Rural Road Departure Crashes: Why is Injury Severity Correlated with Lane Markings?

Transcription

1 Rural Road Departure Crashes: Why is Injury Severity Correlated with Lane Markings? Presented at The Transportation Research Board 91 st Annual Meeting January 2012 By Kristofer D. Kusano Hampton C. Gabler Virginia Tech Paper No Contact Information: Kristofer D. Kusano Graduate Research Engineer, Mechanical Engineering Virginia Tech 440 ICTAS Building Stanger St (MC 0194) Blacksburg, VA Hampton C. Gabler (Corresponding Author) Professor, Mechanical and Biomedical Engineering Virginia Tech 445 ICTAS Building Stanger St (MC 0194) Blacksburg, VA Phone: (540) Fax: (540 ) gabler@vt.edu

2 ABSTRACT This study aimed to determine if injury outcome is related to the presence of lane lines in road departure crashes on rural 2-lane roads. Cases were extracted from a nationally representative sample of crashes, where supplemental crash reconstructions were performed as part of NCHRP Project The data set consisted of 851 road departure collisions that corresponded to 271,603 weighted collisions. The majority of cases (55%) occurred on 2-lane roads with undivided, 2-way traffic. Of all paved 2-lane, undivided roads with 2-way traffic, only 19% of collisions did not have lane markings yet these collisions accounted for a disproportionate 48% of seriously to fatally injured drivers. A logistic regression found that the presence of lane marking at the side of the first lane departure decreased the odds of serious injury for the driver, adjusted for belt use and departure velocity. The finding that the presence of lane markings was correlated with injury severity in road departure crashes was unexpected. Roadside factors, such as maximum sideslope and speed reduction from departure to impact, did not appear to explain the difference in injury outcome. Only 42% of drivers, however, were wearing their seat belt in crashes on unmarked roads compared to 67% of drivers on marked roads. In this sample lane marking presence was correlated to seat belt use. This result suggests that the primary explanation for higher injury levels on unmarked roads was lower seat belt use, not the absence of lane markings.

3 1 Rural Road Departure Crashes: Why is Injury Severity Correlated with Lane Markings? Kristofer D. Kusano Hampton C. Gabler Virginia Tech INTRODUCTION Road departure crashes are one of the most frequent and costly collision types in the United States. Estimates are that these road departure collisions are among the top five most frequent crash types and result in the most economic damage of any type of collision (1). Road departure collisions are more frequent on non-highways in rural areas, with up to 60% of crashes occurring on 2-lane roads (2). The presence of center and edge lines on paved roads has been found to reduce crash frequency. Miller found that installing markings on roads reduces crash frequency by 21% on average and 8% on rural 2-lane roads (3). More recent studies have found that painted longitudinal markings reduce the crash frequency up to 26% on rural 2-lane roads (4). The current study investigates if injury severity may also be correlated with the presence of center and edge lines. FIGURE 1 shows scene photographs from the approach to two similar road departure collisions. The first case, NASS/CDS case , occurred on a 2-lane road with both a center line and edge lines. The vehicle departed the right side of the road and struck a utility pole. The second case, NASS/CDS case , also involved a right side departure. However, in this collision there was no edge line. The vehicle departed the roadway after failing to negotiate a curve and steered back onto the roadway where the vehicle tripped and rolled over. In the first case, with both center and edge lines, the driver was not seriously injured. In the second case, without an edge line, the driver was seriously injured (MAIS3+). Impacts Utility Pole Returns to road, trips and rolls Departs Travel Travel Departs NASS/CDS NASS/CDS FIGURE 1 Example Cases from Road Departure Crashes with Edge Marking at Departure (left) and without Edge Marking at Departure (right). OBJECTIVE The objective of this study was to determine if the presence of edge and/or center lines is correlated with the severity of driver injury outcome in road departure collisions on rural roads. METHODOLOGY This study examines a set of real-world collisions extracted from the National Automotive Sampling System / Crashworthiness Data System (NASS/CDS). NASS/CDS is a nationally representative sample

