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Empirical calibration of a roadside hazardousness index for Spanish two-lane rural roads
Accident Analysis and Prevention 42 (2010) 2018–2023
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Empirical calibration of a roadside hazardousness index for Spanish two-lane rural roads ˜ José M. Pardillo-Mayora ∗ , Carlos A. Domínguez-Lira, Rafael Jurado-Pina Department of Civil Engineering-Transport, Technical University of Madrid (UPM), C/Profesor Aranguren s/n, 28040 Madrid, Spain
a r t i c l e
i n f o
Article history: Received 12 March 2010 Received in revised form 11 June 2010 Accepted 15 June 2010 Keywords: Traffic safety Roadside Two-lane rural roads Run-off-road Hazardousness scale
a b s t r a c t Crash records and roadside data from Spanish two-lane rural roads were analyzed to study the effect of roadside configuration on safety. Four indicators were used to characterize the main roadside features that have an influence on the consequences of roadway departures: roadside slope, non-traversable obstacles distance from the roadway edge, safety barrier installation, and alignment. Based on the analysis of the effect of roadside configuration on the frequency and severity of run-off-road injury crashes, a categorical roadside hazardousness scale was defined. Cluster analysis was applied to group the combinations of the four indicators into categories with homogeneous effects on run-off-road injury crashes frequency and severity. As a result a 5-level Roadside Hazardousness Index (RHI) was defined. RHI can be used as reference to normalize the collection of roadside safety related information. The index can also be used as variable for inclusion of roadside condition information in multivariate crash prediction models. © 2010 Elsevier Ltd. All rights reserved.
1. Introduction and background Run-off-road (ROR) crashes occur when an errant vehicle encroaches into the roadside and either overturns or collides with a non-traversable obstacle or with the terrain. Roadside conditions are determinant of the consequences of a roadside encroachment. Formal research on roadside safety started 50 years ago. Stonex (1960) found that 35% of traffic fatalities occurred as a consequence of roadside encroachments and identified a number of factors that contribute to increase ROR crash severity, including the presence of non-traversable obstacles in the vicinity of the roadway, steep roadside slopes, deep ditches, and inadequate guardrail terminal configuration. Since then, numerous researchers have examined the effect on the frequency and severity of crashes of particular roadside features (Good et al., 1987; Gattis et al., 1993; Michie and Bronstad, 1994; Viner, 1995; Zegeer and Council, 1995; Kennedy, 1997; Wolford and Sicking, 1997; Ray, 1999). The results of these studies demonstrate that the presence of unprotected nontraversable obstacles on the roadside, their offset from the roadway edge, the degree of side slope and roadway geometrics are the main factors of ROR crash risk. Two approaches have been followed to model the relationship between roadway and roadside features and ROR crash risk. One is based on the adjustment of generalized linear regression mod-
∗ Corresponding author. Tel.: +34 913366652; fax: +34 913366654. E-mail address: [email protected] (J.M. Pardillo-Mayora). 0001-4575/$ – see front matter © 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.aap.2010.06.012
els to estimate ROR crash frequencies using exposure and relevant highway and roadside parameters as covariates. The frequency of crashes in a given highway section is treated as a random variable that takes discrete integer non-negative values distributed according to a Poisson distribution. A generalization of the Poisson form that allows the variance of the model to be over-dispersed results in the negative binomial model. Council and Stewart (1996) and Lee and Mannering (2002) have used Poisson and negative binomial regression models to develop ROR crash prediction models. A second approach, usually referred to as encroachment-based, uses a series of conditional probabilities of the sequence of events that lead to a ROR crash following the encroachment of an errant vehicle on the roadside (Mak, 1995; Mak et al., 1998). The main obstacle in the development of this type of models is the lack of encroachment frequency data. Miaou (1997) proposed a method to estimate vehicle roadside encroachment frequencies and the probability distribution of lateral extent of encroachments using existing crash-based prediction models. As Lee and Mannering (2002) point out, the lack of detailed roadside data, due primarily to the cost of collecting and maintaining such data, has been an obstacle to the development of detailed statistical models of the relationship between roadside features and ROR crash frequency and severity. To that respect, a systematic approach to characterizing roadside condition is a useful tool to facilitate roadside data collection and to support the ROR crash analysis. Until present, the main effort in this direction was conducted in the US by Zegeer et al. (1988a,b) in the framework of a major study for FHWA conducted with the objective
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of quantifying the benefits of lane widening, shoulder widening, and shoulder surfacing. Detailed traffic, crash, roadway, and roadside data were collected on 4951 miles of two-lane rural roads. Statistical testing was used along with log-linear modeling to determine the expected crash reduction related to various geometric improvements. A method to rate the safety of roadside conditions was established as part of the research. The resulting Roadside Hazard Rating (RHR) scale is a subjective measure of the hazard associated with the roadside environment. It was based on a review of previous roadside research results and on the results of a workshop involving thirteen highway and roadside safety professionals. Roadsides are evaluated on a scale from 1 to 7 (1 being the best) based on side slopes, clear zone, and distance to the nearest fixed object. The ratings are determined from a 7-point pictorial scale and the data collectors should use the rating value that most closely matches the roadside hazard level for the roadway section in question. The rating values indicate the crash likelihood and damage expected to be sustained by errant vehicles on a scale from one (low likelihood of an off-roadway collision or overturn) to seven (high likelihood of a crash resulting in a fatality or severe injury). RHR has proved to be a valuable tool to overcome the difficulties in characterizing roadside condition and is frequently used for this purpose in road safety studies. Nevertheless, to a certain extent its practical application is based on subjective judgment, and therefore subject to variations when applied by different observers. Additionally, the definition of RHR levels was based on the synthesis of existing knowledge and expert opinion, which is a valid methodology when applied adequately as it was the case, but lacks an explicit validation based on empirical evidence. The research reported in this article departed from RHR as initial reference and aimed at defining a new roadside condition classification procedure for Spanish two-lane rural roads based on objective indicators of roadside configuration to facilitate normalized collection of roadside data and its application to roadside safety analysis. It was also intended to assess the significance of the effect of the resulting roadside condition scale levels on ROR crash frequency and severity rates. 2. Method The research was conducted in four phases: 1. Roadside configuration categorization. 2. Data collection. 3. Grouping of the combinations of the four indicators into categories with homogeneous effects on run-off-road injury crashes frequency and severity and definition of a Roadside Hazardousness Index (RHI). 4. Assessment of the significance of the differences among RHI categories. The first step of the study consisted in defining a set of indicators based on measurable values to allow systematic characterization of the main highway and roadside features that influence safety. Taking into account the results of previous research, four indicators were adopted to characterize roadside slope degrees, non-traversable obstacles offset from the roadway edge, safety barrier installation, and highway alignment. The next phase consisted in the collection of roadside, traffic and crash data from a sample of 1432 km of Spanish two-lane rural roads to support the analysis of the effect of roadside condition on safety. Roadside data was obtained from videologs and site visits, and referred to the indicators that had been defined in the initial phase of the study. During this phase, data of an additional sample
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Fig. 1. Negative binomial and linear models of ROR crash frequency as a function of ADT in the study sample.
