Evaluation of Frequency and Injury Outcomes of Lane Departure Crashes

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1 University of Massachusetts Traffic Safety Research Program Evaluation of Frequency and Injury Outcomes of Lane Departure Crashes Marta Benavente, Heather Rothenberg, Michael A. Knodler Jr. PhD. Published: Compendium of Technical Papers, Institute of Transportation Engineers 2006 Annual Meeting and Exhibit, Milwaukee, WI, August Abstract Lane departure crashes account for approximately 19 percent of all crashes in Massachusetts but almost 46 percent of crashes involving fatal injuries. A lane departure crash occurs when a vehicle leaves the travel lane resulting in a collision. Lane departure crashes typically involve running off the road onto the right or left shoulder and hitting a fixed object, such as a tree, a pole or even a parked vehicle. However, lane departures could involve crossing into an opposite lane and colliding with a vehicle traveling in the opposite direction. Although the vast majority of lane departure crashes are collisions with fixed objects, it can be argued that lane departures resulting in crashes between two moving vehicles are potentially more severe and costly. Therefore, countermeasure implementation strategies should consider not only frequency but also crash costs and severity. Crash data alone are insufficient to effectively evaluate crash injury and cost outcomes. Additional injury data can be gathered by complementing crash data with medical information. The Crash Outcome Data Evaluation System (CODES) links data collected at the crash scene to hospital databases that contain specific injury data; this provides an enhanced understanding of crash injury outcomes by tracking crash victims through the health care system. Massachusetts CODES data were analyzed to examine injury outcomes of lane departure crashes considering hospital charges and length of stay. Results of the analysis would provide a basis for determining where to implement countermeasures based on crash cost and severity rather than frequency alone. UMassSAFE University of Massachusetts 139 Marston Hall Amherst, MA Ph

2 1. Introduction In Massachusetts, from 2002 to 2004, 19 percent of all crashes were lane departure crashes. As the severity of the crash increases so does the percentage of crashes resulting from lane departures. Specifically, lane departure incapacitating injury crashes were 25 percent of all incapacitating injury crashes and lane departure fatal crashes were 46 percent of all fatal crashes. The high percentage of lane departure crashes and severity of these crashes has led Massachusetts to be designated as a lead state in the American Association of State Highway Transportation Officials (AASHTO) Strategic Highway Safety Plan. This initiative aims to reduce the frequency and severity of lane departure crashes working towards achieving the US goal of a traffic fatality rate of 1.0 fatalities/hundred million vehicle miles traveled (VMT) (1). A lane departure crash occurs when a vehicle leaves its travel lane resulting in a collision. Lane departure crashes typically occur when a vehicle runs off the road onto the shoulder and hits a fixed object, such as a tree, pole, guardrail or a parked vehicle. However, some lane departures involve crossing into an opposing travel lane resulting in a head-on collision with a vehicle traveling in the opposite direction. 2. Purpose Lane departure crashes account for a larger percent of crashes as injury severity increases. While lane departure crashes account for less than one-fifth of all crashes, they account for nearly one-half of all fatal crashes in Massachusetts. Although lane departure crashes are most commonly collisions with fixed objects or parked vehicles (96 percent), lane departures crashes involving a two-vehicle head on collision are more likely to be more severe and costly. Consequently, strategies to implement countermeasures should consider not only crash frequency but also crash severity and associated costs. Crash data alone are insufficient to effectively evaluate crash injury and cost outcomes. Additional injury data can be gathered by complementing crash data with medical information. The Crash Outcome Data Evaluation System (CODES) links personspecific statewide crash data to injury data from the health care system resulting in a data set where characteristics of the persons, vehicles and circumstances associated with a crash can be related to their medical and financial consequences. This study analyzes Massachusetts crash data and CODES data to evaluate the impact of lane departure crashes in terms of injury outcomes, hospital charges and length of stay. The study focuses on the potential differences between lane departures that involve a single vehicle colliding with a fixed object versus lane departures that involve a head on crash between two moving vehicles. Results of the analysis would provide a basis for determining where to implement countermeasures based on crash cost and severity rather than frequency alone. 2

