Risky Driving: The Relationship between Cellular Phone and Safety Belt Use

Transcription

1 Risky Driving: The Relationship between Cellular Phone and Safety Belt Use David W. Eby,* Lidia P. Kostyniuk, Jonathon M. Vivoda The University of Michigan Transportation Research Institute Social and Behavioral Analysis Division 2901 Baxter Rd., Ann Arbor, MI (p); (f) Word Total: 3,864 Submitted in revised form: November 7, 2002 * Author for all correspondence

2 Eby, Kostyniuk, & Vivoda 1 ABSTRACT The main purpose of the present study was to explore the relationship between cellular phone and safety belt use. Safety belt use rates of drivers using and drivers not using hand-held cellular phones were compared. All safety belt and hand-held cellular phone use data were collected through direct observation while vehicles were stopped at intersections and freeway exit ramps in Michigan. Data were weighted to be representative of drivers during daylight hours in Michigan. Analyses included statistical comparisons of safety belt use rates and a logistic regression model to determine the effect of hand-held cellular phone use on safety belt use. The study found that safety belt use for drivers using a hand-held cellular phone was significantly lower than for drivers not using cellular phones. This same significant relationship was found within nearly all demographic categories analyzed. The logistic regression model showed that the odds of a hand-held cellular phone user not using a safety belt was 1.77 times that of a driver not using a cellular phone. These results stress the importance of the public health issue posed by cellular phone use; not only are those who are conversing on cellular phones potentially more likely to be in a motor vehicle crash, they are also more likely to sustain greater injury due to the lack of safety belt use.

3 Eby, Kostyniuk, & Vivoda 2 INTRODUCTION Cellular phone ownership in the United States (US) is predicted to increase dramatically over the next few years. Industry estimates show that in 2000, about 35 percent of the adult US population subscribed to a mobile phone service, with ownership expected to increase to more than 80 percent by 2005 (1). Cellular phone use is high, with as much as 40 percent of subscribers using their phone on a daily basis (2). Studies of locations where people use cellular phones show that use is common while traveling in a motor vehicle (2, 3, 4). On-the-road direct observation studies of cellular phone use show that 1.5 to 3.0 percent of drivers are utilizing a hand-held cellular phone at any given time during daylight hours (5, 6, 7). According to NHTSA (7) estimates, about 600,000 drivers in the US use a cellular phone at any given time. There are obvious benefits for having access to a cellular phone while traveling in a motor vehicle, such as having the ability to contact emergency personnel during a crisis. Use of a cellular phone while driving, however, may lead to an increased risk of crashes. Simulator (e.g., 8, 9)and onthe-road studies (10, 11) show that both dialing the phone and engaging in complex conversations can disrupt tasks that are important for safe driving. The association between cellular phone use and crashes is quite difficult to establish, but evidence is beginning to suggest that cellular phone use elevates crash risk (12). If so, according to some definitions (e.g., 13), use of a cellular phone while driving would be considered a risky-driving behavior. Failure to use a safety belt is also a risky-driving behavior, in that injury risk from a crash is elevated with nonuse. In a recent study (5) on cellular phone use in Michigan, it was discovered that drivers who were talking on cellular phones had significantly lower safety belt use rates than drivers who were not. Because of the relatively small sample of cellular phone users in the study, we were unable to explore the relationship between cellular phone and safety belt use. The purposes of the present study were to: replicate the previous result using a different survey design and larger sample and; explore in detail the relationship between cellular phone and safety belt use. METHODS All data in the study were collected through direct observation while vehicles were stopped at intersections and freeway exit ramps in Michigan. The goal of the sample design was to select observation sites that accurately represent drivers in passenger cars, vans/minivans, sport-utility vehicles, and pickup trucks by race, age, and sex in Michigan. All 83 Michigan counties were rank ordered by population (14) and the low population counties were eliminated from the sample space. This step reduced the sample space to 28 counties. In order to ensure that our sample was representative of the major racial groups in Michigan, these 28 counties were then rank ordered by percent of Black/African Americans within each county. The counties with a percentage smaller than 6 percent were removed, reducing the sample space to 13 counties. Vehicle miles of travel (VMT) were obtained for each county by roadway type (trunkline, city area non-trunkline, and non-city area non-trunkline). Safety belt use rates by county were also calculated using data from the most recent statewide direct observation survey of safety belt use in Michigan (15). The 13 counties were grouped into 4 geographic strata based on the percentage of African-Americans in the population and safety belt use. Each geographic stratum was further divided