of 2546 ROR injury crashes was obtained and later used in phase 4 to study the effect of RHI on ROR crash severity. In the third phase of the study, cluster analysis was applied to group the combinations of the four indicators into categories with homogeneous effects on ROR injury crashes frequency and severity. Traffic volume is a measure of exposure to crash risk and has been found to be the variable with the strongest correlation with crash frequency (Pardillo-Mayora and Llamas, 2003). It has also been shown that the relationship between crash frequency and traffic volumes is not linear, and therefore it is common practice in multivariate regression modeling to consider crash frequency instead of crash rate as prediction variable while exposure is introduced in the models as covariate. To assess the magnitude of deviation from linearity of the relationship in the study sample, a negative binomial model and a linear model of ROR crash frequency were fitted to the data using ADT as covariate. The results are shown in Fig. 1. The differences between the two models were found to be very small in the range of 1000–10,000 ADT that encompasses most of the sample sections. In consequence, for the application of cluster analysis the deviation from linearity of the relationship was not considered to be decisive and crash rates were retained as reference variables, as using crash frequencies would not have allowed to differentiate the effect of traffic volume from that of roadside condition in the classification process. Two crash rates were considered for the cluster analysis: • ROR injury crashes frequency rate (ROR-f rate): Number of runoff-road injury crashes/108 veh-km. • ROR severe and fatal casualty rate (ROR-s + f rate): Number of fatalities and severe injuries/108 veh-km. K-means cluster analysis based on the Euclidean distance resulting from the two crash rates was applied to group these conditions in categories with homogeneous effects on ROR crash frequency and severity. Cluster analysis implements this by seeking to identify a set of groups which both minimize within-group distances and maximize between-group distances. A 5-level Roadside Hazardousness Index (RHI) was defined based on the homogeneous categories resulting from the cluster analysis.
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As crash rates cannot be assumed to follow a normal distribution, the Mann–Whitney U test a non-parametric significance test was applied in phase 4 for assessing the significance of mean ROR-f rate and ROR-s + f rate among the five RHI categories. This test only requires the two samples to be independent, and the observations to be ordinal or continuous measurements. The null hypothesis was that the two samples were drawn from a single population, and therefore that their probability distributions were equal. The alternative hypothesis is that one sample is stochastically greater. Additionally, the effect of RHI on crash severity ratios of ROR injury crashes was explored to confirm the consistency of the rating scale.
3. Roadside configuration categorization 3.1. Side slope indicator Transverse slope steepness is one of the factors that previous research had found to bear more influence on ROR crash frequency and severity. Zegeer et al. (1988b) concluded that the possibilities of recovering vehicle control with side slope degrees over 1V:3H are severely compromised. At the same time the probability that the vehicle overturns increases considerably. The AASHTO Roadside Guide (2002) classifies transverse slopes of 1V:3H as non-recoverable, while 1V:4H or flatter are classified as recoverable, although a desirable slope degree of 1V:6H is recommended. The combination of embankment height and side slope rate also has an effect on the severity of the crashes. Spanish Roadside Design Guidelines (Dirección General de Carreteras, 1995) require that foreslopes over 1V:3H with an embankment height greater of 3 m be protected with a roadside barrier, while the AASHTO Roadside Guide recommends the installation of a roadside barrier for embankment heights starting at less than 1 m based on studies of the relative severity of encroachments on embankments versus impact with roadside barrier. Taking into account these references, it was decided to consider five categories for the side slope indicator (SSI): SSI = 1: Slope rate of 1V:6H or lower: Ideal conditions for vehicle control recovery. SSI = 2: Slope rate between 1V:4H and 1 V:6H: Good chances of vehicle control recovery. SSI = 3: Slope rate of 1V:3H, with an embankment height of less than 1 m: Reduced probability of vehicle control recovery. SSI = 4: Slope rate of 1V:3H, with an embankment height of more than 1 m: Low probability of vehicle control recovery. High rollover risk. SSI = 5: Slope rate of 1V:2H or higher: No possibility of vehicle control recovery. 3.2. Non-traversable obstacles offset indicator Previous research indicated that the number of fixed objects and their offset from the roadway edge are determinant of the number and the severity of ROR crashes. The clear zone may be defined as an area free of non-traversable obstacles, adjacent to the roadway. Its desired minimum width is dependent upon traffic volumes and speeds and on the roadside geometry. AASHTO Roadside Guide (2002) recommends clear zone widths ranging from 2.0 to 3.0 m for low volume low speed roads up to 11.5–14.0 m for high volume (over 6000 AADT) an high speed (110 km/h) roads.
Results of the RISER European research project (2005) based on information from France, the US, and the Netherlands concluded that the risk of contact with an obstacle drops dramatically after the first few meters and most impacts with roadside obstacles occur in the first 10 m. Most safety zones in Europe are specified to be between 6 and 10 m for design speeds around 100 km/h. Safety zones are smaller for lower speeds and for 80 km/h roads, the same countries use 4.5–7 m as a safety zone width. In Spain, roadside barrier installation is warranted for offset distances to non-traversable obstacles ranging from 4.5 m in twolane roads on level terrain to 16 m on the outside of a curve in dual-carriageway high speed facilities, and for side slope degrees of 1H:3V or more. Shoulder widths on main rural 2-lane highways are usually 1.5 m wide, and therefore obstacle offset is at least 2 m. Taking into account these references, it was decided to adopt 10 m and 3 m as maximum and minimum threshold for the obstacle offset indicator and to establish an intermediate category at roughly the minimum distance for which safety barrier installation is warranted in Spain. As result, the obstacle offset indicator (OOI) consists of four categories: OOI = 1: Roadside clear of obstacles within 10 m from the roadway edge. OOI = 2: Non-traversable obstacles within 5–10 m from the roadway edge. OOI = 3: Non-traversable obstacles within 3–5 m of the roadway edge. OOI = 4: Non-traversable obstacles within 3 m from the roadway edge. 3.3. Safety barrier indicator Guard rails and safety barriers are installed along the roadside to prevent errant vehicles from hitting rigid obstacles, overturning on steep side slopes or falling from a height, but also pose a risk as hitting them may cause damage to the vehicle and its occupants. Therefore they need to be considered in the characterization of roadside safety. In Spain, the European Road Restraint Systems standard EN 1317 (CEN, 1998) and the Spanish National Roadside Design Guidelines (Dirección General de Carreteras, 1995) are applied to design roadside safety devices. EN1317 contains 6 parts. Parts 1–4 describe how the crash protection of different types of RRS is determined during full scale crash testing. Part 5 describes the durability and documentation for conforming to the standard. Part 6 applies to pedestrian protection and is only relevant for the separation of pedestrians and traffic. The Spanish National Roadside Design Guidelines set containment level requirements in correspondence with the potential consequences of the crash that they are installed to prevent, which are classified into three categories: normal, severe and extremely severe. Roadside barrier installation is warranted for offset distances to non-traversable obstacles ranging from 4.5 m in two-lane roads on level terrain to 16 m on the outside of a curve in dualcarriageway high speed facilities, and for side slope degrees of 1H:3V or more. Safety barriers installation requirements define the length of need, lateral offset, barrier height and terminal conditions. Roadside systems that are not in compliance with these standards may not provide drivers with the adequate protection and therefore where included in a different category. Consequently, three categories were considered for the Safety barrier (SBI) indicator: SBI = 1: Absence of safety barrier. SBI = 2: Safety barrier installed in compliance with EN1317 and the Spanish Roadside Design Guidelines.
