Abstract
A better understanding of attitudes and behavioral principles underlying driving behavior and traffic safety issues can contribute to design and policy solutions, such as, speed limits and seat belt legislation. This work examines the Motor Vehicle Occupant Safety Surveys (MVOSS) data set to illuminate drivers' seatbelt use, driving speed choices, drinking-and-driving tendencies, along with their attitudes towards speed limits and seat belt laws. Ordered probit, negative binomial, and linear regression models were used for the data analysis, and several interesting results emerged. For example, persons of higher income and with a college education prefer higher speeds, are more likely to use a seat belt, and are more likely to support seat belt laws and/or higher speed limits. However, persons with a college education also tend to drink and drive more often. Pickup drivers are less likely to use seat belts, less likely to support seat belt laws, yet less likely to drink and drive. The number and variety of results feasible with this single data set are instructive as well as intriguing.
INTRODUCTION
Many traffic safety issues have been investigated using crash data such as the General Estimates System (GES), Fatality Analysis Reporting System (FARS), Highway Safety Information System (HSIS), and local police crash records. Conclusions across studies on the impact of speed limit changes (e.g., Chang and Paniati 1990 , Wagenaar et al. 1990 , and Ledolter and Chan 1996 and the impact of speed variation on traffic safety (e.g., Lave 1985 , Levy and Asch 1989 , Davis 2002 , and Kockelman et al. 2006 are not definitive, even with these sophisticated and large datasets. One possible explanation of this discrepancy among studies on the same topic is lack of understanding of road user behaviors and attitudes.
INTRODUCTION
The National Highway Traffic Safety Administration (NHTSA) has conducted the Motor Vehicle Occupant Safety Surveys (MVOSS) biannually since 1994 by telephone interview. The 2000 MVOSS data include information on driver attitudes towards safety issues (e.g., attitudes towards the current speed limit), driver behaviors (e.g., speed choice and driving frequency), and crash history, as well as on individual and household characteristics (Boyle and Schulman 2001) . These are analyzed here.
INTRODUCTION
A better understanding of behavioral principles and circumstances that underlie driving behavior and driver attitudes can enhance various traffic safety policies, including speed limit selection, seat belt legislation, and drunk-driving campaigns. In this regard, the MVOSS data set provides many useful pieces of information for investigation. Knowing who the supporters and opponents of traffic safety-related policies are can be very helpful in crafting and promoting such policies, such as defining target groups for anti-speeding campaigns and driver training programs.
INTRODUCTION
This study investigates several interesting issues relating to these variables. Through these investigations, the study aims to provide behavioral and psychological insights into the U.S. driving population. What follows here is a literature review, model and descriptions, a discussion of results, and conclusions.
LITERATURE REVIEW
A few studies have addressed certain driving behaviors and attitudes using a series of crosssectional surveys in the U.S. commissioned by Prevention Magazine. Schechtman et al. (1999) attempted to relate drinking habits (frequency and amount) to seat belt use, speed limit obedience, and drunk driving over 11 years. They found no evidence to link drinking habits with seat belt use and speed limit obedience. However, evidence indicated links between frequency and amount of drinking with drunk driving, as expected. Shinar et al. (1999) used the same datasets as Schechtman et al. (1999) to examine trends in driving behaviors and health maintenance behaviors. They found that the rate of seat belt use increased from 41.5% in 1985 to 74.1% in 1995, along with a slight reduction in drunk driving. The investigators also noted a weak relation between driving behaviors and health maintenance behaviors. Shinar et al. (2001) used more recent Prevention Magazine survey data to investigate associations between seat belt use, speed limit observance, drunk driving, and four demographic characteristics (gender, age, education and income) . Their four-way ANOVA models using 1994-1995 data indicated that females reported more law obedience than males in all behavioral categories. Rates of seat belt use increased with age and education level for both males and females. Interestingly, higher education and income levels were associated with speeding. One may argue that this is due to higher values of travel time and driving newer vehicles with better safety features, in many cases. Koushki et al. (1998) reported that Kuwaiti drivers in the same age group, who did not wear seat belts, violated traffic regulations more than twice as often as those who wore seat belts. They also found that seat belt non-users were mostly young and female among Kuwaitis, and their driving behaviors frequently involved changing lanes without signaling and changing travel speed. Their findings confirm that drivers who are reluctant to wear a seat belt tend to be more dangerous drivers and/or take more risks, in general. Regarding crash injury severity, Kim et al. (1995) found seat belt use among Hawaiians contributed significantly to injury reductions and crash survival. He also argued that discouraging alcohol use placed drivers at less risk.