4 Eby, Kostyniuk, & Vivoda 3 by the three roadway types resulting in 12 strata in the sampling design. Within the 13 counties in the sample, observations were conducted at 400 sites. Because total VMT within each geographic stratum were not equal, the number of observation sites chosen within each geographic stratum was adjusted to account for this difference. In addition, because VMT varies by roadway type, the number of trunkline, city area non-trunkline, and non-city area non-trunkline sites within each geographic stratum was also adjusted. The percent of Black/African American residents in city and non-city areas within each county was also taken into account in this adjustment. Within each stratum, observation sites were randomly assigned. The day of week and time of day for site observations were assigned to sites in such a way that all days of the week and all daylight hours (7:00 am - 7:00 pm) had essentially equal probability of selection. The sample design was constructed so that each observation site was weighted by the traffic volume at the site. This was accomplished by selecting sites with equal probability and by setting the observation interval to a constant duration (50 minutes) for each site. However, since all vehicles passing an observer could not be surveyed, a vehicle count of all eligible vehicles (i.e., passenger cars, vans/minivans, sport-utility vehicles, and pickup trucks) on the traffic leg under observation was conducted for a set duration (5 minutes) immediately prior to and immediately following the observation period (10 minutes total). The vehicle count was used to estimate the traffic volume at each site. Further details on the sample design can be found elsewhere (16). Prior to data collection, field observers participated in 5 days of intensive training including both classroom review of data collection procedures and practice field observations. After intensive review of a training manual, observers conducted practice observations at several sites chosen to represent the types of sites and situations that would actually be encountered in the field. None of the locations of the practice sites were the same as sites observed during the study. Each observer continued training until an interobserver reliability of at least 85 percent was achieved. During data collection, each observer was spot checked in the field on at least two occasions by the field supervisor. Data collection for the study involved direct observation of shoulder belt use, hand-held cellular phone use, sex, race, and estimated age of drivers during daylight hours from April 8, 2001 through May 1, Observations were conducted when a vehicle came to a stop at a traffic light or a stop sign. Observers collected data from vehicles in only the lane immediately adjacent to the curb, regardless of the number of lanes present. During the observation period, observers recorded data for as many eligible vehicles as they could. If traffic flow was heavy, observers recorded data for the first eligible vehicle they saw, and then looked up and recorded data for the next eligible vehicle they saw, continuing this process for the remainder of the observation period RESULTS Statewide hand held cellular phone use was 3.6 ± 0.47%. Safety belt use rates between those using and those not using a hand-held cellular phone were compared statistically as follows. Each stratum (s) in the sample contained N s intersections, where N s was unknown. A sample of n s sites was selected from the N s intersections and each sampled site was observed. Observation periods at all sites were equal. During the observation period at site i, M i vehicles passed, of which m i were sampled and

5 Eby, Kostyniuk, & Vivoda 4 safety belt use within the vehicle were recorded. At each site i, m i vehicles with x i persons using a hand-held cellular phone, of which y i were using safety belts were observed. The estimates of the safety belt use rate for hand-held cellular phone users R s within stratum s were calculated as: R ns i s = = 1 ns i= 1 where w i = M i /m i yw i i xw i i The estimate of the overall statewide rate (R) of safety belt use for the hand-held cellular phone users group was obtained from: R = 12 s= 1 RW s s where W s is the ratio of vehicle miles traveled in stratum s relative to vehicle miles traveled in the state. The variance for safety belt use among hand-held cellular phone users within stratum s, was calculated as: V s ns = ns ns 1 i= 1 ns wx i i k = 1 wx k k 2 ( ri R) 2 where r i is the weighted safety belt use rate of hand-held cellular phone users at site i. The estimate of the overall statewide variance, V, of safety belt use for hand-held cellular phone users group was: V = 12 s= 1 Vs( Ws) 2 Rates and standard errors for safety belt use among those not using hand-held cellular phone was calculated in the same way, with x i being the number of persons observed not using a hand-held cellular phone at a site. In order to compare statistically the difference between safety belt use rates of hand-held cellular phone users and those not using hand-held cellular phones, STATA software was