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SBI = 3: Safety barriers which do not comply with the requirements of the European Standard on Road Restraint Systems EN 1317 and the Spanish Roadside Design Guidelines.
Table 1 Combinations of roadside condition indicators included in the clusters. Cluster
ALI = 1: Tangent. ALI = 2: Curve. 4. Data Roadside data from a large sample of two-lane rural roads were collected from video logs and on-site measurements. The sample included 1432 km of Spanish National System highways in the regions of Extremadura (South west Spain) and Aragón (Northeast Spain) and 514 km of low volume regional roads in Extremadura. Spanish traffic police report crash location in 100 m increments. As there is some uncertainty associated with crash location, it was decided to base the study on 200 m long segments to ensure that each crash was associated in the analysis with the corresponding roadside configuration features. During the data collection process some variation of roadside condition was found along 200 m segments in some cases. The criterion that was followed in these cases was to classify the segments according to the worst condition for each indicator. The values of the four roadside configuration indicators for 200 m long segments were recorded in a database. Traffic volumes and ROR injury crash records for a six-year period (2000–2005) were obtained from the National Road Administration Safety database. Based on this information, ROR-f and ROR-s + f rates were computed for each 200 m segment. A second sample of 2546 ROR injury crashes recorded in twolane rural roads of the Spanish National Road System between 2000 and 2002 was obtained to assess the effect of RHI at the site of the crash on its severity. The available information for each crash included the consequences of the crash, and roadside configuration data for the segment where the crash occurred (cross-slope, nature and location of obstacles, the presence and type of safety barrier and highway alignment). This information was used to establish the RHI value for each crash. The following crash severity ratios were computed for each RHI category:
Roadside condition indicators
Sample size
Run-off-road crash rates
ALI
SSI
OOI
SBI
ROR-f
ROR-s + f
1
1/2 1/2
1 1
1 2
1 1
153 118
0.00 0.21
0.00 0.00
2
1 1 1 2 2 1/2
1/2 2 2 1/2 2 1/2/3/4/5
3 1 2 3 1/2 1/2/3/4
1 1 1 1 1 2
463 245 179 277 116 776
2.67 2.01 2.30 3.37 2.12 3.97
0.00 0.32 0.29 1.08 0.68 0.54
3
1 1/2 1 2
2 3 3 3
4 1 2/3 2/3
1/3 1/3 1/3 1/3
767 706 985 807
4.75 5.35 3.84 7.54
2.08 2.06 2.20 2.01
4
1/2 1/2 1 2
4/5 4 5 2
1 2/3 3 4
1/3 1/3 1/3 1/3
90 317 245 787
13.59 15.75 12.57 11.26
6.60 7.87 8.35 5.18
5
2
5
3
1/3
324
19.86
9.30
3.4. Alignment indicator Ray (1999) among others found that highway alignment influences vehicle encroachment frequencies and obstacle impact angles, which in turn are an important factor for the severity of ROR crashes. Previous research (Lee and Mannering, 2002; Pardillo-Mayora and Llamas, 2003; Pardillo-Mayora et al., 2006; ˜ 2009) had shown that crash frePardillo-Mayora and Jurado-Pina, quency and severity on curves are higher than in tangents. It had also been found that horizontal curvature, longitudinal grade, skid resistance and geometric design consistency determines the degree of hazardousness of a particular curve on two-lane rural roads. As the purpose of the study was to develop an easily applicable rating method the alignment indicator was restricted to differentiate curves and tangents. The effect of the more specific parameters can be incorporated to the analysis by means of multivariate regression modeling. Two categories were established for the alignment indicator (ALI):
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5. Results 5.1. Cluster analysis The combination of the possible values of the four roadside configuration indicators yields 120 possible road side conditions. The cluster analysis was performed in three successive phases the number of clusters K was specified at 7, 6 and 5 respectively. The results of the later study were adopted as the mean ROR-f and s + f rates showed significant differences for the five resulting clusters, which was not the case when six and seven clusters were considered. Table 1 presents the combinations of roadside condition indicators included in each cluster, the sample size (number of 200 m segments) and the mean ROR crash rates for each combination. Fig. 2 shows a graph of the final clusters for K = 5. The points in the graph correspond to the groupings of roadside condition indicators reflected in Table 1. 5.2. Significance assessment Mann–Whitney U tests were applied to assess the significance of the differences in ROR-f and s + f rates among the five roadside condition categories. Tables 2 and 3 summarize the results of the significance tests. The results of the tests indicate that there are significant differences in ROR injury crash frequency rates between the five roadside condition clusters. Additionally, highly significant ROR severe and
• Fatalities per 100 ROR injury crashes. • Severe injuries per 100 ROR injury crashes. The consistency of the crash severity ratios with the RHI rating scale was checked based on these results.
Fig. 2. Roadside condition clusters resulting from the K-means analysis for K = 5.
