Age-based variability in the association between restraint use and injury type and severity in multi-occupant crashes.

PURPOSE: Previous studies have shown older adults receive relatively less protection from seat belts against fatal injuries, however it is unknown how seat belt protection against severe and torso injury changes with age. We estimated age-based variability in seat belt protection against fatal injuries, injuries with maximum abbreviated injury scale greater than two (MAIS 3+), and torso injuries. METHODS: We leveraged the Crash Outcome Data Evaluation System to analyze binary indicators of fatal, MAIS 3+, and torso injuries. Using a matched cohort design and conditional Poisson regression, we estimated age-based relative risks (RR) of the outcomes associated with seat belt use. RESULTS: Our results suggested that seat belts were highly protective against fatal injuries for all ages. For ages 16-30, seat belt use was associated with 66% lower risk of MAIS3+ injury (RR 0.34, 95% CI 0.30, 0.38) for occupants of the same vehicle, whereas for ages 75 and older, seat belt use was associated with 38% lower risk of MAIS3+ injury (RR 0.62; 95% CI 0.45, 0.86) for occupants in the same vehicle. The association between restraint use and torso injury also attenuated with age. CONCLUSIONS: In multi-occupant crashes, seat belts were highly protective against fatal and MAIS3+ injury, however seat belt protection against MAIS3+ and torso injury attenuated with age.

Socioeconomic and Demographic Factors Effect in Association with Driver's Medical Services after Crashes.

Motor vehicle crashes are the third leading cause of preventable-injury deaths in the United States. Previous research has found links between the socioeconomic characteristics of driver residence zip codes and crash frequencies. The objective of the study is to extend earlier work by investigating whether the socioeconomic characteristics of a driver's residence zip code influence their likelihood of resulting in post-crash medical services. Data were drawn from General Use Model (GUM) data for police crash reports linked to hospital records in Kentucky, Utah, and Ohio. Zip-code-level socioeconomic data from the American Community Survey were also incorporated into analyses. Logistic regression models were developed for each state and showed that the socioeconomic variables such as educational attainment, median housing value, gender, and age have p-values < 0.001 when tested against the odds of seeking post-crash medical services. Models for Kentucky and Utah also include the employment-to-population ratio. The results show that in addition to age and gender, educational attainment, median housing value and rurality percentage at the zip code level are associated with the likelihood of a driver seeking follow-up medical services after a crash. It is concluded that drivers from areas with lower household income and lower educational attainment are more likely to seek post-crash medical services, primarily in emergency departments. Female drivers are also more likely to seek post-crash medical services.

Exploring mobility pattern changes between before, during and after COVID-19 lockdown periods for young adults.

The paper aims to investigate changes in travel behavior due to COVID-19 focusing in one of the most active social groups in Greece. A questionnaire survey was conducted and 306 young adults (age 18-34 years) living in various Greek cities responded. The survey collected information about travel-related preferences before, during and after the 1st lockdown and during the 2nd lockdown of the COVID-19 pandemic in Greece. City attributes of the respondent's residency location before and after the 1st lockdown were collected. The data are analyzed descriptively and through statistical modelling techniques. During the 1st lockdown an important increase in physical exercise frequency was observed, but this increase was not permanent. The COVID-19 pandemic resulted in essential reductions in the frequency of public transport use and in an increase of walking frequency. The public transport use reduction was mainly attributed to people that had access to a private car and after the 1st lockdown moved to a smaller city. On the other hand, the changes in walking frequency are closely linked to the city's attributes. Useful policy implications are being derived about how the pandemic can assist in promoting sustainable urban mobility goals.

Identifying high-risk commercial vehicle drivers using sociodemographic characteristics.