3 3. CODES CODES links information collected in crash reports to databases that contain medical information. The CODES project was developed by the National Highway Safety Administration (NHTSA) in response to a Congressional mandate to study the effectiveness of safety belts and motorcycle helmets. The design of a system such as CODES, which is able to interconnect information from the different stages of an injury crash, was required to report the benefits of safety belts and motorcycle helmets on injury severity, health care costs, mortality and morbidity. NHTSA recognized that these types of outcome data could be obtained only at the state level (2). Since 1992, 33 states, including Massachusetts, have been funded to develop CODES programs, conduct data linkages and report findings to NHTSA in a standard format (3). CODES data have the potential to improve highway safety research as they offer a broader view of crash injuries by providing a linked data set that complements crash data with medical data. For example, specific information on type and body location of the injury and the associated hospital charges in dollars provides valuable data to better analyze the nature of injuries and consequences of crashes. Analyses of this nature are necessary to identify more effective countermeasures aimed at minimizing injuries resulting from motor vehicle crashes. CODES links data from various data sources using probabilistic linkage and creates a linked data set that allows for tracking people injured in a motor vehicle crash from the crash scene through the medical system. The probabilistic linkage uses the shared information included in the crash, hospital and emergency medical services (EMS) or emergency department data. Data from different databases are linked by matching specific event characteristics, such as day of crash or location; person characteristics, such as age or sex; and vehicle characteristics. Exact matches using specific join fields select the candidate pairs for linkage. Multiple passes are necessary to ensure that as many cases as possible are included among the candidate pairs. The quality of the linked candidate pairs is then evaluated and the true matches identified, by using match specification fields that are the same for all passes. Most of the administrative statewide datasets available to CODES researchers lack common unique personal identifiers which makes it impossible to relate records for the same person in different databases using a deterministic approach. Another limitation of the datasets is the presence of missing and/or inaccurate data because of paperbased data collection and an emergency environment. Missing data also include the records excluded by state reporting thresholds. Thus, the records that are available and have complete, accurate information are more likely to link, but they may represent only a relatively small and potentially biased sample from the actual population of true record pairs. Eliminating the records with missing data, or guessing what the missing data should be, weakens the data and can lead to biased analysis and incorrect inferences. To solve this problem, the processes used to obtain CODES data multiply impute complete, representative linked datasets. Multiple imputation is a statistical technique 3

4 used for analyzing incomplete datasets that takes into consideration the uncertainty of missing data (4). The CODES program in Massachusetts is conducted through the University of Massachusetts Traffic Safety Research Program (UMassSAFE) of the University of Massachusetts Transportation Center and the Department of Civil and Environmental Engineering. 4. Lane Departure Crashes Lane departure crashes are non-intersection crashes where the vehicle leaves the roadway and strikes a fixed object such as a tree, a pole, or a guardrail; or collides with a parked vehicle; or collides head-on with another moving vehicle. Lane departure crashes for this analysis were identified based on the most harmful event field as recorded on the crash report in the Massachusetts Crash Data System (CDS) database. Similar defining fields were used to identify lane departure crashes in the Fatality Analysis Reporting System (FARS) database, which was used to examine fatal lane departure crashes. For the purpose of this analysis, lane departure crashes were classified into two categories: 1) single vehicle run off the road crashes where the vehicle leaves the road and collides with a fixed object or a parked vehicle and 2) lane departure head on crashes where the vehicle leaves its travel lane crossing into the opposing travel lane and colliding with another vehicle in traffic. 5. Data Analysis Data analysis to examine lane departure crashes was done based on three databases: Fatality Analysis Reporting System (FARS) maintained by National Center for Statistics and Analysis (NCSA), Massachusetts Crash Data System (CDS) maintained by the Massachusetts Registry of Motor Vehicles (RMV), and Massachusetts inpatient discharge data maintained by the Massachusetts Division of Health Care Finance and Policy. FARS and CDS data were used to define lane departure crashes and to gather information on crash characteristics. CDS data were also linked to the inpatient discharge data using CODES to gather additional information on hospital charges and length of stay associated with lane departure crashes. Both CDS and inpatient discharge data for Massachusetts were accessible for use in the CODES project through the University of Massachusetts (UMass) transportation data warehouse. The UMass transportation data warehouse houses data from multiple data sources for use in highway safety data analysis, problem identification, program planning, and program evaluation. The UMass data warehouse serves as a mechanism for validating and interpreting data to improve consistency in data analysis and decision making. Furthermore, the data warehouse also serves as a central repository for maintaining historical information, which in turn allows for analyses not only of current events under current conditions but also past events analyzed in terms of the conditions in which they occurred. 4

