Development of Highway Safety Policies by ...

KSCE Journal of Civil Engineering (2018) 22(4):1418-1426 Copyright ⓒ2018 Korean Society of Civil Engineers DOI 10.1007/s12205-017-1859-0

Transportation Engineering

pISSN 1226-7988, eISSN 1976-3808 www.springer.com/12205

TECHNICAL NOTE

Development of Highway Safety Policies by Discriminating Freeway Curve Alignment Features Soyoung Jung*, Kai Wang**, Cheol Oh***, and Jaenam Chang**** Received June 1, 2016/Revised January 23, 2017/Accepted April 11, 2017/Published Online June 14, 2017

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Abstract In the Republic of Korea, severe crashes with injuries and fatalities have most frequently occurred in freeway sections with combinations of horizontal and vertical curves. To address this issue related to geometric complexness, this study quantitatively verified the adequacy of Korean highway safety policy and identified supplemental strategies required for the freeway sections with horizontal and vertical curve combinations. Based on in-depth road geometric data, this study combined K-means clustering with binomial logit regression to identify the effects of contributing factors on cluster-based crash severity levels. Resultantly, six clusterbased crash severity estimation models were produced. Single vehicle to roadside protective facility and multivehicle crash types were significant to decrease the likelihood of injury crashes for both full- and three cluster-based regression models. Compared with full data model, driver’s age, action, crash occurrence time, and traffic management related factors were significantly identified in only single cluster. The resultant findings show that multidirectional highway safety policies are further required for the current Korean highway safety policy associated with curve alignment features. This study will assist in researchers and field practitioners’ decision-making for future cost-effective highway safety policies provision related to freeway sections with geometric complexness. Keywords: highway safety, geometric complexness, cluster-based crash severity, multidirectional, cost-effective, policies ··································································································································································································································

1. Introduction In the Republic of Korea (hereafter, Korea), freeway crashes remain a significant problem because injuries and fatalities involved in a freeway crash has been the greatest of all road types. Approximately 70% of Korea is mountainous with many horizontal or vertical curves and combinations in freeways. Highway safety has been threatened by the complexness in freeway geometric alignments. The recent trends of severe crashes with injuries and fatalities occurring on Korean freeways support the safety issue associated with horizontal curves combined with vertical curve. According to Korea Expressway Corporation (KoEX)’s crash data in 2014, this combined type of geometric alignments has comparatively led to more severe crashes than any other types of geometric alignments such as straight and tangent sections, horizontal or vertical curve sections. The greatest number of severe crashes has occurred in freeway sections with horizontal and vertical curves combinations since 2009. Note that the section is defined by a certain area in freeway stretch. Several previous studies have also highlighted that the complicated road geometries threatened highway safety. Hassan

et al. (2002) stated that the complicated road geometries imply a higher likelihood of distortion of an individual driver’s visual information, resulting in severe crashes (Hassan et al., 2002). In their study, the significant parameters affecting horizontal radius perception included the overlapping vertical curve’s type, actual radius, turning directions and sight distance. A driving simulator study by Bella also showed that drivers’ travel speeds are consistent with the hypothesis that horizontal curves appear sharper or flatter when overlapped with crest or sag vertical curves, respectively (Bella, 2006). Some other studies identified further extent of curve alignment features that significantly affects crashes such as: multiple travel lane, Average Annual Daily Traffic (AADT), horizontal curve length and curvature, vertical curve length and the algebraic difference in vertical gradients, the percentage of overlap between vertical and horizontal curves, the ratio between horizontal and vertical curve radii, and the location of the vertical intersection point with respect to the horizontal curve (Ikeda and Mori, 2005; Iyinam et al., 2003; Hanno, 2004; Caliendo et al., 2007; Bauer and Harwood, 2014). Additionally, criteria in superelevation or increment in reverse horizontal curve radius were provided especially in the

