Environmental factors included type of seatbeltlaw as of April 2011 (primary enforcement, secondary enforcement, secondary enforcement with a primary provision for youth) ( GHSA, 2015a; IIHS, 2015a ), the April 2011 Insurance Institute for Highway Safety (IIHS) GDL rating (good, fair, marginal) (personal communication, Michele Fields, IIHS, July 9, 2013), prevalence of state-level adult self-reported seatbelt use (always wear) from the 2010 Behavioral Risk Factor Survey (BRFS) ( Shults & Beck, 2012 ), categorized into tertiles of 60 –79%, 80–85%, ≥86%), and rurality, measured as the proportion of students enrolled in public schools located in distant or remote areas ( Keaton, 2012 ), cat- egorized into tertiles of b10%, 10–19%, and ≥20%. Region was deﬁned using the four U.S. Census regions (West, South, Northeast, and Mid- west). Rhode Island enacted a primaryseatbeltlaw in June 2011, after the state's YRBS was conducted.
The estimation strategy consists of two steps. First, I observe each accident that occurred between 2003 and 2007. Since the year 2003, 13 states have changed their seatbelt laws from secondary to primary, so I will focus on the accidents that occurred in these states. Within a state, accidents occur either before (pre-accidents) or after (post-accidents) the enforcement date of the primaryseatbeltlaw. I test if there are behavioral differences among drivers who have accidents before and after the date. Each observation is a driver who is involved in an accident. It contains a variable, a zip-code, that shows the location of the driver’s home address 6 . It also shows when the accident occurred. The observation tells us whether or not the accident occurs under the primaryseatbeltlaw. Since accidents occur on different dates within a state as well as across the states, some occur before the primaryseatbelt laws are adopted, while others occur after them. I use drivers’ careless behavior to measure the behavioral difference before and after the enforcement date of the seatbelt laws. Each observation shows whether or not the driver was less careful at the time of crash. Careless behavior includes talking on, listening to, or dialing a phone; adjusting climate control, the radio, or a CD; using other devices integral to the vehicle; sleeping, eating or drinking; smoking related distractions; and other distractions or inattention 7 .
Second, even if the above relationship is statistically signiﬁcant, the relationship itself doesn’t provide anything meaningful because we are ﬁnding careless drivers among already less-careful drivers. Therefore, we should compare the same type of subsets, accidents data from seven primary and seven non-primary (neighboring) states. I select seven neighboring states that did not change the laws over the same period. I divide accidents again into two groups - pre-accidents and post-accidents - as if the neighboring states changed the seatbelt laws. For example, Mississippi is a primary state that changed the law from secondary enforcement to primary enforcement on May. 27, 2006, so all accidents that occurred before this date are pre-accidents, and accidents that occurred after the date are post- accidents. I choose Louisiana as one of Mississippi’s neighboring states. Louisiana adopted its primaryseat-beltlaw in 1995, so there was no law change between 2004 and 2008. However, I grouped Louisiana accidents into pre- and post-accidents. If accidents occurred before May. 27, 2006, then they are pre- accidents; others are post-accidents. If the drivers in these two states showed the same behavioral pattern, then we could conclude that the laws failed to change people’s behavior. In addition to these seven neighboring states, I also randomly choose another seven states 11 . These states are assigned to 9 Some may argue that the GES data is not reliable because the deﬁnition of less careful behavior may diﬀer
driver), depression, and lower levels of academic achievement (Reisner et al., 2013).
Strong evidence indicates that seatbelt laws are among the most important interventions in increasing safety belt use. Seatbelt laws have been enacted by states since 1984 and vary in the nature of their provisions, with some allowing enforcement officers to make a traffic stop based only on the non-use of a seatbelt (known as primary enforcement laws), while others only allow officers to note the violation of non-seatbelt use if they have already pulled the driver over for a different infraction (such as failing to use a signal). These jurisdictions have what is known as secondary enforcement laws. Previous research has shown that primary safety belt laws are associated with higher safety belt use and lower crash-related injuries and mortality in the general population as compared with secondary laws (Adkins, 2014). Because some teenage populations have lower safety belt use, even with primary enforcement laws, combined approaches that include upgrades to laws with campaigns and increased enforcement might be warranted. In addition, evidence indicates that primary enforcement safety belt laws may play a key role in mitigating the disparity in safety belt use among certain teen groups. As of March 2012, only 17 US states still have secondary safety belt laws in effect, and New Hampshire still has no safety beltlaw at all (García- España, Winston & Durban, 2012).
The seatbelt usage rate in Kansas is still low and in fact is lower than the national average rate of 71%. In comparison with other states, Kansas is among the states with the lowest seatbelt usage (NHTSA 1997a). This can be mainly attributed to secondary enforcement laws. For some years now, the Kansas Department of Transportation has been sponsoring an education campaign through the media popularly known as the “Kansas Click” campaign. As other earlier studies have noted, a primary enforcement law in conjunction with an educational cam- paign is required for high seatbelt usage. Tough fines for offending motorists are also effective. The Kansas law allows a $10 fine to be issued to offending motorists for not using seat belts (NHTSA 2001); this fine is one of the lowest among the states and it is not effective (NHTSA 2000; NHTSA 2001). Driver seatbelt compliance has been higher in urban areas than rural areas. Most of the trends shown by the results of this study agree with the trends shown by similar stud- ies conducted by NHTSA.