5 Data in the warehouse come both from traditional sources such as the Registry of Motor Vehicles and Massachusetts State Police as well as less traditional sources such as the Massachusetts Division of Health Care Finance and Policy and Office of Vital Statistics. This combination of resources translates into access to a variety of data necessary for traffic safety professionals to identify, understand, and address the broad scope of issues associated with highway safety Crash Data For the crash data analysis, CDS and FARS data were used to evaluate lane departure crashes for and consider the two categories of lane departures defined for study. In Massachusetts, lane departure crashes were associated with higher crash severities. Injury severity is based on information gathered by police completing the crash report form using the KABCO scale; crash severity is based on the maximum injury severity associated with anyone in the crash. The KABCO scale is a measure of the functional level of the crash victim at the scene. The KABCO classification levels are representative of the following injury severities: K = Killed; A = Incapacitating Injury; B = Non-incapacitating injury; C = Possible injury; and O = No injury. This scale provides a general understanding of injury severity but does not predict survivability or even hospital admission. The codes are selected based on the on-site judgment of the investigating police officer completing the associated crash report. The variability in criteria and the non-medical background of the reporting agents make these data less than precise to adequately investigate specific injury outcomes. In extreme cases, some victims are transported from the scene before the police officer completing the crash report even arrives (5). Lane departures accounted for 19 percent of all Massachusetts crashes; this percentage increases to 25 percent for incapacitating injury crashes and 46 percent for fatal crashes, as shown in Figure 1. 5

6 Massachusetts All Crashes, Massachusetts Incapacitating Injury Crashes, Massachusetts Fatal Crashes, % 78,259 3,032 25% 46% % 339,837 9,368 75% % Lane Departure Crashes Non Lane Departure Crashes Figure 1 Percentage of Massachusetts crashes that were lane departures by crash severity, (6). Additional analyses examining lane departure crashes by crash severity considered the two types of lane departure crashes. As shown in Table 1, 16 percent of lane departure head on crashes were fatal or incapacitating injury crashes, compared to only four percent of single vehicle run off road crashes. While only 40 percent of lane departure head on crashes resulted in no injuries, 74 percent of single vehicle run off road crashes resulted in no injuries. TABLE 1 Massachusetts Lane Departure Crashes by Crash Severity and Lane Departure Category, Crash Severity Lane Departure Head On Crashes Single Vehicle Run Off Road Crashes Frequency Percent Frequency Percent Fatal (K) 105 3% 507 1% Incapacitating Injury (A) % 2,556 3% Non Incapacitating Injury (B) % 9,503 13% Possible Injury (C) % 6,924 9% Property Damage Only (O) 1,470 40% 55,410 74% Total 3, % 74, % Because of the higher crash severities associated with lane departures crashes, subsequent crash data analysis was limited to fatal and incapacitating injury crashes. Fatal crash information was taken from FARS and incapacitating injury crash information was taken from CDS. In Massachusetts, lane departure head on crashes accounted for 16 percent of fatal and incapacitating injury lane departure crashes. The distribution of fatal and incapacitating injury lane departure crashes over the three year period is shown in Table 2. 6

7 TABLE 2 Massachusetts Fatal and Incapacitating Injury Lane Departure Crashes, Year Single Vehicle Run Off Road Fatal + Incapacitating Injury Crashes Head On Lane Departure Total Percent that are Head On All Fatal + Incapacitating Injury Crashes Percent that are Lane Departure , ,195 15% 4,612 26% ,167 19% 4,609 25% , ,290 14% 4,493 29% TOTAL 3, ,652 16% 13,714 27% To better understand incapacitating injury lane departure crashes that were single vehicle run off road crashes, the object the vehicle collided with was considered. As shown in Figure 2, trees and utility poles are the most common objects struck, accounting for 52 percent of incapacitating injury lane departure crashes. Collision with Curb/ Ditch/ Embankment 21% Other 9% Collision with Tree 26% Collision with Guardrail/ Median Barrier/ Crash Cushion 18% Collision with Utility Pole/ Post 26% Figure 2 Massachusetts incapacitating injury lane departure crashes by object collided with, (6). Subsequent analysis considered the object struck by the functional classification of the roadway. This analysis was conducted in collaboration with the Massachusetts Highway Department (MassHighway). Functional classification could be determined only for those crashes that were geocoded by MassHighway (6); 74 percent of Massachusetts incapacitating injury lane departure crashes were geocoded and 7