*Member, Assistant Professor, School of Public Technology Service, Dongyang University, Dongducheon 11307, Korea (Corresponding Author, Email: [email protected]) **Graduate Research Assistant, Dept. of Civil & Environmental Engineering, University of Connecticut, Storrs, CT 06269-3037, USA (E-mail: [email protected]) ***Member, Professor, Dept. of Transportation and Logistics Engineering, Hanyang University, Ansan 15588, Korea (E-mail: [email protected]) ****Senior Engineer, Dept. of Roads and Airports, Korea Engineering Consultants Corp., Seoul 05288, Korea (E-mail: [email protected]) − 1418 −

Development of Highway Safety Policies by Discriminating Freeway Curve Alignment Features

horizontal curves combined with vertical curves to minimize the negative effects of curve alignment features (Easa and Halim, 2006; Torbic et al., 2014). With the use of clustering approaches, some studies have also focused on addressing the heterogeneity in crashes to discover reliable road safety analysis associated with road geometries. Fernandes and Neves (2013) grouped road segments into uniform road environments by using a hierarchical clustering algorithm and identified the influence of pavement conditions on road safety in each homogeneous road segment. Similarly, a study by Mohamed et al. (2013) considered curve and hillcrest that can segment homogenous crash groups with the use of latent class and K-means clustering analysis. Recognizing the risk of geometric complexness, the Korea Ministry of Land, Transport and Maritime Affairs (KMLTM) has provided a highway safety improvement policy for freeway sections with horizontal and curve combinations (KMLTM, 2011). However, the KMLTM policy contains only “constructing roadside protective facilities such as fence or guardrail”, which is neither comprehensive nor data-driven. In other word, the KMLTM policy has not reflected the following issues: whether or not the current KMLTM policy is appropriate to widely employ it in the Korean freeway system; and what policies are additionally needed. Unfortunately, in Korea, there have been no technical reports, studies or corresponding policy implementations to quantitatively address the aforementioned issues (KMLTM,

2009; KMLTM, 2011). Furthermore, there are financial or spatial limitations to redesign or reconstruct the current freeway curves. From a review of the literature, some studies have dealt with Korean freeway crashes (Choi et al., 2014; Chung, 2011). The studies encompass the issues including developing crash modification factors, identifying speed limit effect for truck traffic operation, and formulating mitigation measures for reducing nonrecurrent congestion delay caused by accidents. However, few studies have considered in-depth geometric features in freeway sections, especially with horizontal and vertical alignment combinations, using clustering techniques. Moreover, few studies have provided the methodological process to develop multifaceted data-driven strategies by uniform groups of geometric features, emphasizing a need for relevant research. Hence, this study aims to quantitatively assess the adequacy of current KMLTM policy and to identify cost-effective policies required for freeway sections with horizontal and vertical curve combinations. This study is the first attempt to quantitatively improve the current governmental highway safety policies using in-depth Korean freeway geometric data.

2. Data Preparation The study area includes 234 combined sections with horizontal and vertical curves in Joongang, Namhae, and Honam freeways, which comprise 151 km of stretch in total. The following factors

Table 1. Descriptive Statistics for Road Geometric Features Variable

Description (unit) R : Radius of transition curve at the beginning of a curve section (m) R : Curve radius (m) R : Radius of transition curve at the end of a curve section (m) L : Transition curve length at the beginning point of a curve section (m) L : Curve length (m) L : Transition curve length at the end point of a curve section (m) S : Difference between two grades (%) SC: Crest or Sag btc

h

Horizontal curve

etc

btc

h

etc

d

Curve types A1 to A6 R : Curve radius (m) L : Curve length (m) RL : Proportion of vertical curve length to total length of horizontal curve section (%) K: Curve rate, (m/%) VDis: Distance between vertical and horizontal curve vertices (m) RDis: Proportion of VDis to total length of horizontal curve section (%) RR: Proportion of horizontal curve radius to vertical curve radius (%) AADT: Average annual daily traffic (vehs/day) TR: Truck traffic proportion (%) SSD1: Standard stopping sight distance (m) SSD2: Observed sight distance (m) SSD3: Difference between SSD2 and SSD1 (m) Number of bridges or tunnels in each section Whether or not each section includes a merge or diverge to an IC/rest area v