Crash data, from the state of Kentucky, for the 2015-2016 period, show that per capita crash rates and increases in crash-related fatalities were higher than the national average. In an effort to explain why the U.S. Southeast experiences higher crash rates than other regions of the country, previous research has argued the regions unique socioeconomic conditions provide a compelling explanation. Taking this observation as a starting point, this study examines the relationship between highway safety and socioeconomic and demographic characteristics, using an extensive crash dataset from Kentucky. Its focus is single- and two-unit crashes that involve commercial motor vehicles (CMVs) and automobiles. Using binary logistic regression and the quasi-induced exposure technique to analyze data on the socioeconomic and demographic attributes of the zip codes in which drivers reside, factors are identified which can serve as indicators of crash occurrence. Variables such as income, education level, poverty level, employment, age, gender, and rurality of the driver's zip code influence the likelihood of a driver being at fault in a crash. Socioeconomic factors exert a similar influence on CMV and automobile crashes, irrespective of the number of vehicles involved. Research findings can be used to identify groups of drivers most likely to be involved in crashes and develop targeted and efficient safety programs.

Use of codes data to improve estimates of at-fault risk for elderly drivers.

The fastest-growing demographic in the United States is people aged 65 and over. Because elderly drivers may experience decline in the physical and mental faculties required for driving (which could lead to unsafe driving behaviors), it is critical to determine whether elderly drivers are more likely than younger drivers to be at fault in a crash. This study uses Kentucky crash data and linked hospital and emergency department records to evaluate whether linked data can more accurately estimate the crash propensity of elderly drivers to be at-fault in injury crashes. The Kentucky crash data is edited to conform to the General Use Model (GUM), with crash propensities for linked data compared to propensities developed using the GUM dataset alone. The quasi-induced exposure method is used to determine crash exposure. Factors such as age, gender, and crash location are explored to assess their influence on the risk of a driver being at fault in an injury crash. The overall findings are consistent with previous research - elderly drivers are more likely than younger drivers to be at fault in a crash. Linking crash with hospital and emergency department records could also establish a clearer understanding of the injury crash propensity of all age groups. Equipped with this knowledge, transportation practitioners can design more targeted and effective countermeasures and safety programs to improve the safety of all motorists.

Validation of not-at-fault driver representativeness assumption for quasi-induced exposure using U.S. national traffic databases.

INTRODUCTION: The quasi-induced exposure (QIE) method has been widely implemented into traffic safety research. One of the key assumptions of QIE method is that not-at-fault drivers represent the driving population at the time of a crash. Recent studies have validated the QIE representative assumption using not-at-fault drivers from three-or-more vehicle crashes (excluding the first not-at-fault drivers; D3_other) as the reference group in single state crash databases. However, it is unclear if the QIE representativeness assumption is valid on a national scale and is a representative sample of driving population in the United States. The aims of this study were to assess the QIE representativeness assumption on a national scale and to evaluate if D3_other could serve as a representative sample of the U.S. driving population. METHOD: Using the Fatality Analysis Reporting System (FARS) and the National Occupant Protection Use Survey (NOPUS), distributions of driver gender, age, vehicle type, time, and roadway type among the not-at-fault drivers in clean two-vehicle crashes, the first not-at-fault drivers in three-or-more-vehicle crashes, and the remaining not-at-fault drivers in three-or-more vehicle crashes were compared to the driver population observed in NOPUS. RESULTS: The results showed that with respect to driver gender, vehicle type, time, and roadway type, drivers among D3_other did not show statistical significant difference from NOPUS observations. The age distribution of D3_other driver was not practically different to NOPUS observations. CONCLUSIONS: Overall, we conclude that D3_other drivers in FARS represents the driving population at the time of the crash. Practical applications: Our study provides a solid foundation for future studies to utilize D3_other as the reference group to validate the QIE representativeness assumption and has potential to increase the generalizability of future FARS studies.

Evaluation of a simulation-based surrogate safety metric.