8 considered in this analysis. Those crashes that could not be geocoded had insufficient information in the location fields on the crash report form. It would stand to reason that a lane departure crash on an interstate is much less likely to involve a collision with a tree while a lane departure crash on a local road would be unlikely to involve a collision with a guardrail. Table 3 shows this to be true in Massachusetts. On lower level roads (i.e. collector and local roads), trees or utility poles/posts were the most common object struck. On higher level roads (i.e. interstates and arterials), guardrails/median barriers/crash cushions as well as curbs/ditches/embankments were the most common objects struck. It is also interesting to note that curbs/ditches/embankments consistently accounted for approximately 20 percent of incapacitating injury run off road crashes, regardless of functional classification. TABLE 3 Massachusetts Incapacitating Injury Single Vehicle Run Off Road Crashes by Functional Classification and Object Struck Object Collided With Interstate Rural Principal Arterials or Urban Other Expressways Rural Minor Arterial or Urban Principal Arterial Urban Minor Arterial or Rural Major Collector Urban Collector or Rural Minor Collector Local Tree 9% 27% 20% 26% 31% 38% Utility Pole/ Post 2% 10% 37% 38% 37% 27% Guardrail/ Median Barrier/ Crash Cushion Curb/ Ditch/ Embankment 62% 37% 10% 4% 7% 1% 23% 23% 23% 21% 17% 21% Other 4% 3% 9% 10% 7% 12% Total 100% 100% 100% 100% 100% 100% Using only FARS and CDS data, it is evident that lane departure crashes are serious crashes when considering injury severity. In addition, lane departure head on crashes are more severe than single vehicle run off road crashes. Information on the object struck and functional classification of roadways provide additional information on what types of countermeasures might be effective in reducing the frequency and severity of lane departure crashes and where they should be implemented CODES data The CODES data analysis was conducted by linking CDS data to hospital inpatient discharge data for the fiscal year 2003, which extends from October 1, 2002 through September 31, The preparation and examination of the data and the linkage were conducted using CODES2000 software. CODES2000 is a research application that uses Microsoft Access database tools to accomplish probabilistic linkage for 8

9 CODES. The application implements Fellegi and Sunter s statistical theory for record linkage as extended by McGlincy, Newcombe, Jaro, Winkler, Belin, Kelley and others and handles the issue of missing entries by multiply imputing complete, representative linked datasets under a hierarchical Bayesian model (7). For the 12 months of linked data, the average number of links in the imputed datasets was 3,362 linked record pairs. Out of those, the average number of pairs identified as cases resulting from lane departure crashes was 1,021 cases, which corresponds to 30 percent of all linked pairs; 240 cases were categorized as lane departure head on crashes (23 percent of lane departures) and 781 cases as single vehicle run off road crashes (77 percent of lane departures). Linked CODES data made it possible to relate crash characteristics to hospital charges and length of stay associated to crash injuries as this information is accessible through inpatient discharge data. Table 4 summarizes median charges and median length of stay in hospital for all types of crashes in comparison with lane departure crashes and also for the two categories of lane departures identified for study. TABLE 4 Median Hospital Charges and Median Length of Stay Associated with Lane Departure Crashes Median Hospital Median Length Type of Crash Charges of Stay (days) All Crashes $16, Lane Departure Crashes $18, Single Vehicle Run Off Road Crashes $17, Lane Departure Head On Crashes $20, Hospital charges were found to be higher for lane departure crashes than for all crash types ($18,460 vs. $16,302). In addition, when examining the two specific categories studied, lane departure head on crashes showed higher hospital charges than single vehicle run off road crashes ($20,413 vs. $17,918). In terms of length of stay in hospital associated with crash injuries, there were no differences between all crashes and lane departures crashes. However, median length of stay associated with injuries from lane departure head on crashes were found to be one day longer than length of stay associated with single vehicle run off road crashes. Median charges and length of stay were also examined for injuries associated to lane departure crashes that were single vehicle run off road crashes broken down by the object the vehicle collided with. As shown in Table 5, hospital cases resulting from collisions with trees and run off road left or crossing median were the two types that had the highest median charges and also the highest median length of stay in hospital. 9