Vertical curve

Traffic Sight distance Road structure

v

v

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Standard deviation 134 736 360 114 460 10,000 1,365 1,214 134 736 364 113 67 422 149 52 60 1365 401 248 67 371 150 53 0 13 1,079 1,231 Crest=110 sections, Sag=124 sections A1=73sections, A2=15 sections, A3=23 sections, A4=70 sections, A5=31 sections, A6=22 sections 3,794 120,992 20,204 18,376 60 1,000 336 156 Minimum

Maximum

Mean

10

225

61

38 0 0 1 41,515 4 137 110 0 0

39

1,210 203 184 1,339 165 165 160 26 24 133 21 21 335,886 128,755 56,354 26 12 5 173 155 8 620 197 77 474 55 70 2 0 1 Yes= 81 sections, No= 153 sections

Soyoung Jung, Kai Wang, Cheol Oh, and Jaenam Chang

Fig. 1. Types of Vertical Curves Table 2. Descriptive Statistics for Crash-related Variables Variable

Variable category

Crash sever- Crash with fatalities and injuries ity (response) PDO crash Spring Summer Season Autumn Winter AM peak (5 h-8 h) Non-peak (9 h-16 h) Crash time PM peak (17 h-18 h) Evening/Midnight (19 h-4 h) Weekday Day of week Weekend Adverse Rain/snow/fog weather Others (clear/cloudy) Pavement Asphalt material Concrete Single vehicle crash collided with roadway facilities Other single vehicle crash Collision type Multivehicle crash collided with roadside facilities Other multivehicle crash (sideswipe/rear-end/head-on) Speeding Distraction due to drowsiness, alcohol Cause of effects, equipment operation crash Poor wheel maneuvering Other cause (no safe distance/passing violation/without license) Passenger car Vehicle type Van/bus Truck Female Driver’s sex Male Aged less than 30 Driver’s age Aged 30 to 49 Aged greater than or equal to 50

Sample proportion (%) 29.6 70.4 20.4 31.1 25.8 22.7 19.7 36.9 8.0 35.4 64.4 35.6 35.6 64.4 50.4 49.6 60.9 16.8 10.3 12.0 33.9 39.3 20.4 6.4

50.6 21.5 27.9 14.6 85.4 21.2 60.5 16.5 Min: 0 minute Time taken from the first responder Management Max: 275 minutes arrival to road blocking release time Mean: 33 minutes after crash occurrence Standard deviation: 31 Note. Crash time was separated based on average daily traffic trends in study area.

are common in the study area: presence of fixed rigid median, two-way freeway with four lanes, and a design speed limit of 100 km/h. Note that only single horizontal curve sections overlapping with single vertical curves were considered as the study area. We also note that two following circular curves and relevant spiral transition curves did not exist in the study area. The current study considered horizontal and vertical alignment features affecting highway safety found in past studies and international policies on the geometric design of highways (KMLTM, 2012; AASHTO, 2004). For each freeway section with horizontal and vertical curve combinations, horizontal and vertical alignment data were collected by road longitudinal and cross-sectional plans of the KoEX. Utilizing the reference station in the KoEX’s road plan, individual crash observation was linked to each section. Crash dataset involved the relevant human, vehicle, environment and traffic data fields, which were also provided by KoEX’s operational analysis supportive information system. Totally, 466 crashes were observed in the study area from 2004 and 2008, which was classified into 145 crashes with fatalities and injuries, and 321 PDO crashes. During the five years, the number of crash occurrences in each section ranged 1 to 11 with two crash occurrences on average. There were no geometric changes in the study during the same time periods. Table 1 and Table 2 summarize the road geometry and crash related variables used in this study, respectively. Note that six types of vertical curves were observed in the study area as shown in Fig. 1. In Table 2, the categories in each explanatory variable were classified by their similarities with meaningful sample size that is more than 30 observations.