4 2 of in-depth crash investigations performed in the U.S. Crash investigation teams located in urban, suburban, and rural locations throughout the country collect information about collisions. In order to be selected for investigations, crashes must involve at least one passenger vehicle and have at least one vehicle towed from the scene due to damage. Investigations consist of photographing and diagraming the crash scenes, conducting interviews with those involved, retrieving medical and police records, and documenting damage to vehicles. National Cooperative Highway Research Program (NCHRP) Project titled Identification of Vehicular Impact Conditions Associated with Serious Ran-Off-Road Crashes performed supplemental investigation and reconstructions on road departure collisions from NASS/CDS (5). In addition to the information that was originally collected as part of the NASS/CDS investigation, NCHRP Project performed detailed reconstructions on 890 cases from NASS/CDS years and 2004 that yielded parameters that describe road departure collisions, such as the speed and angle of the vehicle at the point of road departure. In order to be included in the NCHRP study, the crash had to involve a single passenger vehicle on a road with a speed limit above 45 mph (72.4 kph). In order to examine the effect of center and edge lines on crash outcome, the current study extends the NCHRP dataset to add detailed information on the availability and style of markings, not available in either NASS/CDS or the NCHRP databases. Data Composition and Reduction Scene photographs, available in most cases, were utilized to assess the availability of road markings. Two independent reviewers analyzed all 890 cases in the NCHRP database. For each case, the reviewers assessed the lane markings based upon the scene diagram prepared by the investigator, the case summary text, and all of the photographs available for each case. The reviewers first identified the original travel lane of the vehicle, before it left its lane. The reviewers then determined the side of the first lane departure (right or left), if there was a road marking delineating the edge of the lane or road, and the style of road marking (e.g. dashed, double solid). The availability of lane markings was determined at the point the vehicle first departed its lane. If the initial lane departure occurred in an intersection or on an entrance way which was not marked, this was considered a departure without lane markings. The same information was recorded pertaining to the lane or road boundary opposite the side of the first departure. The result detailed the road marking style on both sides of the vehicle s travel lane prior to the departure. Examining the lane markings at the point of the first lane departure, as opposed to where the vehicle ultimately left the road, allows for assessment of the effect of the lane markings prior to the start of the event. Often these points will occur simultaneously; however, in some cases the vehicle will depart the lane then continue on before departing the road. In order to make the sample representative of all collisions that occur in the U.S., NASS/CDS assigned a weighting factor to each case that corresponded to the number of similar collisions that occurred during the sampling period. In this paper, all analyses used the sampling weight for cases in order to account for the sampling design of NASS/CDS. NASS/CDS uses a complex, stratified survey design, which is not a simple random sample. For example, crashes that involved newer vehicles and hospitalization of occupants were sampled more often than non-injury cases. As a result, case weights can vary from 1 to above 50,000. Because the NCHRP database is a small subset of NASS/CDS cases (890 cases of 27,000 total cases investigated during the study time frame), cases with large weights can have large influence on statistical analysis. To decrease the bias introduced by extreme weighting factors, cases with weighting factors greater than 5,000 were excluded from the analysis (6). From the 890 cases, 14 cases had weights above 5,000 and one case had a case weight of zero, leaving 875 cases. These 14 cases omitted (1.6% of the 890 total cases) corresponded to a disproportionately high 40% of the total case weights. In the remaining 875 cases, the lane marking characteristics could not be determined in 24 cases, leaving 851 cases for analysis. Some cases did not have any scene photographs available. In other cases, the road was obscured by snow in the scene photographs or the scene photographs showed an active construction site, which made the road markings at the time of the collision unclear. The remaining 851