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Table 2 Mann–Whitney U test p-values for ROR injury crashes frequency rate differences. Cluster
1
2
3
4
5
1 2 3 4 5
– 0.0011 0.0000 0.0000 0.0000
0.0011 – 0.0021 0.0000 0.0000
0.0000 0.0021 – 0.0021 0.0000
0.0000 0.0000 0.0000 – 0.0003
0.0000 0.0000 0.0000 0.0003 –
Table 3 Mann-Whitney U test p-values for ROR crashes severe and fatal casualty rate. Cluster
1
2
3
4
5
1 2 3 4 5
– 0.1452 0.0182 0.0004 0.0000
0.1452 – 0.0003 0.0000 0.0000
0.0182 0.0003 – 0.0000 0.0000
0.0004 0.0000 0.0000 – 0.0012
0.0000 0.0000 0.0000 0.0012 –
Table 4 Roadside Hazardousness Index values in tangent sections with no safety barrier or with non-compliant safety barrier installation. Roadside slope/embankment height
<1V:6H 1V:4H–1:V:6H 1V:3H, h ≤ 1.0 m 1V:3H, h > 1.0 m 1V:2H
Non-traversable obstacles offset 0–3 m
3–5 m
5–10 m
>10 m
3 3 4 4 4
2 2 3 4 4
1 2 3 4 4
1 2 3 4 4
fatal casualty rate differences were found between all categories, except between 1 and 2, for which the difference is significant at the 85% level. 5.3. Roadside Hazardousness Index categories Based on the results of the cluster analysis, a five level Roadside Hazardousness Index (RHI) was finally adopted. All segments with a safety barrier installed in conformance with EN1317 and the Spanish Roadside Safety Guidelines were rated as RHI = 2. The analysis had shown that while all segments in which a safety barrier was installed in compliance with EN1317 and the Spanish Roadside Design Guidelines (SBI = 2) fell in cluster 2, those with a non-compliant safety barrier was installed (SBI = 3) fell in higher RHI levels. It was concluded that in these cases the barrier did not afford sufficient protection and did not modify the Hazardousness Index that corresponded to the rest of the roadside condition indicators. Tables 4 and 5 show the RHI values for all the combinations of roadside slope and non-traversable obstacles offset in tangents and curves in which no safety barrier exists or non-compliant safety barrier installation.
Table 6 Severity rates of run-off-road injury crashes by Road Hazardousness Index category. RHI ROR injury Fatalities Severe crashes injuries
Fatalities/100 ROR injury crashes
Severe injuries/100 ROR injury crashes
1 2 3 4 5
4.5 5.4 5.7 6.8 12.9
31.9 34.3 37.2 37.9 40.2
288 405 298 789 766
13 22 17 54 99
92 139 111 299 308
5.4. Effect of RHI on crash severity 2546 ROR injury crashes recorded in two-lane rural roads of the Spanish National Road System between 2000 and 2002 were analyzed to assess the effect of RHI at the site of the crash on its severity. Table 6 reflects the results of the analysis. Crash severity was found to increase in consistency with the value of RHI (Fig. 3). 6. Summary and discussion The calibration process of a roadside hazardousness scale for Spanish two-lane rural roads based on categorical indicators of the main factors that affect road departure frequency and consequences is presented in this article. Run-off-road crash records and roadside data from 1946 km of Spanish two-lane rural roads were analyzed to study the effect of roadside conditions on safety. Cluster analysis was applied to group the 120 possible combinations of the four indicators into categories including those roadside configurations with homogeneous effects on ROR crash frequency and severity. Significant differences in ROR injury crash frequency and severity rates were found between the five roadside condition clusters. Based on these results, a five level Roadside Hazardousness Index (RHI) was defined. Fatalities and serious injury rates on a sample of 2546 ROR injury crashes were found to increase with the value of RHI, confirming the consistency of the index. The resulting RHI can be used to normalize the collection of roadside safety related information, which can be referred to the four roadside configuration indicators that are used in the definition of the index. RHI may also be used as variable for inclusion of roadside condition information in multivariate crash prediction models. The following conclusions were drawn from the results of the research to provide a reference for roadside design and safety improvement planning:
Table 5 Roadside Hazardousness Index values in curves with no safety barrier or with noncompliant safety barrier installation. Roadside slope
<1V: 6H 1V:4H–1:V:6H 1V:3H, h ≤ 1.0 mm 1V:3H, h > 1.0 m 1V:2H
Non-traversable obstacles offset 0–3 m
3–5 m
5–10 m
>10 m
3 4 4 5 5
2 2 3 4 5
1 2 3 4 4
1 2 3 4 4
Fig. 3. Effect of Roadside Hazardousness Index category on fatality rates of run-offroad injury crashes.
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• In road safety improvement programs, safety barrier installation should be considered to protect those sections that present RHI values higher than 2, giving priority to the highest RHI values. • Whenever it is feasible technically and economically a cross-slope of 1:6 or less should be provided to attain a RHI of 1. • 1:3 may be considered as critical cross-slope value to warrant the installation of a safety barrier to protect embankment slopes. • Safety barrier installations that were not in compliance with EN1317 and the Spanish Roadside Design Guidelines (SBI = 3) were found not to modify the Hazardousness Index that corresponded to the rest of the roadside condition indicators. This result confirms the importance of designing and installing barriers according to the standards. Acknowledgements The research reported in this article was funded by the Centre for Public Works Studies and Experimentation (CEDEX) of the Spanish Ministry of Infrastructures (Ministerio de Fomento). References AASHTO, 2002. Roadside Design Guide. American Association of State Highway Officials, Washington, DC. CEN, 1998. EN 1317—Road Restraint Systems. European Committee for Standardization, Brussels. Council, F., Stewart, J., 1996. Severity indexes for roadside objects. Transportation Research Record: Journal of the Transportation Research Board 1528, 87–96. Dirección General de Carreteras, 1995. Recomendaciones sobre sistemas de contención de vehículos OC 321/95. Ministerio de Fomento, Madrid. Gattis, J., Varghese, J., Toothaker, L., 1993. Analysis of guardrail-end accidents in Oklahoma. Transportation Research Record: Journal of the Transportation Research Board 1419, 52–62. Good, M., Fox, J., Joubert, P., 1987. An in-depth study of accidents involving collisions with utility poles. Accident Analysis and Prevention 19 (5), 397–413. Kennedy, J., 1997. Effect of light poles on vehicle impacts with roadside barriers. Transportation Research Record: Journal of the Transportation Research Board 1599, 32–39.