DOI: 10.4236/ajor.2018.81002 18 American Journal of Operations Research The sampling methodology used to collect data has a two stage design asso- ciated with primary sampling unit (PSU) strata from 15 counties and secondary sampling units (SSU) from 136 road segments within the counties, under Na- tional Highway Transportation Safety Authority (NHTSA) guidelines . If sampling weights are ignored, then the model parameter estimates can be biased . In fact, since the sample is collected from a two stage stratified sampling de- sign, standard underlying assumptions of parametric statistical models may be violated, and guidelines based on the statistical design cannot be ignored.   and  have given suggestions for such complex methodologies. Other authors have applied the methodology to studies. Our intent is to apply the seatbelt sampling methodology to predict the seatbelt usage.   and  have used such methodologies and they concluded that females are more likely to wear seatbelts than males. The relationship between vehicle type and seatbelt use has been explored by   and  who concluded that seatbelt use in pickup trucks is lower than other passenger vehicles.  suggested that passenger and driver use are related.  asserts that the seatbelt use is increased in those states within the United States that have primary seatbelt enforcement laws and ac- tively enforce seatbelt use. Studies have also explored relationships between race, socio-economic status, age, rural/urban environments, law enforcement type (primary, secondary), the amount of fines, and the type of road traveled (prima- ry, secondary, tertiary).  employed a multivariate approach using the afore- mentioned factors along with cultural variables to explain the differences in seatbelt use between states using self-reported information, direct observation, and crash reports. However, the validity of self-reported seatbelt use in surveys is questionable compared to observed seatbelt usage . While the methodology is simple to describe, the challenge is found in the statistical analysis tool used to make prediction, especially in the presence of behavioral variables, such as driver gender, vehicle type, traffic volume, road segment length, weather conditions, driver cellphone use, passenger presence, lane, and passenger seatbelt use. The goal is to get meaningful information that can be translated into quantitative measures.  and  propose the addition of a score variable due to the mea- surement of concern. Those researchers have incorporated latent traits of data in a score function.
We develop a methodology to use FARS data as an alternative to NOPUS in estimating seatbelt usage. The advantages of using FARS over NOPUS are that (i) FARS is broader because it contains more variables relevant for policy analysis, (ii) FARS allows for easy multivariate regression analysis, and, finally, (iii) FARS data is more cost-effective. Methodology: We apply a binary logit model in our analysis to determine the likelihood of seatbelt usage given various occupant, vehicle, and built environment characteristics. Using FARS data, we derive coefficient estimates for categories such as vehicle occupants' age and night time seatbelt use that observational surveys like NOPUS cannot easily provide. Results: Our results indicate that policies should focus on passengers (as opposed to drivers), male and young vehicle occupants, and that law enforcement should focus on pick-up trucks, rural roads, and nights. We find evidence that primaryseatbelt laws are effective. Conclusions: Although this is primarily a methodological paper, we present and discuss our results in the context of public policy so that our findings are relevant for road safety practitioners, researchers, and policymakers.
CHAPTER 5 - SUMMARY AND CONCLUSIONS
This study developed a procedure to estimate potential economic benefits associated with increased seatbelt usage. A two-phased procedure was utilized. In the first phase, seatbelt effectiveness in reducing injuries to motor vehicle occupants was estimated using different methods available. These methods included multiple logistic regression, double pair comparison method, Cox proportional hazards regression, conditional logistic regression, and risk ratio model using estimating equation approach. Results from each method were evaluated to identify strengths and limitations. Crash data from Kansas Accident Reporting System (KARS) database was used, and the estimations were based on KABCO injury scale. Two vehicle groups were considered: passenger cars, and other passenger vehicles, which included pickup trucks and vans. Only front seat occupants who were older than 14 years of age were considered in the analysis. In the second phase of the study, the potential economic benefits that could be expected due to increased seatbelt usage by motorists were estimated. The estimated seatbelt effectiveness values using logistic regression method were used to estimate potential injury reductions and those injury reductions were converted into economic values by using costs associated with each injury severity level. The injury costs used in this study were the FHWA recommended costs based on national data.