5 3 cases had a total weight of 271,603. The 23 cases with unknown markings had summed weights of 7,696, which accounted for only 2% of the total weights. Discarding the 23 cases without discernable lane markings likely did not introduce bias into the analysis. In most crashes, the determination of lane markings and departure sides was straightforward. However, reviewers interpreted some cases differently, which led to different results from each reviewer. Agreement between the two reviewers on individual assessments (e.g. which side of the lane did the vehicle depart? What was the lane color on the side of the first departure?) was between 89% and 97%. An analogous situation often arises in independent review of medical studies. For instance, two physicians will be asked to diagnose an ailment from x-ray film. A measure for assessing observer variability often used in medical studies is the kappa statistic (7). The kappa statistic determines what proportion of agreement between observers is above chance alone. A kappa statistic of 0 represents only chance agreement between two reviewers and a statistic of 1 represents perfect agreement. For assessments in the current study, the kappa statistic ranged from 0.77 to In medical studies, kappa statistics in this range would be considered substantial to almost perfect agreement (8). In crashes where there was no agreement between reviewers on one or more items (215 out of 890 cases), a third member of research team reviewed the case to determine the departure and lane marking characteristics. Data Analysis All analysis utilized the national weighting factors, or weighted frequency. The software package SAS (version 9.2) was used for all statistical analysis. When confidence intervals are shown, they were computed using survey procedures in SAS. Because NASS/CDS uses a complex survey design with clustering and stratification, standard methods of computing standard error will underestimate variance. The survey procedures use appropriate methods to estimate standard errors. Injury in this study was determined using the driver s Maximum Abbreviated Injury Score (MAIS). The Abbreviated Injury Score is a 0 to 6 scale that measures an injury s threat to life with a 0 corresponding to no injury and 6 corresponding to an unsurvivable injury. The MAIS is simply the maximum AIS an occupant sustains. A MAIS of 3 to 6 (MAIS3+) corresponds to a seriously to fatally injured driver, which is the definition of injury used in this study. RESULTS TABLE 1 summarizes the road and driver characteristic from the 851 NCHRP cases with lane marking information. Of these collisions, 70% of the collisions occurred in areas classified as rural land use. The majority of the roads were classified as having a low Average Daily Travel (ADT) volume (51%), as determined from scene photographs and road type by investigators. State and county roads accounted for over half of the collisions (53%), with 20% occurring on interstates and 17% occurring on U.S. routes. The majority of roads were paved (concrete, 5% or asphalt, 85%), with few gravel (6%) and dirt (3%) roads. Drivers were wearing their seat belts in 69% of crashes, and 15% of drivers were seriously to fatally injured (MAIS3+).

6 4 TABLE 1 Summary of Road and Driver Characteristics from NCHRP Cases (n=851) Weighted Category Values N Per cent Frequency Land Use Rural , % Urban/Suburban , % Missing/Unknown 34 14, % Total , % ADT Very Low 27 5,136 2% Low ,831 51% On/Off Ramp (Low) % Medium ,156 20% High ,296 25% Very High 25 5,191 2% Total , % Road Classification County Road ,795 29% State Route ,045 24% Interstate ,898 20% U.S. Route ,341 17% City Street 41 10,478 4% Other 8 2,283 1% Missing/Unknown 34 14,763 5% Total , % Surface Type Asphalt , % Slag/Gravel/Stone 23 16, % Concrete 44 12, % Dirt 31 7, % Other 10 4, % Total , % Driver Belt Use Belted , % Unbelted , % Missing/Unknown % Total , % Injured Driver (MAIS3+) Seriously Injured ,574 15% Not Seriously Injured ,007 85% Missing/Unknown % Total , % Distribution of Lane Markings FIGURE 2 shows the distribution of lane markings in road departure crashes in NCHRP Vehicles involved in collisions were traveling on different types of roads, i.e. number of lanes and traffic flow. As a result, there were a variety of marking styles on the left and right side of the lane from which the vehicle first departed. However, 11% of crashes occurred on roads with no markings on either side of the lane. Also, 24% of crashes had no marking on one or both boundaries of the initial travel lane. The Other lane marking category included non-standard lane lines, such as in merging areas.

7 5 Solid Right Side Marking Single None Other Total Dashed Solid? 4% 12% 2% 0.1% 18% Single Dashed? 25% 2% 6% 0.1% 34% Left Side Marking Double Solid Dashed Solid 20% 0.1% 4% 0% 24%?? None Other 10% 0.02% 1% 0% 12% 0.6% 0.1% 11% 0% 12%?????? 0.6% 0.01% 0.1% 0% 0.7% Total 63% 13% 24% 0.3% 100% * All cells and row/column percentages are rounded FIGURE 2 Distribution of Lane Marking Style in Road Departure Crashes from NCHRP (n=851). TABLE 2 shows the availability of lane markings at the side of first departure by road configuration. There were a few cases that occurred on roads with 5 or more lanes. However, a majority of crashes occurring on roads with 5 or more lanes in the current data set had no lane marking. These crashes occurred in intersections or on entranceways to the roadway, which were unmarked on the side of the first departure. Of those crashes that did not have a lane marking at the point of first lane departure, 89% (42,193 of 47,197) occurred on 2-lane roads with 2-way traffic.