10 TABLE 5 Median Hospital Charges and Median Length of Stay Associated with Single Vehicle Lane Departure Crashes by Collided With Object Median Hospital Median Length of Object Collided With Charges Stay (days) Tree $22, Post, utility pole or light pole $15, Guardrail, median barrier or crash cushion $16, Curb, ditch, or embankment $16, Parked vehicle $18, Ran off road left or cross median $21, Ran off road right $14, Other/unknown $17, It should be noted that CODES data were not used to examine the charges or length of stay associated with crash characteristics/fields that could only be obtained through the use of geocoded data (such as functional classification). Future analysis which consider the fields associated with geocoded data could provide additional insight into lane departure crash charges in terms of the types of roadways on which the crashes occur. Linked data were also used to validate the accuracy of the KABCO scale to determine injuries resulting from vehicle crashes. As mentioned, KABCO codes are a measure of the level of injury of crash victims at the crash scene and may be approximate. Table 6 summarizes the percentage of cases by KABCO scale that were linked and classified as resulting from lane departure crashes. As shown, 92 percent of linked records were classified as K, A, B, C which correspond to injury levels. Only 3 percent of crashes linked to inpatient records were cases reported as no injury, and 5 percent had no injury reported. TABLE 6 Linked Records by KABCO Scale Number of linked Percentage of linked KABCO records records Fatal (K) 52 3% Incapacitating (A) 29 45% Non-incapacitating (B) % Possible (C) % No injury (O) 135 3% Not reported 33 5% TOTAL 1, % The CODES data analysis supports the results obtained from the crash data analyses adding valuable hospital data. Lane departure crashes account for a higher percentage of severe crashes in terms of injury, and accordingly, hospital charges were higher for lane departure crash victims. In addition, victims of lane departure head on crashes 10

11 had higher hospital charges and lengths of stay than those victims of single vehicle run off road crashes. Analyses by object struck showed that collision with a tree and crossing median/run off left had the highest associated hospital charges of all. This information provides additional insight to determine and prioritize what types of countermeasures should be implemented to reduce the impacts of lane departure crashes. 6. Conclusions and recommendations Lane departure crashes result in a much higher proportion of severe injuries than other types of crashes making them a serious concern in traffic safety. Countermeasures to reduce the frequency and severity of this type of collision should be a priority for transportation engineers. Complementing the analysis of crash data with CODES data gives additional information that allows taking into account not only injury severity but also the associated cost of injuries resulting from lane departure crashes. The two categories of lane departure crashes identified in this study were compared in terms of injury severity, hospital charges and length of stay. Lane departure head on crashes were found to result in more severe injuries and had higher associated costs and lengths of stay in hospital than single vehicle run off road crashes. Crash data showed higher frequency of single vehicle crashes colliding with a tree or a pole. CODES data showed that single vehicle collisions with trees and single vehicle running off left or crossing median had the highest hospital charges and days in hospital among single vehicle crashes. Road safety analysis should evaluate not only frequency but also severity of crashes; in the case of lane departure crashes the more frequent type of crash (single vehicle run off road) has lower associated injury severities, lower associated inpatient charges, and shorter length of stay. Crash data alone could be insufficient to examine specific injury outcomes and costs of motor vehicle crashes. CODES data offer a comprehensive dataset which includes characteristics of the crash and also hospital information such as economic charges and length of stay. This information can help prioritize and determine where to implement strategies to improve road safety and which countermeasures to use. 11

12 7. References 1. NCHRP Project Implementing the AASHTO Strategic Highway Safety Plan. Accessed May Johnson, S.W., and J. Walker. The Crash Outcome Data Evaluation System (CODES). DOT HS NHTSA Technical Report. U.S. DOT, Washington D.C., January Crash Outcome Data Evaluation System Contact List. As of: June 13, CODES 2005 TA Meeting. Bloomington, MN, June Benavente, M., M. Knodler and H. Rothenberg. A Case Study of Crash Data Challenges: Linking Databases for Analyzing Injury Specifics and Crash Compatibility Issues. Transportation Research Board, Transportation Research Record. In Press. 5. Blincoe, A. et al. The Economic Impact of Motor Vehicle Crashes, DOT HS Plans and Policy NHTSA. Washington D.C., May MassHighway and UMassSAFE. Lane Departure Crash Data Analysis Fact Sheet. Accessed May McGlincy, M. A Bayesian Record Linkage Methodology for Multiple Imputation for Missing Links. Joint Statistical Meeting August 9,

13 Authors Information Marta Benavente Research Fellow University of Massachusetts Traffic Safety Research Program (UMassSAFE) 139 Marston Hall University of Massachusetts, Amherst Amherst MA Phone Number: Fax Number: Heather Rothenberg Project Coordinator and Lead Analyst University of Massachusetts Traffic Safety Research Program (UMassSAFE) 139 Marston Hall University of Massachusetts, Amherst Amherst MA Phone Number: Fax Number: Michael A. Knodler Jr. PhD. Assistant Professor Civil and Environmental Engineering University of Massachusetts, Amherst 216 Marston Hall University of Massachusetts, Amherst Amherst MA Phone Number: Fax Number:

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