LITERATURE REVIEW
Speed choice also has been investigated. Haglund and Aberg (2000) examined drivers' attitudes towards speeding and the influence of other drivers on speed choices. Data were collected on Swedish highways, with a speed limit of 90 kilometers per hour (km/h) (56 mph). They concluded that drivers' decisions regarding speeding are highly correlated with their view of other drivers' behaviors. Drivers usually overestimated the fraction of high-speed drivers (i.e., those traveling at least 10 km/h over the speed limit); their estimates averaged 50.7%, while the observed percentage was 22.9%. Furthermore, high-speed drivers believed that a high proportion (58%) of other drivers also qualified as high-speed drivers, indicating a false impression of speed consensus.
LITERATURE REVIEW
Driving speeds are influenced by various factors, including roadway geometry, driver attitudes and environmental factors (e.g., weather and enforcement). Kanellaidis et al. (1990) studied passenger car speeds on horizontal curves of two-lane rural roads in Greece. A total of 207 Greek drivers rated the impact of 14 elements of the road's environment (e.g., sight distance, pavement condition, and lane width) on their choice of speed. Drivers who tended to violate speed limits rated all types of signage (e.g., warning signs) significantly lower than speed limit observers. Speed limit offenders also paid less attention to roadway design. Liang et al. (1998) found considerable reductions in mean speed and significant increases in speed variance under foggy and snowy conditions on Interstate 84 in Idaho, while Edwards (1999) only reported small reductions in both mean and variance under rainy and foggy conditions on the M4 Motorway in the U.K. Vaa (1997) found statistically significant and somewhat large reductions in average speeds and fraction of speeders due to increased police enforcement on Norway highways. Kockelman et al. (2006) found average speed increases in cross-section to be double those in before/after studies of speed-limit increases, and modeled optimal speed choices as a trade-off of crash, speed limit violation, and delay costs. They also found instantaneous speed variations (across individual vehicles) to hardly depend on speed limits and roadway design attributes, and they concluded that higher speed limits have their greatest effect on crash outcomes, in terms of injury severity.
LITERATURE REVIEW
Many behaviors are recorded as discrete responses (e.g., yes/no) in data sets. Discrete-response models are now common in assessing crash results. For example, Kockelman and Kweon (2002) applied an ordered probit model for prediction of driver injury severity using the 1998 GES data and developed separate models for single-vehicle, two-vehicle and multi-vehicle crashes. As expected, higher travel speeds were predicted to significantly increase injury severity. Females and older persons were also predicted to be at greater risk for severe injury, if they experience a crash as a driver. Their results are similar to those of O'Donnell and Connor (1996) , who used Australian crash records and ordered logit and probit models. They found that driving light-duty trucks at high speeds, not wearing seat belts, and head-on collisions all increased the likelihood of severe injury and fatality. Cooper (1997) used binary logit models to investigate the relationship between various violation convictions (e.g., exceeding the speed limit and disobeying signals) and crash involvement based on data for British Columbia, Canada. In order to reduce serious and fatal crashes, he concluded that the focus should be on excessive speeders (40 km/h or more over the speed limit). Simply exceeding the speed limit, while statistically significant, was not a primary predictor of increased risk of serious injury. Many others have modeled crash counts (e.g., Miaou 1994 , Kim et al. 1995 Gebers 1998, and Ivan et al. 1999 ) using Poisson and negative binomial models. The negative binomial model is typically more appropriate than the Poisson, since it allows for unobserved heterogeneity while permitting over-dispersion in the data (rather than requiring that variance equal mean). Thus, it was used here to examine the frequency of drinking and driving.
MODELS
Three different model specifications were used. Brief general descriptions of two modelsordered probit and negative binomial models -are provided here. Standard ordinary least squares (OLS) regression also was performed, but is not described here.
Ordered Probit Model
In an ordered probit model, the focus is on the probability of one of many possible, ordered responses: (1) 
Ordered Probit Model
For example, i y = 0 if an individual i thinks current speed limits are too low, 1 if these are about right, and 2 if they are felt to be too high. Here, J = 3. The associated probabilities are as follows:
Ordered Probit Model
where ) (⋅ Φ denotes the standard normal cumulative distribution function. The product of these probabilities is the likelihood function, which assumes independent responses across individuals in the sample:
Negative Binomial Model
Count data are non-negative, integer values. These characteristics often render linear regression models inappropriate, while making Poisson models a popular alternative (with an exponential function of explanatory variables for the rate term, λ).
Negative Binomial Model
Poisson models do not allow for unobserved heterogeneity and presume equidispersion (such that mean equals variance). A negative binomial model adds a random disturbance ( i ε ) to the rate function of the Poisson model as follows: (4)
Negative Binomial Model
Assumption of a gamma distribution for i δ results in a negative binomial probability mass function (PMF), as follows: ; so that α is the distribution's over-dispersion parameter.