8 6 TABLE 2 Availability of Lane Marking at Point of First Departure by Road Configuration (n=851) 2 lanes 2 lanes 2 lanes 1 One Same 2-Way Lane Markings at Lane Way Lanes Lanes Lanes Direction Traffic First Departure Traffic Total Yes 17,854 52,692 4, ,762 26,496 11,744 1, ,565 No , ,707 47,197 Curb (no marking) ,787 2,842 Total 17,879 53,417 4, ,015 27,086 12,590 6, ,603 In the sample of 851 cases, 455 occurred on 2-lane roads with undivided, 2-way traffic, corresponding to 149,015 weighted collisions. This category of roads contains low to medium volume roads in rural and suburban environments. The majority of crashes on 2-lane roads with 2-way traffic (82%) occurred on asphalt with the remaining occurring on either gravel, dirt, or other surfaces. No dirt or gravel roads in the sample had center or edge markings, and thus examining the effect of lane markings on these non-asphalt roads would not yield meaningful results. Lane Markings and Injury on Paved 2-Lane, 2-Way Roads There were 393 crashes that occurred on 2-lane, undivided roads that were paved, which corresponds to 122,001 weighted collisions. TABLE 3 shows the frequency of collisions by first departure side and availability of lane markings at the first departure. More vehicles departed the right side of the lane first (59%) compared to the left (41%). For departures from the left side of the travel lane, the majority of lanes were marked (92%), i.e. had a center line. On the other hand, fewer of the right side departures had an edge line (73%) compared to left side departures. One case had a curb with no lane marking at the side of first departure. This case will be included with the no marking departures for the remainder of the analysis. TABLE 3 Frequency of Collisions by First Departure Side and Availability of Marking at First Departure (n=393) Weighted Frequency Per Cent Marking at 1st Row Left Right Total Departure % Left Right Yes 46,632 51,989 98,621 81% 92% 73% No 3,821 19,499 23,320 19% 8% 27% Curb (no marking) % 0% 0.1% Total 50,453 71, , % 100% 100% Column % 41% 59% TABLE 4 shows the number of seriously to fatally injured drivers (MAIS3+) by the presence of lane markings for left and right departures. FIGURE 3 shows graphically the same information than is in TABLE 4. Although only 19% of crashes lacked lane markings at the point of first departure, these crashes accounted for 48% of the seriously to fatally injured drivers. The same trend is observed in both left and right departures individually. Only one case, with weighted frequency of 23, had missing injury information for the driver.

9 7 TABLE 4 Number of Seriously to Fatally Injured Drivers (MAIS3+) by Presence of Lane Markings at the Point of First Departure for 2-Lane, 2-Way Paved Roads (n=392) Left Departure Right Departure Total Injury Markings No Markings Markings 1 No Markings Markings 1 No Markings MAIS3+ 4,716 1,576 6,624 8,817 11,341 10,393 MAIS < 3 41,916 2,245 45,342 10,742 87,257 12,987 Total 46,632 3,821 51,966 19,559 98,598 23,380 1Weighted frequency with missing injury information = 23(n=1) Crashes 92% 8% 81% 19% MAIS3+ 75% 25% 52% 48% Left All Right FIGURE 3 Proportion of Crashes and Seriously to Fatally Injured Drivers (MAIS3+) for Left and Right Side Departures on 2-lane, 2-Way Paved Roads (n=393). To investigate the correlation between injury severity and the presence of road markings at the point of first departure, a logistic regression model was fit to the injury outcome of the driver (MAIS3+) based on the covariates of lane marking presence, belt use, and departure velocity. Belt use has been strongly correlated to injury outcome (9, 10). Lack of belt use may also be indicative of drivers who are prone to risk-taking behavior (11). Departure velocity, as reconstructed by NCHRP Project 17-22, is the vehicle velocity at the point of road departure and is a surrogate for travel speed. TABLE 5 shows the results of the logistic regression. All coefficient estimates and odds ratio estimates were significant to the 95% confidence level. Of the cases, 19 (weight of 5,028) were excluded from the logistic regression analysis due to missing values for one of the variables. This regression suggests that for collisions with similar departure speed and driver belt use, the odds of the collision resulting in a seriously injured driver were approximately 4.9 times greater when there were no lane marking on the side of the first departure. TABLE 5 Logistic Regression Coefficient Estimates and Odds Ratios for Model for Probability of Serious Injury (MAIS3+) Coefficient Estimate Odds Ratio 95% Confidence Limits Intercept Lane Marking (No vs. Yes) Belt Use (Belted vs. Unbelted) Departure Velocity (kph) indicates not applicable

