combined effects on motorcycle crashes of rider behavior (helmet use, .... Night. 1727. 106. 0.0614. 50. 56. 111.4%. Rider Characteristics and Behavior. CRR.
Transportation Research Record 1818 ■ Paper No. 02-2885
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Typology of Motorcycle Crashes Rider Characteristics, Environmental Factors, and Spatial Patterns Karl Kim, Joseph Boski, and Eric Yamashita Research was conducted on motorcycling and alcohol-impaired motorcycling in Hawaii to develop a crash typology. The investigation expanded the scope of rider characteristics analysis by examining the combined effects on motorcycle crashes of rider behavior (helmet use, speeding, actions taken before a crash, etc.), environmental factors (urban versus rural locations, roadway alignment, etc.), and spatial patterns. The crash typology was used to derive logistic regression models for explaining alcohol-involved crashes, single-vehicle crashes, and injury outcomes (including fatalities) associated with motorcycle crashes. The logistic models allowed comparisons of the relative importance of various rider characteristics, temporal and environmental correlates associated with motorcycle crashes, and the associated crash types and injury outcomes. Finally, a spatial cluster analysis was also performed with both geographic information system and spatial analytical tools. The analysis suggests that overall behavioral and temporal factors are more significant predictors of alcohol-involved crash patterns than environmental or roadway features. The findings are qualified in terms of the usefulness of the methods to motorcycle safety researchers and relevance to motorcycle safety initiatives in the state of Hawaii.
Motorcycle safety is of concern not only in Hawaii but throughout the world. Nationwide motorcycle deaths, after nearly two decades of decline, have increased significantly during the last 3 years. In the latest available annual data, rider fatalities rose 15.3% in 1 year, from 2,483 in 1999 to 2,862 in 2000 (1). Although the numbers overall are much smaller in Hawaii (approximately 450 injured riders and 15 fatalities per year), they exceed the state’s expected share based on a simple ratio of population. Because approximately 70% of the state’s population resides within the largest county and because of that county’s geographic isolation as the island of Oahu, research was limited to the city and county of Honolulu (a single county-level administrative district). Hawaii provides a relatively ideal environment for closely studying motorcycle safety. With a year-round favorable climate, relatively short travel distances associated with island living, and high costs of fuel and transportation in comparison to mainland cities, motorcycling is a popular means of travel. Hawaii also provides an excellent location in which to conduct motorcycle safety research, because with only four county governments and only four police departments in the entire state and the associated consolidation of data reporting and collecting methods, the quality and consistency of police crash reporting are arguably better than in other places. Furthermore, specially trained accident investigators are dispatched Department of Urban and Regional Planning, University of Hawaii at Manoa, 2424 Maile Way, Suite 107, Honolulu, HI 96822.
to the scene of all injury-producing motor vehicle crashes in the state (including, naturally, all motorcycle-involved crashes). This research builds on previous research in traffic safety analysis. The University of Hawaii developed a special expertise in accident analysis and statistical modeling. In 1992, the University of Hawaii received a crash outcome data evaluation system (CODES) grant from NHTSA. This project involved the acquisition of crash data; emergency medical service data; and hospital, insurance, and other information related to traffic safety. In addition to producing a linked database and performing various statistical analyses, the project also led to the development of a comprehensive traffic safety geographic information system (GIS) for mapping and analyzing all police-reported crashes in Hawaii. Associated with these efforts were several motorcycle safety research projects (2–4). The purpose of this research is threefold. First, by using data from Hawaii, a typology of motorcycle crashes was developed to illustrate the most important dimensions or factors influencing the likelihood of a motorcycle crash. Second, by using this typology and logistic regression techniques, a statistical model was built to allow the examination of the interrelationships among rider, temporal and environmental factors, and the likelihood of crashes and the associated injury outcomes. Finally, by using GIS tools, the spatial pattern of motorcycle crashes was characterized to determine if there are key locations for introducing interventions for increasing motorcycle safety. Although focused on Hawaii, the intent of the research is to explore both methodologies as well as the implications of the findings for enhancing motorcycle safety, and therefore it may be of interest to researchers in other places. The phrase “rider characteristics” refers to analysis of rider behavior during or immediately preceding the crash. As the variety of data in this study is already quite extensive and as rider demographics were discussed well and at length elsewhere for Hawaii and other regions (2, 5), the emphasis of this analysis is on understanding the interaction among rider, temporal and environmental factors, and their effects on crashes and injury outcomes.