Negative Binomial Model
In cases where α =0, the negative binomial reduces to a Poisson distribution. As an example application of this model based on the MVOSS data, one can analyze the number of days that a respondent reports having imbibed alcohol and driven in the past 30 days. Readers may consult Cameron and Trivedi (1986) for details on the negative binomial model.
DATA
The 2000 Motor Vehicle Occupant Safety Survey (MVOSS) data were collected between November 2000 and January 2001. Data were obtained from 6,072 respondents, age 16 or older, residing in all 50 U.S. states and Washington, D.C. The survey emphasized traffic safety issues, including driving frequency, seat belt use, and driving attitudes. Basic variable details are shown in Table 1 . Due to non-response on certain questions, the sample sizes in the final analyses vary from 4,057 to 4,137, depending on which explanatory variables were used. Household income, originally a categorical variable, was made continuous by using approximate median values in each category. Pressure to Speed 0 = never feels pressure to exceed the speed limit (18.35%); 1 = rarely feel pressure to exceed the speed limit (30.32%); 2 = often feel pressure often to exceed the speed limit (34.91%); 3 = very often feel pressure to exceed the speed limit (16.41%) Number of times having been injured in a crash 0.4444 a An "injury" in the MVOSS data set is defined as one that requires medical attention.
DATA
Among the variables in the MVOSS data relating to traffic safety, those that merit special examination are seat belt usage, response to speed limits and seat belt laws, preferred driving speed, and drinking and driving. The relationship between these variables and a set of explanatory variables -including traffic crash history, individual characteristics (e.g., age and education level), recent drinking habits (e.g., drinking frequency and amount), vehicle type, and employment -was investigated. Separate analyses were carried out for each variable of interest, using a discrete choice model (ordered probit), a count data model (negative binomial), and a linear regression model (for speed choice).
DATA
It should be mentioned that several of these MVOSS variables involve stated preferences (e.g., support for seat belt laws) and sensitive stated behaviors (e.g., drinking days per month and speed choice). Respondents may not know their true response or may choose to "color" their response to hide the truth. (Readers may be interested in Corbett's (2001) and Bradburn and Sudman's (1979) discussions of these issues, as well as survey design.) Such tendencies certainly can bias results (e.g., biasing estimates of drinking and driving to the low side and support for seat belt laws to the high side). For example, 82.5% of MVOSS respondents reported using the shoulder belt all of the time, and 9.5% reported using their belt most of the time. In contrast, the National Occupant Protection Use Survey (NOPUS) data, collected at 2,063 sites in October and November of 2000, suggest that only 55% to 74% of adults (across different vehicle types) wear shoulder belts (NHTSA 2001) . Therefore, there is probably some over-reporting of seat belt use in the MVOSS data.
RESULTS AND DISCUSSIONS
Model outputs are provided in Tables 2 through 6. In each of these, an initial model including all possible explanatory variables of interest, as shown in Table 1 was estimated, and a final model was then developed, to recognize only those variables that remained statistically significant at the 10% level (p-value ≤ 0.10), after a series of step-wise deletions. Only the final model outputs are presented here. Ordered probit and negative binomial models were estimated using maximum likelihood (ML) estimation techniques, and a linear model was estimated using OLS.
RESULTS AND DISCUSSIONS
Along with estimates of coefficients (and thresholds for an ordered probit model), a likelihood ratio index (LRI) or McFadden's pseudo R 2 , is provided for MLE models, which represents the ratio of likelihood values of models estimated with and without explanatory variables.
RESULTS AND DISCUSSIONS
1 All model estimations were performed using LIMDEP 7.0.
Perceptions of Current Speed Limits
A total of 76.6% of the survey respondents reported being "satisfied" with current speed limits, 16.2 % felt they were too low, and 7.2% thought they were too high. As a point of comparison, Haglund and Aberg (2000) reported figures of 61.1%, 37.0%, and 1.9%, respectively, for Swedes on highways with a 90 km/h (56 mph) speed limit. Of course, the MVOSS survey of American drivers asked a more general question, and U.S. freeway speeds are often above 100 km/h. Thus, it is not unusual to expect that Americans may be less likely to want higher limits than their Swedish counterparts.
Perceptions of Current Speed Limits
The perceptions on the current speed limits were analyzed using the ordered probit model, and Table 2 shows these results. Positive signs on estimated coefficients suggest that respondents are more likely to consider current speed limits to be too high, and thus favor lowered limits.