10 8 Influence of Roadside and Driver Characteristics on Injury Outcome The fact that injury severity was correlated with the presence of lane markings was an unexpected finding. Our hypothesis, explored in this section, was that lane markings might be a surrogate for other vehicle or road characteristics that affect injury outcome. Our approach was to use a Rao-Scott chi-squared test to check for correlation between the presence of road markings at the point of departure and categorical variables that described the roadside. A chi-squared test for independence has the null hypothesis that two variables have no relationship to one another. The Rao-Scott chi-squared test is similar to the traditional Pearson s chi-squared test but utilizes a design adjusted F statistic that corrects for the complex survey design of NASS/CDS. Continuous variables were separated into bins to facilitate a chi-squared test. TABLE 6 shows the results for Rao-Scott chi-squared tests for independence between crashes that occurred on roads with and without road markings. P-values less than 0.05 reject the null hypothesis and indicate that the variable of interest is different between crashes with and without road markings. The tests did not include cases with missing values. Pre-crash departure scenarios (i.e. no maneuver drift off road, control loss, and avoidance maneuver) were similar between groups. The majority of collisions did not occur close to an intersection (91% and 93% of unmarked and marked crashes, respectively). The frequency of lighting conditions, rollovers, road alignment, and angle of departure at the point of departure (POD) were also similar between groups. The difference in the vehicle s speed between the most severe impact and the point of departure was also similar between groups. Finally, the maximum sideslope of the roadside that the vehicle encountered prior to the first harmful event was similar between groups. TABLE 6 Results for Rao-Scott Chi-Squared Test for Independence for Crashes Occurring on Roads with and without Road Markings Variable Rao-Scott Number Chiof n Frequency Squared Levels Adjusted F p Different Object Struck , <.0001 Y Pre-crash Departure Scenario , Intersection Related , Lighting Conditions , Rollover , Speed Limit , <.0001 Y Road Alignment , Velocity at POD , <.0001 Y Velocity Heading at POD , Lateral Departure Distance (1st Event) , Y Speed Reduction from POD to Impact , Steepest Sideslope , Average Lane Width , Y Five variables were found to be statistically different between crashes with and without markings. TABLE 7 summarizes the mean and median values for variables these variables. The most harmful object struck during the collision (not shown in table) was different between marking groups. Crashes with road markings encountered more culverts than non-marked crashes. Speed limits were lower for