To ensure whether the seatbelt is worn by the driver or not seatbelt checker is used. It is made using IR sensor. It has two parts IRemitter and IR receptor. IR emitter consists of IR led and a series resistance which emits infrared light and is fixed to the end of the belt. The infrared receptor is placed at the other end that is where the belt is inserted and locked. The IR receiver is made of a photodiode. When the seatbelt is fastened photodiode will receive the infrared light and sends a positive signal to the processor. Only after the seatbelt is fastened and mobile is brought into the silent mode using Wi-Fi connectivity, the ignition is enabled. D. Working
This investigation demonstrated that passengers traveling in trucks also have low
compliance levels at 27.4%; however, current laws in Namibia allow six unrestrained passengers to travel in the backs of trucks legally. Rather than recommend amendments to Namibian law, the researchers proposed an alternative solution to target people that often travel unrestrained in trucks and taxis. Lack of public transportation in Windhoek leads to high taxi use and unsafe transportation of workers in overcrowded trucks, both of which contribute to low passenger compliance. As Windhoek’s working population grows, the Move Windhoek bus system has budding potential to provide a safe, accessible form of transportation. Because the current system operates on limited routes throughout the city and often runs behind schedule, many commuters do not choose to travel by bus as shown in Figure 58. Improving the public transportation system in Windhoek could encourage more workers to utilize the bus system as a commuting option rather than unsafely crowding the beds of trucks to travel to work quickly and cheaply. Though this issue is outside the scope of this project, the project team advises a future project to
et al. (1999) supports the conclusion that primary laws are more effective than secondary laws and that both have helped increase seat-belt usage.
While the usage of seat-belts has clearly been established as reducing the likelihood that traffic fatalities will occur (US DOT, 1996), there has been some suggestion that drivers may off-set the risk reduction through compensating behavior (Evans et al., 1982; Singh & Thayer, 1992). Evans & Graham (1991) developed a fixed-effects model across states to analyze whether seat-belt use decreases fatalities and found a positive significant effect, including some weak evidence of
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 seatbelt 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 seatbelt 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 seatbelt, and are more likely to support seatbelt 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 seatbelt 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.
Abstract- This paper clearly explains about the safety and control systems in the car . Most of the accidents are occurred because of violation of rules . Result of this major accidents happened .In our day-to-day life we are careless in our safety while driving in vehicles for this we have to introduce some techniques to do these precautions compulsory . Such a new technique is explained in this paper.While driving car wearing seatbelt is important that can safe our life during accident periods .But most of us are careless to wear seatbelt .
For the purposes of data analysis, the participants were categorized into three groups i.e. never use seat belts, sometimes use seat belts and always use seat belts when driving. Pearson Chi square test was done to test for statistically significant association between the socio- demographic variables, selected questions on seatbelt use and the three different categories of seatbelt use by the participating drivers. A multinomial logistic regres- sion analysis was done in order to determine the possi- ble predictors of seatbelt use by the participating drivers.
expansion provision of the 2010 ACA. The Oregon Health Insurance Experiment, for example, showed that there was a 40% increase in emergency department visits in covered individuals when compared to those eligible for Medicaid who were not covered (Finkelstein et al 2012, Taubman et al 2014). While this would appear to be a moral hazard effect, it is possible that this can be explained by the relative difficulty individuals have accessing primary care through Medicaid. Because physicians are willing to take on so few new Medicaid patients, this could be explained by many patients receiving care through emergency departments instead of through a primary care physician. The capitation paid by the government to providers, usually through managed care
Abstract- Major causes of death in road accidents are carelessness in safety while driving. In 2012, more than half of all people who died on Utah's roadways weren't buckled . Hence wearing seat belts might have reduced serious crash- related injuries and saved life.
School secretaries who fulfill the requirements in terms of qualification level and profile may continue to perform the activities in their position under the condition that they complete successfully the professional examination within two years from the adoption of the program specified in Article 27 of this Law.
Chi-square tests were used to examine the associations be- tween seatbelt use (yes, no) and demographic and other fac- tors (age, gender, race/ethnicity, gender, age, crash time, crash area, crash type, alcohol consumption, and driving speed) and a Tukey-type of multiple comparison procedure for post hoc analysis; then chi-square tests were used to examine the as- sociations between seatbelt use (yes, no) and injury proﬁles of body regions and nature of injury. The mean and 95% conﬁdence interval (CI) of the charge and MAIS value were used to estimate of medical costs and severity of injuries. Ad- ditionally, we performed logistic regression to evaluate the association between seatbelt use and body region and na- ture of injury and calculated the odds ratios (ORs) and 95% CIs. In addition to seatbelt use, some other factors, such as driver’s gender, age, alcohol consumption, ejected or trapped condition, crash time, crash area, type, and speed may have an impact on the body region and nature of injury. To remove possible confounding bias between seatbelt use and injuries of body region and nature of injuries, we performed these logistic regression models after adjusting for these potential contribu- tors to the risk of injury; gender (male and female), age group (15–19, 20–29, 30–39, 40–49, 50–59, and 60 and over), crash time (12 a.m.–5:59 a.m., 6 a.m.–11:59 a.m., 12 p.m.–5:59 p.m., and 6:00 p.m.–11:59 p.m.), crash area (urban and rural), crash type (multiple vehicles, single vehicle), alcohol consumption (yes and no), drive speed (slower <50 mph and faster ≥50 mph), and ejected or trapped during crash (yes and no). SAS software, version 9.2, was used for all data analysis (SAS Insti- tute Inc., Cary, NC). A 2-sided P value < .05 was considered to be statistically signiﬁcant.