6 Eby, Kostyniuk, & Vivoda 5 used to calculate the t-statistic. Table 1 shows safety belt use rates, 95 percent confidence bands, and unweighted numbers of drivers in the samples using and not using hand-held cellular phones. Also shown in Table 1 are the standard errors of the differences between the safety belt use rates (SE d ) and the t-statistic. Only categories in which there was a significant difference in safety belt use rates between users and nonusers of hand-held cellular phones are included in this table Insert Table 1 About Here Overall, those using cellular phones had significantly lower safety belt use than those not using phones (Table 1). This difference was more than 10 percentage points, illustrating that drivers in Michigan use safety belts significantly less often when conversing on a hand-held cellular phone than when not using these phones. This result replicates previous work with a small sample of cellular phone users (5). Safety belt use rates between those using and those not using a hand-held cellular phone by sex, race, age, vehicle type, roadway type, time of day, and sex/age were also investigated. The study found significantly lower safety belt use for those using cellular phones among men, women, Whites, Black/African Americans, year olds, year olds, 65-year-olds or older, passenger vehicles, freeways, local roads, between 7 AM and 1 PM, women years of age, and men 30 years of age or older. The effects of hand-held cellular phone use on safety-belt use was further explored using a logistic regression model on the data collected in the study. The model estimates the probability of safety belt use of an individual as a function of the observed characteristics of that individual. The model form was: p i = β kxk k e kx k + e β 1 k where p i is the probability of individual i using a safety belt, X k is a set of independent variables, β k are the coefficients to be estimated. STATA software was used to estimate the parameters of the model and select the best model. The initial full model contained independent variables for: hand-held cellular phone use, sex, age (over 15 years), race, roadway type, vehicle type, and time of day. The final best model was selected through a backward selection process. The final variables, their β coefficients, standard errors, and t-statistics are shown in Table Insert Table 2 About Here As shown in Table 2, the model found that hand-held cellular phone use was significantly related to a lack of safety belt use. The β coefficients can be interpreted as the odds ratio of observing

7 Eby, Kostyniuk, & Vivoda 6 safety belt use, relative to observing safety belt nonuse, controlling for individual level covariates. Thus, the odds of safety belt use for a hand-held cellular phone user is.564 times that of a person not using a hand-held cellular phone. Stated differently, the odds of a hand-held cellular phone user not using a safety belt is 1.77 times more than that of a person not using a hand-held cellular phone. DISCUSSION The study found that safety belt use for hand-held cellular phone users was significantly lower than for those not using a hand-held cellular phone. This result stresses the importance of the public health issue posed by cellular phone use; not only are those who are conversing on cellular phones potentially more likely to be in a motor vehicle crash, they are also more likely to sustain greater injury due to the lack of safety belt use. The observed difference in safety belt use between hand-held cellular phone users and nonusers cannot be explained by differences in the demographic composition of the samples. As shown in Table 1, when each sample was compared within demographic categories, we found significantly lower safety belt use for those using hand-held cellular phones in nearly every category. It is possible, however, that some other demographic variable that is known to affect safety belt use but was not measured in the current study, such as income (17), accounts for these results. While the reasons for lower belt use in the hand-held cellular phone user group will need to be addressed in further research, there are at least two classes of hypotheses, besides differing demographics, for the relative lack of safety belt use in this group. First, it is possible that use of the hand-held cellular phone causes a functional disruption of safety belt use. For example, it is possible that the mental and physical processes involved in using a mobile phone during the start of a trip may disrupt the sequence of actions, or script, that lead to use of safety belts. As another example, is it possible that use of a hand-held cellular phone, on occasion, may require a person to unbuckle their safety belt. This could occur if the cellular phone had to be retrieved from a bag or the back seat. The person may unfasten their safety belt to retrieve a ringing phone and then forget to refasten the belt once he or she begins a conversation. If hand-held cellular phone use leads to lower safety belt use because of a functional disruption, then technology-based countermeasures may be particularly effective, such as safety belt reminder systems or improved cellular phone system designs. Research on safety belt use for drivers using hands-free cellular phones, eating, using personal data assistants (PDAs), reading, or engaging in other distracting activities (factors that could not investigated in the present study), would be helpful in testing this class of hypotheses. A second class of hypotheses to account for the lower safety belt use of those on cellular phones are related to personality differences between drivers who frequently use cellular phones and those who do not. For example, those who use cellular phones while driving may be greater risktakers than those who do not use phones while driving. It is possible that people are generally aware of the fact that use of a hand-held mobile phone is a crash-risk factor in the same way that they are aware that lack of safety belt use is an injury-risk factor and they choose to ignore both risks. There is good evidence that risky behaviors tend to co-occur (e.g., 18, 19, 20, 21). If personality differences account, at least in part, for the lower safety belt use of hand-held cellular phone users, then effective countermeasures would be quite difficult to develop. High visibility police enforcement may be an effective way to increase safety belt use of risky drivers. Whatever the reason for lower belt use among the cellular phone user population, it is