11 9 unmarked roads (range of 45 mph to 55 mph) compared to marked roads (range of 45 mph to 75 mph). However, the velocity of the vehicle at road departure was higher for unmarked roads (median 74 kph) than marked roads (median 64 kph). Both the mean and median lane width was larger for unmarked roads. The median lane width for unmarked roads was smaller than the mean, suggesting that the distribution of lane widths is positively skewed (tail to the right) for unmarked roads, with a few wide roads. The maximum lateral departure distance from the roadway before the 1 st harmful event was larger for marked roads than unmarked roads, suggesting that marked roads may have larger clear-zones compared to crashes on unmarked roads. However, the difference in speed of the vehicle at impact and at the point of departure was similar in crashes on marked and unmarked roads. The data suggests that although the clear-zone may be larger on marked roads, drivers in the observed crashes are not using the additional clear area to slow their vehicles compared to crashes on unmarked roads. TABLE 7 Mean and Median Values for Speed and Departure Characteristics for Crashes with and without Markings Mean Median Variable No Marking Marking No Marking Marking Speed Limit (kph) Velocity at POD (kph) Average Lane Width 1 (m) Lateral Departure Distance (1st Event, m) Includes shoulder for roads with lane markings The differences between the roadside factors examined for crashes occurring on marked and unmarked roads did not appear to explain the differences in injury outcome. Our next approach was to examine the driver characteristics. FIGURE 4 shows the distribution of driver seat belt use for crashes on marked and unmarked roads. Approximately two-fifths of drivers (42%) in crashes on unmarked road were wearing a seat belt compared to two-thirds (67%) of drivers in crashes on marked roads. A chisquared test between road marking and driver belt use shows that they were statistically correlated (p=0.0102). FIGURE 4 Distribution of Driver Seat Belt Use for Crashes on Marked and Unmarked Roads In the sample of crashes examined for this study, driver belt use and presence of lane markings were correlated. Therefore, it was not possible to separate the effect of seat belt use from presence of lane markings. What at first appeared to be correlation between lane markings and injury outcome observed in the data and previous logistic regression was actually related to seat belt use more than presence of lane

12 10 lines. As a crude method of controlling for belt use, only crashes with belted occupants were compared. In these belted-only crashes, approximately 8% and 5% of drivers were seriously to fatally injured on unmarked and marked roads, respectively. A chi-squared test between road markings and injury found the two were not related (p=0.4291). We conclude that for belted drivers lane marking was not correlated with injury severity. DISCUSSION This study examined the presence of center and edge line markings on a representative sample of road departure collisions that occurred on rural and suburban roads. In crashes occurring on 2-lane, undivided roads with 2-way traffic, the data initially suggested that the presence of lane markings may be related to the injury outcome. A logistic regression analysis, that corrected for driver belt use and vehicle departure speed, found that the presence of lane markings decreased the probability of injury in road departure crashes. The finding that lane markings were correlated with injury severity in road departure crashes was an unexpected result. Our hypothesis was that there may be some other roadside or vehicle factors that lane markings were a surrogate for. To investigate this theory, we examined differences in roadside factors between crashes on marked and unmarked roads. We found that many of the roadside factors available were similar between the two groups. Speed limits were lower and departure speeds were higher in crashes on unmarked roads compared to marked roads. Also, it appeared that the clear-zone was larger on marked roads but speed reduction from departure to impact was similar between groups. Our final thought was to examine driver factors. We found that driver belt use was markedly different between crashes occurring on marked and unmarked roads. Only 42% of drivers were wearing their seat belt in crashes on unmarked roads compared to 67% of drivers on marked roads. This result suggests that the primary explanation for higher injury levels on unmarked roads was seat belt use. It is still possible that lane markings may play some role in the severity of road departures. One possible explanation as to why center and edge lines may be related to road departure severity lies in how drivers track lane lines. It has been shown that drivers balance low-level tasks, such as lane keeping, with other driving and non-driving tasks (12). In the presence of secondary driving tasks, drivers may use peripheral vision to stay within their lane (13). If there is not a center or edge line present on the road, lane keeping performance may deteriorate and resulting road departure may be more severe than if there was a lane marking present. The results of this study show, however, that the dominant factor predicting injury outcome in observed crashes was seat belt use, which varies by road characteristics. Previous studies have also found that drivers on rural roads are less likely to wear their seat belts. Drivers in metropolitan areas self-report always wearing their seat belt 87% of the time compared to only 68% of those in rural areas (14). Unmarked roads likely have lower ADT and more rural drivers, which may have affected seat belt use. There were more light trucks and SUVs involved in crashes on unmarked roads (55%) compared to on marked roads (41%), although the difference was not statistically significant. Drivers of light trucks in rural areas are also less likely to wear seat belts compared to other drivers (15). Another limitation of this study is the sample size. The NCHRP dataset was restricted to 12 geographical regions and sampled to be representative of the general crash population. Practices of the local jurisdiction responsible for lane markings may vary by locality affecting the placement of lane markings. The majority of collisions on 2-lane roads with 2-way traffic occurred on continuous segments of road (93%). The relationship to an intersection coded in NASS/CDS is characteristic of the pre-crash phase of the crash. Therefore, even when a crash is intersection related, the actual departure may have occurred outside of the intersection. Of those crashes that were intersection related, 77% had markings at the side of the 1 st departure. The relationship to an intersection may have an effect driver behavior and crash outcome, but due to the sample size this relationship could not be investigated in this study.