3. Methodologies Road geometry is one of the most significant factors that cause severe crashes. The road geometries are spatially specific. If we consider all crash observations that occur under various road geometric conditions, the effect of specific road geometry on crash occurrences may not be identified in the full data model because the effects of other factors with a large sample size on crashes are comparatively powerful. For the study aim mentioned in Introduction, this study made an effort to identify whether or not

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the impacts of contributing factors on crash severity levels are consistent throughout the entire freeway sections especially with horizontal and vertical curve combinations. Relevantly, this study did not rely on the full crash data-based model, so the clustering analysis was employed to separate freeway crashes occurred in horizontal and vertical curve combinations with the use of a few road geometric factors. The clustering approach enables us to identify crash groups where fog contributes to death, which mitigates the effect of confounding variables that may cause biased results (Jung et al., 2016). To perform the task, cluster-based regression models were built in this study. The clustering approach distributes road geometric elements by maximizing the homogeneity of elements within clusters (Hair et al., 1998). It relieves the influence of confounding and unavailable variables that may lead biased results. In this study, K-means clustering was employed to separate freeway curve alignment features into homogeneous clusters. Binomial logit regression was also conducted to identify the impacts of contributing factors on the K-means cluster-based crash severities. Note that crash severity in the current study indicates the most severe injury level involved in a crash occurrence. 3.1 Selection of Clustering Technique Typical clustering techniques are partitioning-based (such as K-means), hierarchical-based (such as Ward’s method or the single linkage method) and density-based approaches including Latent Class Clustering (LCC) (Mohamed et al., 2013). To assign data into homogeneous groups, LCC was preliminarily performed in this study because there is no need to normalize or standardize the data a priori (Depaird, 2008; Magidson and Vermunt, 2002; Vermunt and Magidson, 2002) for employing LCC. The LCC result in the current study, however, showed that nearly all datasets were assigned to the same cluster, which leads to difficulties in differentiating the patterns among clusters. Therefore, K-means clustering analysis was then conducted to identify these patterns due to its simplicity. The K-means clustering predefines the number of clusters and an objective distance function (such as Euclidean distance) categorizes the data by optimizing the objective function. To determine the optimum number of clusters in K-means clustering, different numbers of clusters were tested using the Calinski and Harabase pseudo-F index (Calinski and Harabasz, 1974). A large Calinski and Harabase pseudo-F index denotes highly accurate separation between clusters. An abundance of clustering variables is avoided because it increases the odds that the variables are no longer dissimilar (Mooi and Sarstedt, 2011). To address this issue, the correlation between clustering variable candidates was first tested before K-means clustering.

with fatality and injury. To quantify the effects of factors on the binary injury severity levels, a better approach is to fit the standard binomial logit regression model, which is formulated as (Hosmer and Lemeshow, 2000): logit (P) = log [P/(1 − P)] = α + βX

where α is the intercept, β is the vector of slope parameters, X is a vector of explanatory variables and P is the response probability to be modeled. In this study, PDO crash was a base category. With the logit formulation, accordingly, equation (1) is specified by: P(1) = P(Y = crash with fatalities and injuries | X) = exp (α + βX) / [1+ exp (α + βX)]

(2)

P(0) = P(Y = PDO) = 1 − P(1) = 1 / [1 + exp (α + βX)]

(3)

Backward elimination, which better removes multi-collinearity than forward or stepwise selection, was then conducted to select the best multiple regression model (Chatterjee et al., 1999).

4. Results and Discussion 4.1 K-Means Clustering Analysis For the reliable clustering, highly correlated variables were eliminated from a list of clustering variable candidates used in this study. Absolute Pearson correlations above 0.90 are always problematic (Mooi and Sarstedt, 2011). Employing this value of 0.9, variables eliminated from a list of clustering variable candidates were: cross slope, horizontal curve curvature, and the proportion of vertical curve curvature to design standard of the vertical curve curvature. As shown in Fig. 2, six-cluster model outperforms others in the K-means clustering, achieving the highest Calinski and Harabase pseudo-F index. A summary of geometric patterns for six clusters is also provided in Table 3. According to Table 3, K-means clustering classified total 234 freeway sections into: five clusters with 15% to 25% of total segments and one cluster (cluster 6) with only 2% of total segments. The clustering variables that discriminate clusters included horizontal curve length, rate of vertical curve curvature, distance between horizontal and vertical curve