8 Eby, Kostyniuk, & Vivoda 7 important to begin to focus public information and education efforts on the co-occurring risky-driving behaviors of hand-held cellular phone use and safety belt nonuse. ACKNOWLEDGMENTS This work was sponsored by the Michigan Department of State (DOS) through contract number 071B The opinions, findings, and conclusions expressed in this publication are those of the authors and not necessarily those of the DOS. Dr. Hans C. Joksch and Devi Putcha of the University of Michigan Transportation Research Institute provided assistance on the statistical analyses. Linda Miller provided valuable comments on a previous version of this manuscript and assisted in its production.

9 Eby, Kostyniuk, & Vivoda 8 REFERENCES 1. Telecompetition, Inc. US Mobile Phone Subscribers and Penetration Rates: San Ramon, CA: Telecompetition, Inc, Gallup. Half of All Americans Own a Cellular Phone: Many Chat Frequently Behind the Wheel. Accessed: 9/18/ Bureau of Transportation Statistics. August 2000 Household Survey Results. Washington, DC: US Department of Transportation, Insurance Research Council. Public Attitude Monitor Wheaton, IL: Insurance Research Council Eby, D.W., and J.M. Vivoda. (in press). Driver hand-held mobile phone use and safety belt use. Accident Analysis & Prevention. 6. Hornberry, T., C. Bubnich, L. Hartley, and D. Lamble. Drivers use of hand-held mobile phones in Western Australia. Transportation Research Part F, Vol. 4, 2001, pp National Highway Traffic Safety Administration. Passenger Vehicle Driver Cell Phone Use Results from the Fall 2000 National Occupant Protection Use Survey. Report No. DOT-HS Washington, DC: US Department of Transportation, McKnight, A.J. and A.S. McKnight. The effect of cellular phone use upon driver attention. Accident Analysis & Prevention, Vol. 25, 1993, pp Serafin, C., C. Wen, G. Pailke, and P. Green. Development and Human Factors Tests of Car Phones. Report No. UMTRI Ann Arbor, MI: University of Michigan Transportation Research Institute, Brookhuis K.A., G. devries, and D. de Waard. The effects of mobile telephoning on driving performance. Accident Analysis & Prevention, Vol. 23, 1991, pp Tijerina, L., S. Kiger, T.H. Rockwell, and C. Tornow. Final Report-Workload Assessment of In-Cab Text Message System and Cellular Phone Use by Heavy Vehicle Drivers on the Road. Report No. DOT-HS Washington, DC: US Department of Transportation, Redelmeier, D.A., and R.J. Tibshirani. Association between cellular-telephone calls and motor vehicle collisions. The New England Journal of Medicine, Vol. 336, No. 7, 1997, pp Eby, D.W., and L.J. Molnar. Matching Safety Strategies to Youth Characteristics: A Literature Review of Cognitive Development. Report No. DOT-HS Washington, DC: US Department of Transportation, 1999.