DATA AND METHODS According to state records, an estimated 35,000 persons have motorcycle licenses and there are approximately 18,000 registered motorcycles in the state of Hawaii. Insurance data indicate that approximately 11,000 current policies cover motorcyclists in the state. Crash data, police citation data, and anecdotal sources indicate significant numbers of unregistered motorcycles, unlicensed riders, and uninsured motorcyclists in Hawaii. Over the 10-year period examined (1986 to 1995), there were approximately 525 police-reported
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Transportation Research Record 1818
log e [ Pr( E ) 1 − Pr( E )] = f ( B, R, T )
motorcycle crashes per year. According to police crash data, approximately 20 crashes per year involved alcohol. It is likely that there is a large amount of underreported alcohol involvement in crashes. This is evidenced in the linked police crash and hospital records. The CODES Project (6) found that among hospitalized victims of motor vehicle crashes, police reported alcohol involvement in 8% of the cases. Yet hospital records for the same individuals show alcohol involvement in 25% of the cases, more than three times the number reported by the police. Although it would be ideal to further analyze and compare the differences in alcohol involvement between police-reported crash data and hospital data, at the time of this research a limited number of years of hospital data was made available, thus making further comparisons and analyses difficult over the same 10-year period. In the study presented here, a database of all police reported motorcycle crashes over the period 1986 to 1995 was built. This period is used because of the availability of CODES-linked data at the time the research was conducted. The data were analyzed by using a Sun workstation and a version for personal computer of SAS, a statistical analysis package. After basic frequency distributions and inferential statistical tests were run, the nature of motorcycle crashes was characterized for key environmental, temporal, and behavioral factors. Alcohol-impaired crashes and single-vehicle crashes were examined. A simple reporting method called multipliers (7 ) was used to calculate expected values that were then compared to the actual values for various combinations of rider factors, temporal and environmental conditions, and crash outcomes. These results are summarized in a series of tables that provide a revealing look at the principal factors associated with motorcycle accidents. Next, by using inputs from the preceding analysis, a series of logistic regression models was developed, and the associated odds ratios for various events such as alcohol-impaired crashes, single-vehicle motorcycle crashes, injury-producing crashes, and fatality-producing crashes as a function of various rider, temporal, and environmental correlates were calculated. The logistic regression procedure was used because it provides a powerful tool for estimating the odds of a particular event occurring subject to various conditions. It allows the examination of categorical data such as whether an alcoholimpaired crash occurred or whether an injury-producing accident resulted, while controlling for various environmental, temporal, or behavioral factors. To illustrate, let E represent the odds of a particular event occurring (such as an alcohol-impaired crash), let other variables represent various behavioral, B, roadway, R, and temporal factors, T:
TABLE 1
The dependent variable, the logged odds of an event occurring over the odds of it not occurring, log e [Pr(E) / [1 − Pr(E)], is contingent on the behavioral characteristics of the riders, as well as roadway and temporal factors. The modeling process involves fitting various terms and factors associated with the events occurring and assessing model fit by using various goodness-of-fit measures and by examining the concordance between the fitted and observed pairs. This approach builds on previous research at the University of Hawaii on log–linear modeling (8); the application of methods and analytical techniques for traffic safety research are described in detail elsewhere (9, 10). After the logistic regression models were built, the ArcView GIS software was used to map and spatially analyze the locations of various types of motorcycle crash. By using a spatial clustering procedure, the spatial location of various types of crash was mapped to determine if there were key locations for launching motorcycle safety initiatives.
FINDINGS: CRASH TYPE ANALYSIS The research began with an investigation of the nature and extent of alcohol-related motorcycle crashes. As shown in Table 1, there were a total of 3,960 police-reported motorcycle crashes during the period of 1986 to 1995. Of these, 115 involved alcohol impairment. The multiplier approach, developed in an earlier paper (7), is a convenient yet robust tool for examining relative frequencies. The overall multiplier for alcohol-related motorcycle crashes (115 / 3,960) is 0.0290. In other words, for each crash there are approximately 0.029 alcohol-involved crashes. Alternatively, one might say there are 2.9 alcohol-related crashes for every 100 motorcycle crashes. Table 1 also allows comparison of the multipliers of other types of alcoholinvolved crashes. For example, the multiplier for rural alcoholinvolved crashes (12 / 265) is 0.0453, which is higher than the overall multiplier for alcohol-involved crashes. It is possible to gauge the extent to which the rural factor contributes more to alcohol crashes by comparing the expected value (265 × 0.0290) to the actual number of rural alcohol-involved crashes. Note that the actual number exceeds the expected value by four crashes. This suggests that rural locations are somewhat prone to alcohol-involved motorcycle crashes. By applying the same method to other factors, it is seen that time of day (night), speeding, and careless risky and reckless behav-
Characteristics of Alcohol-Related Crashes
Alcohol
Crashes All Oahu Crashes 3960 Environmental Factors Rural 265 Temporal Factors Night 1727 Rider Characteristics and Behavior CRR 1370 Speeding 785 No Helmet 2122 Other Factors Single Vehicle 1599
(1)
Expected (Actual) Multiplier Value
Exceeds Expected by: #
%
115
0.0290
n/a
n/a
n/a
12
0.0453
8
4
55.9%
106
0.0614
50
56
111.4%
76 57 82
0.0555 0.0726 0.0386
40 23 65
36 34 17
87.8% 150.0% 26.9%
83
0.0519
46
37
78.7%
Kim et al.