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Lee, J., Mannering, F., 2002. Impact of roadside features on the frequency and severity of run-off-roadway accidents: an empirical analysis. Accident Analysis and Prevention 34, 149–161. Mak, K., 1995. Safety effects of roadway design decisions-roadside. Transportation Research Record: Journal of the Transportation Research Board 1512, 16–21. Sicking, K., Zimmerman, K., 1998. Roadside safety analysis program: a costeffectiveness analysis procedure. Journal of the Transportation Research Board 1647, 67–74. Miaou, S.P., 1997. Estimating vehicle roadside encroachment frequencies by using accident prediction models. Transportation Research Record: Journal of the Transportation Research Board 1599, 64–71. Michie, J., Bronstad, M., 1994. Highway guardrails: safety feature or roadside hazard? Transportation Research Record: Journal of the Transportation Research Board 1468, 1–9. Pardillo-Mayora, J.M., Llamas, R., 2003. Relevant variables for crash rate prediction in Spain’s two-lane rural roads. In: Proceedings of the TRB 82nd Annual Meeting Compendium of Papers Transportation Research Board, Washington, DC. Pardillo-Mayora, J.M., Bojorquez, R., Camarero, A., 2006. Refinement of accident prediction models for the Spanish national network. Transportation Research Record: Journal of the Transportation Research Board 1950, 65–72. ˜ R., 2009. An assessment of the skid resistance Pardillo-Mayora, J.M., Jurado-Pina, effect on traffic safety under wet-pavement conditions. Accident Analysis and Prevention 41, 881–886. Ray, M.H., 1999. Impact conditions in side-impact collisions with fixed roadside objects. Accident Analysis and Prevention 31, 21–30. RISER Consortium, 2005. Guidelines for roadside infrastructure on new and existing roads. Roadside infrastructure for safer European Roads, Deliverable 6. 5th Research Framework Programme “Growth”, European Commission, Brussels. Stonex, K., 1960. Roadside design for safety. In: Highway Research Board proceedings, vol. 33. Highway Research Board, Washington, DC. Viner, J., 1995. Rollovers on sideslopes and ditches. Accident Analysis and Prevention 27 (4), 483–491. Zegeer, C., Council, F., 1995. Safety relationships associated with cross-sectional roadway elements. Transportation Research Record: Journal of the Transportation Research Board 1512, 29–36. Wolford, D., Sicking, D., 1997. Guardrail need: embankment and culverts. Transportation Research Record: Journal of the Transportation Research Board 1599, 48–56. Zegeer, C., Reinfurt, D., Hummer, J., Herf, L., Hunter, W., 1998. Safety effects of crosssection design for two-lane roads. Transportation Research Record: Journal of the Transportation Research Board 1195. Zegeer, C., Reinfurt, D., Hummer, J., Stewart, R., Herf, L., 1998. Accident effects of sideslope and other roadside features on two-lane roads. Transportation Research Record: Journal of the Transportation Research Board 1195.
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Empirical calibration of a roadside hazardousness index for Spanish two-lane rural roads ˜ José M. Pardillo-Mayora ∗ , Carlos A. Domínguez-Lira, Rafael Jurado-Pina Department of Civil Engineering-Transport, Technical University of Madrid (UPM), C/Profesor Aranguren s/n, 28040 Madrid, Spain
a r t i c l e
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Article history: Received 12 March 2010 Received in revised form 11 June 2010 Accepted 15 June 2010 Keywords: Traffic safety Roadside Two-lane rural roads Run-off-road Hazardousness scale
a b s t r a c t Crash records and roadside data from Spanish two-lane rural roads were analyzed to study the effect of roadside configuration on safety. Four indicators were used to characterize the main roadside features that have an influence on the consequences of roadway departures: roadside slope, non-traversable obstacles distance from the roadway edge, safety barrier installation, and alignment. Based on the analysis of the effect of roadside configuration on the frequency and severity of run-off-road injury crashes, a categorical roadside hazardousness scale was defined. Cluster analysis was applied to group the combinations of the four indicators into categories with homogeneous effects on run-off-road injury crashes frequency and severity. As a result a 5-level Roadside Hazardousness Index (RHI) was defined. RHI can be used as reference to normalize the collection of roadside safety related information. The index can also be used as variable for inclusion of roadside condition information in multivariate crash prediction models. © 2010 Elsevier Ltd. All rights reserved.
1. Introduction and background Run-off-road (ROR) crashes occur when an errant vehicle encroaches into the roadside and either overturns or collides with a non-traversable obstacle or with the terrain. Roadside conditions are determinant of the consequences of a roadside encroachment. Formal research on roadside safety started 50 years ago. Stonex (1960) found that 35% of traffic fatalities occurred as a consequence of roadside encroachments and identified a number of factors that contribute to increase ROR crash severity, including the presence of non-traversable obstacles in the vicinity of the roadway, steep roadside slopes, deep ditches, and inadequate guardrail terminal configuration. Since then, numerous researchers have examined the effect on the frequency and severity of crashes of particular roadside features (Good et al., 1987; Gattis et al., 1993; Michie and Bronstad, 1994; Viner, 1995; Zegeer and Council, 1995; Kennedy, 1997; Wolford and Sicking, 1997; Ray, 1999). The results of these studies demonstrate that the presence of unprotected nontraversable obstacles on the roadside, their offset from the roadway edge, the degree of side slope and roadway geometrics are the main factors of ROR crash risk. Two approaches have been followed to model the relationship between roadway and roadside features and ROR crash risk. One is based on the adjustment of generalized linear regression mod-
∗ Corresponding author. Tel.: +34 913366652; fax: +34 913366654. E-mail address: [email protected] (J.M. Pardillo-Mayora). 0001-4575/$ – see front matter © 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.aap.2010.06.012
els to estimate ROR crash frequencies using exposure and relevant highway and roadside parameters as covariates. The frequency of crashes in a given highway section is treated as a random variable that takes discrete integer non-negative values distributed according to a Poisson distribution. A generalization of the Poisson form that allows the variance of the model to be over-dispersed results in the negative binomial model. Council and Stewart (1996) and Lee and Mannering (2002) have used Poisson and negative binomial regression models to develop ROR crash prediction models. A second approach, usually referred to as encroachment-based, uses a series of conditional probabilities of the sequence of events that lead to a ROR crash following the encroachment of an errant vehicle on the roadside (Mak, 1995; Mak et al., 1998). The main obstacle in the development of this type of models is the lack of encroachment frequency data. Miaou (1997) proposed a method to estimate vehicle roadside encroachment frequencies and the probability distribution of lateral extent of encroachments using existing crash-based prediction models. As Lee and Mannering (2002) point out, the lack of detailed roadside data, due primarily to the cost of collecting and maintaining such data, has been an obstacle to the development of detailed statistical models of the relationship between roadside features and ROR crash frequency and severity. To that respect, a systematic approach to characterizing roadside condition is a useful tool to facilitate roadside data collection and to support the ROR crash analysis. Until present, the main effort in this direction was conducted in the US by Zegeer et al. (1988a,b) in the framework of a major study for FHWA conducted with the objective
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of quantifying the benefits of lane widening, shoulder widening, and shoulder surfacing. Detailed traffic, crash, roadway, and roadside data were collected on 4951 miles of two-lane rural roads. Statistical testing was used along with log-linear modeling to determine the expected crash reduction related to various geometric improvements. A method to rate the safety of roadside conditions was established as part of the research. The resulting Roadside Hazard Rating (RHR) scale is a subjective measure of the hazard associated with the roadside environment. It was based on a review of previous roadside research results and on the results of a workshop involving thirteen highway and roadside safety professionals. Roadsides are evaluated on a scale from 1 to 7 (1 being the best) based on side slopes, clear zone, and distance to the nearest fixed object. The ratings are determined from a 7-point pictorial scale and the data collectors should use the rating value that most closely matches the roadside hazard level for the roadway section in question. The rating values indicate the crash likelihood and damage expected to be sustained by errant vehicles on a scale from one (low likelihood of an off-roadway collision or overturn) to seven (high likelihood of a crash resulting in a fatality or severe injury). RHR has proved to be a valuable tool to overcome the difficulties in characterizing roadside condition and is frequently used for this purpose in road safety studies. Nevertheless, to a certain extent its practical application is based on subjective judgment, and therefore subject to variations when applied by different observers. Additionally, the definition of RHR levels was based on the synthesis of existing knowledge and expert opinion, which is a valid methodology when applied adequately as it was the case, but lacks an explicit validation based on empirical evidence. The research reported in this article departed from RHR as initial reference and aimed at defining a new roadside condition classification procedure for Spanish two-lane rural roads based on objective indicators of roadside configuration to facilitate normalized collection of roadside data and its application to roadside safety analysis. It was also intended to assess the significance of the effect of the resulting roadside condition scale levels on ROR crash frequency and severity rates. 2. Method The research was conducted in four phases: 1. Roadside configuration categorization. 2. Data collection. 3. Grouping of the combinations of the four indicators into categories with homogeneous effects on run-off-road injury crashes frequency and severity and definition of a Roadside Hazardousness Index (RHI). 4. Assessment of the significance of the differences among RHI categories. The first step of the study consisted in defining a set of indicators based on measurable values to allow systematic characterization of the main highway and roadside features that influence safety. Taking into account the results of previous research, four indicators were adopted to characterize roadside slope degrees, non-traversable obstacles offset from the roadway edge, safety barrier installation, and highway alignment. The next phase consisted in the collection of roadside, traffic and crash data from a sample of 1432 km of Spanish two-lane rural roads to support the analysis of the effect of roadside condition on safety. Roadside data was obtained from videologs and site visits, and referred to the indicators that had been defined in the initial phase of the study. During this phase, data of an additional sample
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Fig. 1. Negative binomial and linear models of ROR crash frequency as a function of ADT in the study sample.