3.2 Cluster-Based Crash Severity Estimation Based on the extent of human or economic damage, injury severities involved in each crash are categorized into binary levels, i.e., either a Property Damage Only (PDO) crash or one Vol. 22, No. 4 / April 2018

(1)

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Fig. 2. Post Estimation Index with Use of K-means Clustering

Soyoung Jung, Kai Wang, Cheol Oh, and Jaenam Chang

Table 3. Summary of K-means Clustering Profile C1 Total C2 C3 C4 C5 C6 (234) (17%) (25%) (15%) (22%) (19%) (2%) [1360] [1451] [1303] [1159] [1648] [1190] [1302] Curve radius (R ) Curve length (L ) [401] [376] [562e] [333] [351] [328] [379] Beginning transition curve radius (R ) [285] [261] [258] [282] [307] [303] [416] Horizontal Alignment Beginning transition curve length (L ) [118] [110] [111] [120] [127] [119] [142] Ending transition curve radius (R ) [288] [265] [258] [289] [307] [313] [396] Ending transition curve length (L ) [119] [114] [110] [125] [127] [119] [133] [20177] [23469] [18880] [17495] [19880] [11321] [111914] Curve radius (R ) Curve length (L ) [335] [311] [328] [332] [361] [343] [300] Curve rate (K) [203] [235] [186] [175] [199] [123] [1119] Distance between vertices (VDis) [165] [130] [241] [175] [132] [135] [68] Difference between two grades (S ) [2.6] [1.5] [2.6] [2.6] [3.0] [3.4] [0.3] [61] [56] [48] [65] [71] [70] [46] Proportion of vertical to horizontal length (RL ) Proportion of Dis to horizontal curve length (RDis) [26] [24] [31] [31] [24] [24] [9] Proportion of horizontal to vertical radius (RR) [21] [25] [19] [21] [18] [14] [105] Type (SC) Vertical Alignment Sag {53%} {56%} {68%} {32%} {40%} {53%} {100%} Crest {47%} {44%} {32%} {68%} {60%} {47%} {0%} Curve type (Type A) Type A1 {31%} {28%} {15%} {47%} {44%} {29%} {0%} Type A2 {7%} {8%} {7%} {8%} {6%} {7%} {0%} Type A3 {10%} {18%} {12%} {8%} {2%} {7%} {40%} Type A4 {30%} {26%} {38%} {16%} {33%} {33%} {0%} Type A5 {13%} {13%} {18%} {8%} {6%} {14%} {60%} Type A6 {9%} {7%} {10%} {13%} {9%) {10%} {0%} AADT [32211] [33305] [15091] [55522] [40812] [25104] [32383] Traffic Truck percentage (TR) [12] [14] [10] [14] [11] [13] [15] Standard stopping sight distance (SSD1) [154] [153] [156] [153] [153] [155] [155] Sight Observed sight distance (SSD2) [197] [197] [197] [190] [214] [183] [204] Distance [43] [44] [41] [37] [61] [28] [49] Difference between SSD1 and SSD2 (SSD ) Number of structures (bridges or tunnels) 0 {65%} {49%} {52%} {86%} {81%} {60%} {60%} 1 {29%} {46%} {37%} {5%} {19%} {31%} {40%} {6%} {5%} {11%} {9%} {0%} {9%} {0%} 2 Structure and Interchange (IC) area Facility No {85%} {92%} {85%} {83%} {85%} {84%} {40%} Yes {15%} {8%} {15%} {17%} {15%} {16%} {60%} Rest area No {96%} {95%} {95%} {92%} {98%} {100%} {60%} Yes {4%} {5%} {5%} {8%} {2%} {0%} {40%} Note. The total number of freeway sections with horizontal and vertical curve combinations is 234; Numbers in “[ ]” are means for continuous variables; Numbers in “{ }” are percentages for categorical variables; Percentage of sections with horizontal and vertical combinations in each cluster per total sections; and The numbers in bold are factors for the cluster that significantly vary from the total sample, which are used to identify each cluster. a