Paper No. 02-2885
TABLE 2
Characteristics of Single-Vehicle Crashes
Single Crashes
(Actual)
All Oahu Crashes 3960 1599 Environmental Factors Curved 788 618 Road Defect 207 163 Rural 265 154 Road Was Wet, 537 294 Oily, etc. Rider Characteristics and Behavior Speeding 785 538 Alcohol 115 83
iors (CRRs), which include disregarding of traffic controls, failure to yield, speeding, and other rider actions, are also strongly associated with alcohol-related crashes. It is important to note that in addition to the CRR variable, speeding was coded by itself to isolate the associations between speeding-related and alcohol-impaired crashes. The actual number of speeding-related alcohol-impaired crashes (57) was 150% greater than the expected value (23). In addition, the actual number of nighttime alcohol-crashes (106) was more than double the expected value (50). Crashes involving risky and reckless actions on the part of riders also accounted for a larger number of alcohol-impaired crashes (76) than expected (40). This finding, along with the association between nonhelmeted-rider crashes and impaired-rider crashes, supports the notion that personal, behavioral, and temporal factors are strongly associated with alcoholimpaired crashes. Table 1 shows that alcohol impairment is more likely in single-vehicle crashes than in other crashes. The expected value is 46, yet 83 alcohol-impaired single-vehicle crashes occurred over the examined period. Table 2 focuses on characteristics of single-vehicle crashes, and the same methods are used as shown in Table 1. Of the 3,960 crashes involving motorcycles, 1,599 were single-vehicle motorcycle crashes. As Table 2 reveals, the actual number of both speedinginvolved and alcohol-impaired crashes exceeds the expected values by 69.7% and 79.7%, respectively. Table 2 also contains data on environmental factors, such as the effects of curved roads, roadway defects, rural location, and if the roadway surface was wet or oily. Each of these factors produced higher than expected frequencies for single-vehicle crashes. There were 301 more crashes on curved
TABLE 3
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Multiplier
Expected Value
0.4038
Exceeds Expected by: #
%
n/a
n/a
n/a
0.7843 0.7844 0.5811 0.5475
317 83 107 216
301 80 47 78
95.0% 96.4% 43.9% 36.1%
0.6853053 0.7217391
317 46
221 37
69.7% 79.7%
roads than expected. There were 80 more crashes on surfaces with defects, 78 more on wet or oily surfaces, and 47 more in rural locations than expected. These findings suggest that roadway conditions are relevant for single-vehicle crashes. Table 3 was prepared to investigate the relationships among various types of crash and injury outcomes. This table isolates the various types of crash and examines the associated injuries. Again, the multiplier technique of the first two tables was used. Injuries were defined to include all fatal and incapacitating injuries. This corresponds with the K (killed) and A (incapacitating) injury levels on the police KABC0 scoring system (where B is nonincapacitating injury, C is possible injury, and 0 is no injury); in other words, injuries in this report refers to serious injuries. By using the injury multiplier for all motorcycle crashes and applying it to the frequency of certain types of crash (e.g., curved road, rural, night), an expected injury frequency can be derived and then compared with the actual frequencies to measure the extent to which certain conditions produce a higher than expected level of injury. Table 3 thus reveals that curved roads produce 55.6% more injury crashes than expected, whereas rural locations are associated with 53.3% more injury crashes than expected. Night is responsible for 17.5% more injury crashes than expected, and speeding- and alcohol-involved crashes greatly increase the likelihood of fatal and incapacitating crashes. The actual number of alcohol-impaired crashes with injuries is 39, compared with the expected value of 20. The actual frequency of speed-related crashes is 254, compared with the expected value of 121. Single-vehicle crashes produced 23.9% more fatalities and incapacitating injuries than expected.
Characteristics of Crashes with Serious Injuries
Crashes All Oahu Crashes 3749 Environmental Factors Curved Road 759 Rural 254 Temporal Factors Night 1616 Rider Characteristics and Behavior Speeding 750 Alcohol 112 Other Factors Single 1197
Injuries Expected (Actual) Multiplier Value
Exceeds Expected by: # %
664
0.1771
n/a
n/a
n/a
210 69
0.2767 0.2717
135 45
75 24
55.6% 53.3%
336
0.2079
286
50
17.5%
254 39
0.3387 0.3482
133 20
121 19
91.0% 95.0%
337
0.2197
272
65
23.9%
50
Paper No. 02-2885
Transportation Research Record 1818
TABLE 4
Alcohol-Impaired Crashes
95% CL Variable
Parameter
s.e.
Chi-Square
Intercept Helmet Risky Night Single
-6.2606 -0.7766 1.3220 2.4357 0.9841
0.4324 0.2822 0.2966 0.3773 0.2618
209.6369 7.5702 19.8678 41.6655 14.1322
p-value Odds Ratio
Low
High
