1519 (0.000)
(0.009) NA Traffic per lane (in 1000 vehicles)
With regard to PDO crash rates, the marginal effect of driveways is larger on segments having TWLTL than those with RM as expected. The density of median openings has almost the same impact on both injury and PDO crashes. Signal spacing and cross roads are significant on segments having TWLTL only. Also residential areas have less PDO crashes compared to commercial ones.
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Crash rates by severity
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(per mile) NA 0.0513 (0.019) NA NA 0.0456
9 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 Variable All RM TWLTL All RM TWLTL Length (1000 feet)
0.0207
(0.018)
0.0153 (0.011)Driveways ( per mile) 0.0026 (0.020)
0.0059 (0.008)
0.0026
(0.044)
0.0102 (0.000) 0.0071 (0.000)
0.0094 (0.000) Median type (1 for RM, 0 for TWLTL)
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46 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)
∆y% = 100(
e 0.01*(-15)
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.
Other studies have reported somewhat larger marginal effects. Gluck et al. (6) generalized an impact of 4% for every new driveway in a mile; almost four times bigger than the values reported in this research. The authors also reported an increase of 0.09 to 0.13 in crash rates (crashes per million vehicle miles) on roads having TWLTL or RM in urban and suburban areas. Eisele and Frawley (8, 18) used a univariate linear model and reported slopes of access density as 0.0618, and 0.1225, for segments with RM, and TWLTL. Their sample size was only
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Discussion of Results
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23. These results indicate that increasing the access density by 8 (2 driveways in a 0.25 mile segment with TWLTL) will increase the crash rate by 1 per million vehicle miles. The size the marginal impacts might have been caused by inclusion of intersection crashes in the analysis.
Overall, other than driveway density, signal spacing in probably the next most significant factor in impact on safety.
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43 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
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%.
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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 median openings
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46 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 collisions (12%). These reductions can further be disaggregated to 5.3% reduction 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.
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For retrofit projects, the single most effective strategy is converting a TWLTL segment into an RM segment. Though it may be costly, its safety benefits over the lifetime of a facility may far outweigh those initial costs. The study results have also shown that consolidating driveways, hence reducing the driveway density, can also have very significant improvement in safety of a roadway.
Conclusions
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45 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.
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