13 11 CONCLUSION This study examined a nationally representative sample of road departure collisions where the availability and style of lane markings were investigated. The majority of the sample (55%) was composed of 2-lane, undivided roads with 2-way traffic, which were mostly paved and on rural lands. In paved 2-lane, undivided roads only 19% of crashes did not have a lane marking at the point of first departure. However, this 19% of crashes accounted for 48% of the seriously to fatally injured drivers, suggesting that the presence of either center or edge line markings is correlated with crash severity. To investigate this trend a logistic regression model fit to the probability of an injured driver with lane marking presence, driver belt use, and departure velocity as covariates. This initial model indicated that the presence of lane markings appeared to decrease the odds of the driver being seriously to fatally injured. The finding that lane markings were correlated with injury severity in road departure crashes was an unexpected result. Our hypothesis was that there may be some other roadside or vehicle factors that lane markings were a surrogate for. Many of the roadside factors available were similar between the two groups. It appeared that the clear-zone was larger on marked roads but speed reduction from departure to impact was similar between groups. Our final thought was to examine driver factors. We found that driver belt use was markedly different between crashes occurring on marked and unmarked roads. Only 42% of drivers were wearing their seat belt in crashes on unmarked roads compared to 67% of drivers on marked roads. This result suggests that the primary explanation for higher injury levels observed in crashes on unmarked roads was seat belt use. ACKNOWLEDGEMENT The authors would like to thank Jesse Butch and Lindsey Hatcher for their assistance in comprehensive case review of scene photographs and Stephanie Kusano and Allison Daniello for their assistance in testing software. REFERENCES 1. Najm, W. G., Smith, J. D., and Yanagisawa, M. Pre-Crash Scenario Typology for Crash Avoidance Research. Publication DOT HS NHTSA, U.S. Department of Transportation, Najm, W. G., Schimek, P. M., and Smith, D. L. Definition of the Light Vehicle Off-Roadway Crash Problem for the Intelligent Vehicle Initiative. In Transportation Research Record: Journal of the Transportation Research Board, No. 2069, Transportation Research Board of the National Academies, Washington, D. C., 2001, pp Miller, T. R. Benefit/Cost Analysis of Lane Marking. In Transportation Research Record: Journal of the Transportation Research Board, No. 1334, Transportation Research Board of the National Academies, Washington, D. C., 1992, pp Tsyganova, A. R., Machemehl, R. B., Warrenchuck, N. M., and Wang, Y. Before-After Comparison of Edgeline Effects on Rural Two-Lane Highways. Publication FHWA/TX-07/ Federal Highway Administration, U.S. Department of Transportation, Mak K.K., Sicking D.L., de Albuquerque F.D.B., and Coon B.A. Identification of Vehicular Impact Conditions Associated with Serious Ran-off-Road Crashes. Transportation Research Board, National Cooperative Highway Research Program (NCHRP) Report 665, Kononen, D. W., Flannagan, C. A., and Wang, S. C. Identification and Validation of a Logistic Regression Model for Predicting Serious Injuries Associated with Motor Vehicle Crashes. Accident Analysis and Prevention, Vol. 43, 2011, pp Brennan, P. and Silman, A. Statistical methods for assessing observer variability in clinical measures. BMJ, Vol. 304, pp , June McGinn, T., Wyer, P. C., Newman, T. B., Keitz, S., Leipzig, R., For, G. G. Tips for learners of evidence-based medicine: 3. Measures of observer variability (kappa statistic). CMAJ, Vol. 171, pp , Nov

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