Perceptions of Current Speed Limits
Conversely, negative signs on coefficients indicate that respondents are less likely to consider current speed limits to be too high, and thus favor higher limits.
Perceptions of Current Speed Limits
According to Table 2 , males, employed, married, and higher-income drivers tend to favor higher speed limits, in contrast to drivers of light-duty-trucks including pickup trucks, vans and sportutility vehicles (SUVs) that favor lower speed limits. People who favor seat belt laws, those who are pressured to speed up by other drivers, and those who have experienced injury crashes as drivers tend to support lowering speed limits. In contrast, less frequent seatbelt users, those who frequently pass others on the highway (the reference group for all passing responses), those who have recently been stopped by a police officer, those who drink more often, and/or those who indicated higher driving speeds tend to favor speed limit increases τ and β are provided in Table 2 . Then, the values for age, indicators for gender and vehicle types were varied. Figures 3 through 6 were created in a similar way.
Perceptions of Current Speed Limits
Older persons are predicted to respond that current speed limits are too high; however, this trend tops out at about age 80. The gender effect is much greater than the vehicle-type effect: females are more likely to consider current speed limits to be too high, regardless of the vehicle types that they use. As alluded to above, van drivers are estimated to be most likely to favor lowered limits, SUV drivers follow, pickup truck drivers are next, and passenger car drivers (the reference group for the vehicle type indicator variables) are least likely to support lowered speed limits. These are findings for the reference person who is married and employed and has average conditions for all other factors (e.g., has drunk alcohol on 3.6 "occasions" [days] during the past month). Table 2 . (2) The curves for females driving passenger cars and males driving SUVs are too close to one another to distinguish visually; thus, they are presented as a single curve. Table 3 and Figure 3 show the OLS model results for predictions of driver speed choices on highways. Respondents' reports of their usual highway speeds tend to increase with their household income, alcohol consumption (amount and frequency), and recent traffic violations. Male, younger, college-educated persons, frequent drivers, those living in central cities, and those who have been recently stopped or cited by police also tend to prefer higher speeds. Based on this finding, public anti-speeding campaigns should target such drivers. Those who are older, employed, Hispanic, drive SUVs, and regard others as "good" drivers prefer lower speeds. The potential reason why drivers with higher household income tend to drive faster and support higher speed limits might be that they value their travel time more highly -and drive more expensive vehicles with better acceleration and safety features. Table 3 . (2) $50k denotes an annual household income of $50,000.
Speed Choices on Highways

Seat Belt Use
Wearing a seat belt plays a crucial role in diminishing the severity of crashes for vehicle occupants (Kim et al. 1995 , NHTSA 2004 , and Shults et al. 2004 . Table 4 displays the results of an ordered probit model for frequency of seat belt use while driving. The positive signs of the coefficients are interpreted in the same way as in Table 2 : as the associated explanatory variable's value increases, respondents are more likely to wear their seat belts more often. A negative sign means that as the particular independent variable increases, respondents are less likely to wear their seat belts more often. Figure 4 illustrates how model estimates of individuals' probabilities of responding that they "always wear their seat belt" vary with age, gender, and vehicle type (pickup truck, heavy-duty truck, and any other vehicle).
Seat Belt Use
Using an ordered probit model for driver-reported frequency of seat belt use, greater belt use is expected to occur among married, college-educated women, and having higher household incomes (as well as among those who favor a seat belt law). However, as shown in Figure 4 , the effect of certain vehicle types was estimated to dominate that of gender. Thus, male passenger car drivers are estimated to be more likely to always use their seatbelts than pickup-driving females (when they are married and college-educated). In addition to males and pickup or heavy-truck drivers, more frequent drivers, those recently stopped by police, those more likely to pass other vehicles (the reference group for passing responses), more frequent imbibers of alcohol, and those drinking and driving more frequently are estimated to be less likely to wear a seat belt (Table 4) .
Seat Belt Use
However, those having received a traffic violation in the past 12 months along with those who favor higher speed limits, and have been injured as a driver are more likely to use seat belts often. In general, Shinar et al.'s (2001) seatbelt-use findings relating to education levels are consistent with ours; however, they estimate that the positive income effect applies only to females using Prevention Magazine's survey data from 1983 through 1995. 
Support for Seat Belt Laws
Respondents' support for seat belt laws also was estimated via an ordered probit specification, and the results are presented in Table 5 and Figure 5 . The positive signs of the coefficients in Table 5 are interpreted in the same way as in Tables 2 and 3: as the values of the variables increase, respondents are more likely to support seat belt laws. According to Table 5 , males, pickup, SUV and heavy truck drivers, those with less income and/or education, those who drive and/or use a seat belt less frequently, and those with recent traffic violations are less likely to support a seat belt law. Those who prefer higher speed limits, choose higher speeds and/or drink more are predicted to be less likely to favor a seat belt law, while married persons, Hispanic, those who view others as "good drivers", those who reside in central cities, and those who feel pressured to speed up by other drivers showed more support for such a law. 