of 2546 ROR injury crashes was obtained and later used in phase 4 to study the effect of RHI on ROR crash severity. In the third phase of the study, cluster analysis was applied to group the combinations of the four indicators into categories with homogeneous effects on ROR injury crashes frequency and severity. Traffic volume is a measure of exposure to crash risk and has been found to be the variable with the strongest correlation with crash frequency (Pardillo-Mayora and Llamas, 2003). It has also been shown that the relationship between crash frequency and traffic volumes is not linear, and therefore it is common practice in multivariate regression modeling to consider crash frequency instead of crash rate as prediction variable while exposure is introduced in the models as covariate. To assess the magnitude of deviation from linearity of the relationship in the study sample, a negative binomial model and a linear model of ROR crash frequency were fitted to the data using ADT as covariate. The results are shown in Fig. 1. The differences between the two models were found to be very small in the range of 1000–10,000 ADT that encompasses most of the sample sections. In consequence, for the application of cluster analysis the deviation from linearity of the relationship was not considered to be decisive and crash rates were retained as reference variables, as using crash frequencies would not have allowed to differentiate the effect of traffic volume from that of roadside condition in the classification process. Two crash rates were considered for the cluster analysis: • ROR injury crashes frequency rate (ROR-f rate): Number of runoff-road injury crashes/108 veh-km. • ROR severe and fatal casualty rate (ROR-s + f rate): Number of fatalities and severe injuries/108 veh-km. K-means cluster analysis based on the Euclidean distance resulting from the two crash rates was applied to group these conditions in categories with homogeneous effects on ROR crash frequency and severity. Cluster analysis implements this by seeking to identify a set of groups which both minimize within-group distances and maximize between-group distances. A 5-level Roadside Hazardousness Index (RHI) was defined based on the homogeneous categories resulting from the cluster analysis.
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As crash rates cannot be assumed to follow a normal distribution, the Mann–Whitney U test a non-parametric significance test was applied in phase 4 for assessing the significance of mean ROR-f rate and ROR-s + f rate among the five RHI categories. This test only requires the two samples to be independent, and the observations to be ordinal or continuous measurements. The null hypothesis was that the two samples were drawn from a single population, and therefore that their probability distributions were equal. The alternative hypothesis is that one sample is stochastically greater. Additionally, the effect of RHI on crash severity ratios of ROR injury crashes was explored to confirm the consistency of the rating scale.
3. Roadside configuration categorization 3.1. Side slope indicator Transverse slope steepness is one of the factors that previous research had found to bear more influence on ROR crash frequency and severity. Zegeer et al. (1988b) concluded that the possibilities of recovering vehicle control with side slope degrees over 1V:3H are severely compromised. At the same time the probability that the vehicle overturns increases considerably. The AASHTO Roadside Guide (2002) classifies transverse slopes of 1V:3H as non-recoverable, while 1V:4H or flatter are classified as recoverable, although a desirable slope degree of 1V:6H is recommended. The combination of embankment height and side slope rate also has an effect on the severity of the crashes. Spanish Roadside Design Guidelines (Dirección General de Carreteras, 1995) require that foreslopes over 1V:3H with an embankment height greater of 3 m be protected with a roadside barrier, while the AASHTO Roadside Guide recommends the installation of a roadside barrier for embankment heights starting at less than 1 m based on studies of the relative severity of encroachments on embankments versus impact with roadside barrier. Taking into account these references, it was decided to consider five categories for the side slope indicator (SSI): SSI = 1: Slope rate of 1V:6H or lower: Ideal conditions for vehicle control recovery. SSI = 2: Slope rate between 1V:4H and 1 V:6H: Good chances of vehicle control recovery. SSI = 3: Slope rate of 1V:3H, with an embankment height of less than 1 m: Reduced probability of vehicle control recovery. SSI = 4: Slope rate of 1V:3H, with an embankment height of more than 1 m: Low probability of vehicle control recovery. High rollover risk. SSI = 5: Slope rate of 1V:2H or higher: No possibility of vehicle control recovery. 3.2. Non-traversable obstacles offset indicator Previous research indicated that the number of fixed objects and their offset from the roadway edge are determinant of the number and the severity of ROR crashes. The clear zone may be defined as an area free of non-traversable obstacles, adjacent to the roadway. Its desired minimum width is dependent upon traffic volumes and speeds and on the roadside geometry. AASHTO Roadside Guide (2002) recommends clear zone widths ranging from 2.0 to 3.0 m for low volume low speed roads up to 11.5–14.0 m for high volume (over 6000 AADT) an high speed (110 km/h) roads.