10 Eby, Kostyniuk, & Vivoda US Bureau of the Census. Census 2000 Results. ttab04.pdf. Washington, DC: US Department of Commerce. Accessed 10/26/ Eby, D.W., T.A. Fordyce, and J.M. Vivoda. Michigan Safety Belt Use Immediately Following Implementation of Standard Enforcement. Report No. UMTRI Ann Arbor, MI: University of Michigan Transportation Research Institute, Eby, D.W., L.P. Kostyniuk, L.J. Molnar, H. Joksch, J.M. Vivoda, and L.M. Miller. The Effects of Standard Safety Belt Enforcement on Police Harassment: Year 2 Annual Report. Report No: UMTRI Ann Arbor, MI: University of Michigan Transportation Research Institute, Wagenaar, A.C., L.J. Molnar, and K.L. Businski. Direct Observation of Safety Belt Use in Michigan: December (report No. UMTRI-87-03). Ann Arbor, MI: The University of Michigan Transportation Research Institute, Barnes, G.M., and J.W. Welte. Predictors of driving while intoxicated among teenagers. Journal of Drug Issues, Vol. 18, 1988, pp Donovon, J.E. Young adult drinking driving: Behavioral and psychosocial correlates. Journal of Studies on Alcohol, Vol. 54, 1993, pp Evan, L., P. Wasielewski, and C.R. von Buseck. Compulsory seat belt usage and driver risk taking behavior. Human Factors, Vol. 24, 1982, pp Jessor, R. Risky driving and adolescent problem behavior: An extension of problem behavior theory. Alcohol, Drugs, and Driving, Vol. 3, 1987, pp

11 Eby, Kostyniuk, & Vivoda 10 LIST OF TABLES TABLE 1 Safety Belt Use Rates and Unweighted Ns for Hand-Held Cellular Phone Users and Nonusers and the Comparisons Between Rates by Several Categories TABLE 2 Estimates for Coefficients in Logistic Regression Model of Safety Belt Use

12 Eby, Kostyniuk, & Vivoda 11 TABLE 1 Safety Belt Use Rates and Unweighted Ns for Hand-Held Cellular Phone Users and Nonusers and the Comparisons Between Rates by Several Categories Difference Between Using Cellular Phone Not Using Cellular Phone Category Means Safety Belt Use Unweighted N Safety Belt Use Unweighted N SE d t Total 70.2 ± 4.8% ± 1.2% 22, # Sex Men Women Race White Black/AfAm Age up 65.6 ± ± 6.9 % 71.4 ± 4.8% 66.2 ± 10.6% 59.9 ± 15.4% 72.7± 5.9% 40.9 ± ± 1.5% 87.1 ± 1.4% 82.2 ± 1.4% 77.4 ± 1.9% 77.7 ± 3.7% 82.1 ±1.4% 83.1 ± 3.0% 13,278 9,442 18,143 3,985 1,986 14,676 1, # 2.08* 2.41* * Veh Type Passenger 72.1 ± 6.5% ± 1.2% 12, Roadway Freeway Local Time of Day 7-9 am 9-11 am 11-1 pm 70.0 ± 5.2% 72.2 ± 8.8% 68.7 ± 16.6% 59.3 ± 14.5% 68.4 ± 9.8% ± 1.3% 84.9 ± 1.8% 84.8 ± 3.5% 81.7 ± 3.1% 80.1 ± 2.4% 18,742 3,978 2,008 4,523 4, # 3.14* 2.02* * Women/Age ± 11.9% ± 2.3% 2, * Men/Age up 66.8 ± 7.8% 29.0 ± 43.5% * p <.05; p <.01; # p < ± 1.9% 80.1 ± 4.7% 8,778 1, *

13 Eby, Kostyniuk, & Vivoda 12 TABLE 2 Estimates for Coefficients in Logistic Regression Model of Safety Belt Use Variable Coefficient β Std. Error t Cellular phone # Female # Freeway # Black/African-American # Van # Sport-utility vehicle Pickup truck # Age # Age # Constant # Number of observations = 23,548 Number of strata = 12 Number of primary sampling units = 400 Population size = F(9,380) = Prob> F = p <.01; # p <.001