Support for Seat Belt Laws
, where k is the number of estimated parameters.
Support for Seat Belt Laws
As illustrated in Figure 5 , the gender effect exceeds the vehicle-type effect, and females are predicted to be more likely to favor seat belt laws, irrespective of vehicle type. In addition, support for seat belt laws varies in a convex way with age: declining with age up to age 50, and then increasing. Table 5 's model
Drinking and Driving
Drinking and driving (during the past 30 days) was examined using a negative binomial regression model, and the results are displayed in Table 6 and Figure 6 . Positive signs of coefficients in Table 6 suggest that, as the values of the associated explanatory variables increase, respondents are more likely to have imbibed alcohol and then driven in the past 30 days. Negative signs have the opposite interpretation. In estimating the number of days one had been recently drinking and driving, it was found that the number of drinks per event had almost twice the effect of the number of drinking days in the past month. Male, employed persons, collegeeducated persons, and those recently stopped by police reported more drinking and driving. More frequent driving was associated with more drinking and driving, as one may expect. Married people and those who more often wear seat belts were less likely to drink and drive. Those who drive pickups or heavy trucks were less likely to drink and drive than those driving other types of vehicles. Figure 6 presents the effects of age, gender and vehicle types on the number of drinking and driving days in the past 30 days. Males and those driving non-pickup trucks including passenger cars, SUVs, and vans are more likely to drink and drive with higher frequency than females and those driving pickup trucks. However, the gender effect is much greater than the vehicle-type effect. As a driver ages, the number of drinking and driving days tends to increase, until around age 65.
CONCLUSIONS
This work relies largely on discrete-response (ordered probit) models and count data (negative binomial) models for analysis of the MVOSS. A standard linear regression model also was used to estimate usual highway speed choice. Dependent variables included seat belt use, frequency of drinking and driving, attitudes toward speed limits and a seat belt law, and speed choices on highways in the U.S.
CONCLUSIONS
There are a multitude of results available from this work. For example, males are less likely to use a seat belt and favor seat belt laws, but more likely to favor raised speed limits, to drive faster on highways, and to drive after drinking. In general, males are found to exhibit riskier behaviors and less favorable attitudes towards safety policies than females.
CONCLUSIONS
Younger persons tend to prefer higher speed limits and choose higher driving speeds on highways. Persons around age 50 are estimated to be among those least likely to support seat belt laws; and those near 85 years of age are most likely to consider speed limits to be too high, and therefore are most likely to support a reduction of speed limits. Interestingly, there is relatively little spread in reported speed preferences: the "average" respondent at the age of 20 prefers to travel 70 mph on highways, but 67 mph at the age of 75.
CONCLUSIONS
With regard to vehicle types, those who drive pickups and heavy trucks are less likely to use a seat belt, less likely to favor seat belt laws, and less likely to drive after drinking. Drivers of vans, SUVs, and pickups are more likely to support lowering the speed limits than passenger car drivers.
CONCLUSIONS
Higher household incomes and educational attainment increase the predicted probabilities of seat belt use and one's support of seat belt laws; however, higher-income drivers tend to support higher speed limits, and those with a college education appear to drink and drive more often. High income and college-educated drivers may value their lives and time more (and drive more expensive vehicles with more safety features and better acceleration performance), but the college educated also appear to value their alcohol consumption more (thus driving after drinking).
CONCLUSIONS
This summary relates just a few of the results quantified in the model outputs presented here. It is hoped that these will be useful to policymakers and traffic engineers in the domain of traffic safety for the traveling public. Anticipating public reaction to drunk-driving campaigns, speed limits, and seat belt regulations is important and useful. For example, efforts to reduce drunk driving may find it useful to focus on drivers who are young, male, employed, and collegeeducated (or about to become college educated). To boost seat belt usage rates, it may be more effective to target males driving pickups and heavy-duty trucks. Moreover, policymakers and public information officers in states seeking to enact more stringent seat belt laws (e.g., shifting from secondary to primary enforcement laws) 2 may do well to seek support among those who are middle-aged, male, without a college degree, of lower income, and driving pickups, SUVs, or trucks. Targeted messages on safe driving practices can make a difference -saving lives, time, money and other resources. An understanding of human behavior on the roadway is a valuable step in this direction.

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