Results of the RISER European research project (2005) based on information from France, the US, and the Netherlands concluded that the risk of contact with an obstacle drops dramatically after the first few meters and most impacts with roadside obstacles occur in the first 10 m. Most safety zones in Europe are specified to be between 6 and 10 m for design speeds around 100 km/h. Safety zones are smaller for lower speeds and for 80 km/h roads, the same countries use 4.5–7 m as a safety zone width. In Spain, roadside barrier installation is warranted for offset distances to non-traversable obstacles ranging from 4.5 m in twolane roads on level terrain to 16 m on the outside of a curve in dual-carriageway high speed facilities, and for side slope degrees of 1H:3V or more. Shoulder widths on main rural 2-lane highways are usually 1.5 m wide, and therefore obstacle offset is at least 2 m. Taking into account these references, it was decided to adopt 10 m and 3 m as maximum and minimum threshold for the obstacle offset indicator and to establish an intermediate category at roughly the minimum distance for which safety barrier installation is warranted in Spain. As result, the obstacle offset indicator (OOI) consists of four categories: OOI = 1: Roadside clear of obstacles within 10 m from the roadway edge. OOI = 2: Non-traversable obstacles within 5–10 m from the roadway edge. OOI = 3: Non-traversable obstacles within 3–5 m of the roadway edge. OOI = 4: Non-traversable obstacles within 3 m from the roadway edge. 3.3. Safety barrier indicator Guard rails and safety barriers are installed along the roadside to prevent errant vehicles from hitting rigid obstacles, overturning on steep side slopes or falling from a height, but also pose a risk as hitting them may cause damage to the vehicle and its occupants. Therefore they need to be considered in the characterization of roadside safety. In Spain, the European Road Restraint Systems standard EN 1317 (CEN, 1998) and the Spanish National Roadside Design Guidelines (Dirección General de Carreteras, 1995) are applied to design roadside safety devices. EN1317 contains 6 parts. Parts 1–4 describe how the crash protection of different types of RRS is determined during full scale crash testing. Part 5 describes the durability and documentation for conforming to the standard. Part 6 applies to pedestrian protection and is only relevant for the separation of pedestrians and traffic. The Spanish National Roadside Design Guidelines set containment level requirements in correspondence with the potential consequences of the crash that they are installed to prevent, which are classified into three categories: normal, severe and extremely severe. Roadside barrier installation is warranted for offset distances to non-traversable obstacles ranging from 4.5 m in two-lane roads on level terrain to 16 m on the outside of a curve in dualcarriageway high speed facilities, and for side slope degrees of 1H:3V or more. Safety barriers installation requirements define the length of need, lateral offset, barrier height and terminal conditions. Roadside systems that are not in compliance with these standards may not provide drivers with the adequate protection and therefore where included in a different category. Consequently, three categories were considered for the Safety barrier (SBI) indicator: SBI = 1: Absence of safety barrier. SBI = 2: Safety barrier installed in compliance with EN1317 and the Spanish Roadside Design Guidelines.
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SBI = 3: Safety barriers which do not comply with the requirements of the European Standard on Road Restraint Systems EN 1317 and the Spanish Roadside Design Guidelines.
Table 1 Combinations of roadside condition indicators included in the clusters. Cluster
ALI = 1: Tangent. ALI = 2: Curve. 4. Data Roadside data from a large sample of two-lane rural roads were collected from video logs and on-site measurements. The sample included 1432 km of Spanish National System highways in the regions of Extremadura (South west Spain) and Aragón (Northeast Spain) and 514 km of low volume regional roads in Extremadura. Spanish traffic police report crash location in 100 m increments. As there is some uncertainty associated with crash location, it was decided to base the study on 200 m long segments to ensure that each crash was associated in the analysis with the corresponding roadside configuration features. During the data collection process some variation of roadside condition was found along 200 m segments in some cases. The criterion that was followed in these cases was to classify the segments according to the worst condition for each indicator. The values of the four roadside configuration indicators for 200 m long segments were recorded in a database. Traffic volumes and ROR injury crash records for a six-year period (2000–2005) were obtained from the National Road Administration Safety database. Based on this information, ROR-f and ROR-s + f rates were computed for each 200 m segment. A second sample of 2546 ROR injury crashes recorded in twolane rural roads of the Spanish National Road System between 2000 and 2002 was obtained to assess the effect of RHI at the site of the crash on its severity. The available information for each crash included the consequences of the crash, and roadside configuration data for the segment where the crash occurred (cross-slope, nature and location of obstacles, the presence and type of safety barrier and highway alignment). This information was used to establish the RHI value for each crash. The following crash severity ratios were computed for each RHI category:
Roadside condition indicators
Sample size
Run-off-road crash rates
ALI
SSI
OOI
SBI
ROR-f
ROR-s + f
1
1/2 1/2
1 1
1 2
1 1
153 118
0.00 0.21
0.00 0.00
2
1 1 1 2 2 1/2
1/2 2 2 1/2 2 1/2/3/4/5
3 1 2 3 1/2 1/2/3/4
1 1 1 1 1 2
463 245 179 277 116 776
2.67 2.01 2.30 3.37 2.12 3.97
0.00 0.32 0.29 1.08 0.68 0.54
3
1 1/2 1 2
2 3 3 3
4 1 2/3 2/3
1/3 1/3 1/3 1/3
767 706 985 807
4.75 5.35 3.84 7.54
2.08 2.06 2.20 2.01
4
1/2 1/2 1 2
4/5 4 5 2
1 2/3 3 4
1/3 1/3 1/3 1/3
90 317 245 787
13.59 15.75 12.57 11.26
6.60 7.87 8.35 5.18
5
2
5
3
1/3
324
19.86
9.30
3.4. Alignment indicator Ray (1999) among others found that highway alignment influences vehicle encroachment frequencies and obstacle impact angles, which in turn are an important factor for the severity of ROR crashes. Previous research (Lee and Mannering, 2002; Pardillo-Mayora and Llamas, 2003; Pardillo-Mayora et al., 2006; ˜ 2009) had shown that crash frePardillo-Mayora and Jurado-Pina, quency and severity on curves are higher than in tangents. It had also been found that horizontal curvature, longitudinal grade, skid resistance and geometric design consistency determines the degree of hazardousness of a particular curve on two-lane rural roads. As the purpose of the study was to develop an easily applicable rating method the alignment indicator was restricted to differentiate curves and tangents. The effect of the more specific parameters can be incorporated to the analysis by means of multivariate regression modeling. Two categories were established for the alignment indicator (ALI):
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5. Results 5.1. Cluster analysis The combination of the possible values of the four roadside configuration indicators yields 120 possible road side conditions. The cluster analysis was performed in three successive phases the number of clusters K was specified at 7, 6 and 5 respectively. The results of the later study were adopted as the mean ROR-f and s + f rates showed significant differences for the five resulting clusters, which was not the case when six and seven clusters were considered. Table 1 presents the combinations of roadside condition indicators included in each cluster, the sample size (number of 200 m segments) and the mean ROR crash rates for each combination. Fig. 2 shows a graph of the final clusters for K = 5. The points in the graph correspond to the groupings of roadside condition indicators reflected in Table 1. 5.2. Significance assessment Mann–Whitney U tests were applied to assess the significance of the differences in ROR-f and s + f rates among the five roadside condition categories. Tables 2 and 3 summarize the results of the significance tests. The results of the tests indicate that there are significant differences in ROR injury crash frequency rates between the five roadside condition clusters. Additionally, highly significant ROR severe and
• Fatalities per 100 ROR injury crashes. • Severe injuries per 100 ROR injury crashes. The consistency of the crash severity ratios with the RHI rating scale was checked based on these results.