Variable (code)

d

b

h

h

btc

btc

etc

etc

v

v

d

v

c

d

a

b

c

d

e

vertices, type of curve, AADT, and the number of structures and facilities. With the current speed limit of 100 km/h and 4-travel lanes in the study area, the KMLTM provides freeway design standards for the following clustering variables (KMLTM, 2009; KMLTM, 2012): (1) 110 meter minimum for horizontal curve length; (2) 60 meter minimum for transition curve length at the beginning of a horizontal curve; (3) 85 meter minimum for vertical curve

length; (4) distance between horizontal and vertical curve vertices is recommended to be zero; (5) proportion of horizontal radius to vertical radius ranging from 5% to 20%; (6) average daily traffic ranging from 23,000 for level of service (LOS) A to 85,300 for LOS E; (7) 60 m/% minimum and 36 m/% minimum for crest and sag curve curvature rates, respectively. The resultant mean horizontal curve radii in all clusters were satisfied by freeway design standards provided by the KMLTM.

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The resultant mean horizontal or vertical curve lengths and vertical curve curvatures throughout all clusters also met the minimum design standards. There is no specific index to choose clustering variables that can separate each cluster, which is the most challenging in the clustering analysis. Theoretically, the mean of a certain variable are compared across all clusters (Depaire et al., 2008; Fernades and Neves, 2013; Kaplan and Prato, 2014; Mohamed et al., 2013). If the mean of a certain variable is apparently high or low in one cluster compared to those in the other clusters, then, the certain variable is considered as a clustering variable to discriminate the cluster. Considering this rule and cluster profile, each cluster of freeway sections with horizontal and vertical curve combination is labeled as follows: (1) Cluster 1: Existence of single bridge or tunnel; (2) Cluster 2: Excessively long horizontal curve length, and lack of vertex conformity; (3) Cluster 3: Type A1 crest, and high AADT (LOS D); (4) Cluster 4: Long horizontal curve radii, and large difference between stopping sight distance and observed sight distance; (5) Cluster 5: Large difference between two grades; (6) Cluster 6: Excessively long vertical curve radii (large Rv), slow grade (large K), high ratio of horizontal to vertical curve radius (large RR), sag (types A3 and A5), and existence of facilities (such as IC or rest areas). Note that Cluster 6 contains only five freeway sections with vertical and horizontal combinations, 2% of total sections. The

trends of clustering variables in Cluster 6 are different from those in the other five clusters. These results for Cluster 6 imply that K-means clustering treats Cluster 6 as an outlier group, possibly due to the very small sample. For the unbiased results with sizable observations, therefore, crash severity estimation models for Clusters 1 to 5 are discussed as follows. 4.2 Cluster-Based Crash Severity Estimation Employing binomial logit regression, the effects of contributing factors on cluster-based crash severity levels are shown in Table 4. Note that PDO crash was base case for response variable and binary coding for each explanatory variable category was employed. Except for cluster 6 with only nine crashes, all cluster-based binomial logit regression models have sizable sample data and high classification accuracies to predict the likelihoods of crash severity levels ranging 60% to 85%. Sensitivity rates throughout clusters were shown ranging 51% to 82%, which confirms the high model performance to predict the probabilities of injury crashes in particular. Additionally, cluster-based binomial logit regression models were statistically validated by leave-one-out cross validation method. Each time, one observation was withheld from each cluster dataset used for building the cluster-based binomial model. The restricted model was then compared to the model using all observations involved in cluster-based model. The process repeated until all the observations were tested. For all of the cluster-based and full data models, high and similar prediction accuracies between an estimated model and the model

Table 4. Cluster-Based Crash Severity Estimation Cluster 1

Cluster 2

Cluster 3

Cluster 4

Cluster 5

Cluster 6 - Very large R - Very slow grade - High RR - Sag (type A3:40% vs. type A5: 60%) - Many facilities 9 0.13

Full data

v

Cluster characteristics

- 1 bridge/ tunnel

- Type A1 crest - Long R - High AADT - Large SSD

- Very long L - Large VDis

h

h

d

- Large S

d

Crash observations 80 110 86 90 92 466 0.0007