The development of surrogate safety measures is essential due to the problems of availability and quality of historical crash data. The Aggregate Conflict Propensity Metric (ACPM) is a surrogate metric recently proposed and it is based on conflict studies and traffic simulations. ACPM is expected to be capable of assessing the relative safety levels of traffic facilities and/or treatments in order to help traffic engineers to select appropriate treatments based on traffic safety estimates. This paper presents three experimental tests conducted to evaluate the reliability of ACPM. In each test, ACPM is compared to a traditional conflict indicator in terms of identifying and ranking safety of traffic conditions under various traffic volumes based on traffic simulations. ACPM shows its strength and reliability in all three tests, as it provides results highly consistent with the Highway Safety Manual. The experimental tests indicate that ACPM is a promising surrogate safety measure that can appropriately identify relative safety among traffic treatments and/or facilities and provide traffic engineers with useful information on potential safety impact.

Quasi-induced exposure method: evaluation of not-at-fault assumption.

Crash rates are used to establish the relative safety of various variables of concern such as driver classes, vehicle types and roadway components. Appropriate exposure data for estimating crash rates is critical but crash databases do not contain information on driver or vehicle exposure. The quasi-induced exposure method, which uses not-at-fault driver/vehicle data as an exposure metric, is a technique used in order to overcome this problem. The basic assumption made here is that not-at-fault drivers represent the total population in question. This paper examines the validity of this assumption using the Kentucky crash database to define two samples of not-at-fault drivers. One sample included only not-at-fault drivers selected from the first two vehicles in a multi-vehicle crash (two or more vehicles involved) while the other included the not-at-fault drivers from multi-vehicle crashes with more than two vehicles involved and excluding the first two drivers. The assumption is that the randomness of the involvement of drivers in the second sample is more reasonable than the drivers in the first two vehicles involved in crashes. The results indicate that these two samples are similar; there is no statistical evidence demonstrating that both samples represent two different populations in the maneuvers and other variables/factors examined here; and they are representative simple random samples of the driver population with respect to the distribution of the driver age when there is no reasonable doubt about investigating officers' judgments. Thus, estimating relative crash propensities for any given driver type by using the quasi-induced exposure approach will yield reasonable estimates of exposure.

Crash involvement of drivers with multiple crashes.

A goal for any licensing agency is the ability to identify high-risk drivers. Kentucky data show that a significant number of drivers are repeatedly involved in crashes. The objective of this study is the development of a crash prediction model that can be used to estimate the likelihood of a driver being at fault for a near future crash occurrence. Multiple logistic regression techniques were employed using the available data for the Kentucky licensed drivers. This study considers as crash predictors the driver's total number of previous crashes, citations accumulated, the time gap between the latest two crashes, crash type, and demographic factors. The driver's total number of previous crashes was further disaggregated into the drivers' total number of previous at-fault and not-at-fault crashes. The model can be used to correctly classify at-fault drivers up to 74.56% with an overall efficiency of 63.34%. The total number of previous at-fault crash involvements, and having previous driver license suspensions and traffic school referrals are strongly associated with a driver being responsible for a subsequent crash. In addition, a driver's likelihood to be at fault in a crash is higher for very young or very old, males, drivers with both speeding and non-speeding citations, and drivers that had a recent crash involvement. Thus, the model presented here enables agencies to more actively monitor the likelihood of a driver to be at fault in a crash.

Evaluating the impact of passengers on the safety of older drivers.

PROBLEM: This study involved a quasi-induced exposure analysis of 4 years of crashes involving older drivers in the state of Kentucky. METHOD: Single- and multivehicle crashes were disaggregated according to the number of passengers: (a) no passenger, (b) one passenger, and (c) two or more passengers. RESULTS: Overall, the presence of two or more passengers was found to negatively impact the probability that drivers 75 years of age or older were at fault in crashes. Several potential factors were studied for interactive effects with passengers: vehicle occupant gender mix, time of the day, road curvature, grade, and number of lanes. The negative impact of passengers increased for some geometric road conditions. However, older drivers were found to be safer at night when carrying two or more passengers. The presence or absence of passengers was not found to affect the 65- to 74-year-old driver group. Groups of male vehicle occupants with a 75+ male driver were found to have high single-vehicle crash rates. IMPACT: These results are among the first to directly consider the effect of passengers on the crash-causing propensity of older drivers and the findings suggest more work is warranted to consider causes for the crash rate differences.