Fig. 2. Roadside condition clusters resulting from the K-means analysis for K = 5.
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Table 2 Mann–Whitney U test p-values for ROR injury crashes frequency rate differences. Cluster
1
2
3
4
5
1 2 3 4 5
– 0.0011 0.0000 0.0000 0.0000
0.0011 – 0.0021 0.0000 0.0000
0.0000 0.0021 – 0.0021 0.0000
0.0000 0.0000 0.0000 – 0.0003
0.0000 0.0000 0.0000 0.0003 –
Table 3 Mann-Whitney U test p-values for ROR crashes severe and fatal casualty rate. Cluster
1
2
3
4
5
1 2 3 4 5
– 0.1452 0.0182 0.0004 0.0000
0.1452 – 0.0003 0.0000 0.0000
0.0182 0.0003 – 0.0000 0.0000
0.0004 0.0000 0.0000 – 0.0012
0.0000 0.0000 0.0000 0.0012 –
Table 4 Roadside Hazardousness Index values in tangent sections with no safety barrier or with non-compliant safety barrier installation. Roadside slope/embankment height
<1V:6H 1V:4H–1:V:6H 1V:3H, h ≤ 1.0 m 1V:3H, h > 1.0 m 1V:2H
Non-traversable obstacles offset 0–3 m
3–5 m
5–10 m
>10 m
3 3 4 4 4
2 2 3 4 4
1 2 3 4 4
1 2 3 4 4
fatal casualty rate differences were found between all categories, except between 1 and 2, for which the difference is significant at the 85% level. 5.3. Roadside Hazardousness Index categories Based on the results of the cluster analysis, a five level Roadside Hazardousness Index (RHI) was finally adopted. All segments with a safety barrier installed in conformance with EN1317 and the Spanish Roadside Safety Guidelines were rated as RHI = 2. The analysis had shown that while all segments in which a safety barrier was installed in compliance with EN1317 and the Spanish Roadside Design Guidelines (SBI = 2) fell in cluster 2, those with a non-compliant safety barrier was installed (SBI = 3) fell in higher RHI levels. It was concluded that in these cases the barrier did not afford sufficient protection and did not modify the Hazardousness Index that corresponded to the rest of the roadside condition indicators. Tables 4 and 5 show the RHI values for all the combinations of roadside slope and non-traversable obstacles offset in tangents and curves in which no safety barrier exists or non-compliant safety barrier installation.
Table 6 Severity rates of run-off-road injury crashes by Road Hazardousness Index category. RHI ROR injury Fatalities Severe crashes injuries
Fatalities/100 ROR injury crashes
Severe injuries/100 ROR injury crashes
1 2 3 4 5
4.5 5.4 5.7 6.8 12.9
31.9 34.3 37.2 37.9 40.2
288 405 298 789 766
13 22 17 54 99
92 139 111 299 308
5.4. Effect of RHI on crash severity 2546 ROR injury crashes recorded in two-lane rural roads of the Spanish National Road System between 2000 and 2002 were analyzed to assess the effect of RHI at the site of the crash on its severity. Table 6 reflects the results of the analysis. Crash severity was found to increase in consistency with the value of RHI (Fig. 3). 6. Summary and discussion The calibration process of a roadside hazardousness scale for Spanish two-lane rural roads based on categorical indicators of the main factors that affect road departure frequency and consequences is presented in this article. Run-off-road crash records and roadside data from 1946 km of Spanish two-lane rural roads were analyzed to study the effect of roadside conditions on safety. Cluster analysis was applied to group the 120 possible combinations of the four indicators into categories including those roadside configurations with homogeneous effects on ROR crash frequency and severity. Significant differences in ROR injury crash frequency and severity rates were found between the five roadside condition clusters. Based on these results, a five level Roadside Hazardousness Index (RHI) was defined. Fatalities and serious injury rates on a sample of 2546 ROR injury crashes were found to increase with the value of RHI, confirming the consistency of the index. The resulting RHI can be used to normalize the collection of roadside safety related information, which can be referred to the four roadside configuration indicators that are used in the definition of the index. RHI may also be used as variable for inclusion of roadside condition information in multivariate crash prediction models. The following conclusions were drawn from the results of the research to provide a reference for roadside design and safety improvement planning:
Table 5 Roadside Hazardousness Index values in curves with no safety barrier or with noncompliant safety barrier installation. Roadside slope
<1V: 6H 1V:4H–1:V:6H 1V:3H, h ≤ 1.0 mm 1V:3H, h > 1.0 m 1V:2H
Non-traversable obstacles offset 0–3 m
3–5 m
5–10 m
>10 m
3 4 4 5 5
2 2 3 4 5
1 2 3 4 4
1 2 3 4 4
Fig. 3. Effect of Roadside Hazardousness Index category on fatality rates of run-offroad injury crashes.
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• In road safety improvement programs, safety barrier installation should be considered to protect those sections that present RHI values higher than 2, giving priority to the highest RHI values. • Whenever it is feasible technically and economically a cross-slope of 1:6 or less should be provided to attain a RHI of 1. • 1:3 may be considered as critical cross-slope value to warrant the installation of a safety barrier to protect embankment slopes. • Safety barrier installations that were not in compliance with EN1317 and the Spanish Roadside Design Guidelines (SBI = 3) were found not to modify the Hazardousness Index that corresponded to the rest of the roadside condition indicators. This result confirms the importance of designing and installing barriers according to the standards. Acknowledgements The research reported in this article was funded by the Centre for Public Works Studies and Experimentation (CEDEX) of the Spanish Ministry of Infrastructures (Ministerio de Fomento). References AASHTO, 2002. Roadside Design Guide. American Association of State Highway Officials, Washington, DC. CEN, 1998. EN 1317—Road Restraint Systems. European Committee for Standardization, Brussels. Council, F., Stewart, J., 1996. Severity indexes for roadside objects. Transportation Research Record: Journal of the Transportation Research Board 1528, 87–96. Dirección General de Carreteras, 1995. Recomendaciones sobre sistemas de contención de vehículos OC 321/95. Ministerio de Fomento, Madrid. Gattis, J., Varghese, J., Toothaker, L., 1993. Analysis of guardrail-end accidents in Oklahoma. Transportation Research Record: Journal of the Transportation Research Board 1419, 52–62. Good, M., Fox, J., Joubert, P., 1987. An in-depth study of accidents involving collisions with utility poles. Accident Analysis and Prevention 19 (5), 397–413. Kennedy, J., 1997. Effect of light poles on vehicle impacts with roadside barriers. Transportation Research Record: Journal of the Transportation Research Board 1599, 32–39.
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