What is the relationship between self-reported aberrant driving behaviors,mindfulness, and self-reported crashes and infringements?

Objectives: This study investigated the relationship between self-reported aberrant driving behaviors, mindfulness, and self-reported crashes and infringements.

Methods: Three hundred and eighteen participants (M = 46.0 years, SD = 13.7 years; female: 81.8%) completed an online survey that assessed aberrant driving behaviors, mindfulness (including regular mindfulness meditation [MM]), and self-reported crashes and infringements during the past 2 years. Structural equation modeling (SEM) was used to examine the relationship between self-reported aberrant driving behaviors and mindfulness simultaneously, as well as with participants' age and estimated kilometers driven over the past year.

Results: The results of the SEM showed that mindfulness was negatively related to each self-reported aberrant driving behavior, with the strongest relationships being between mindfulness and driving-related lapses (?0.58) and errors (?0.46). Participants who practice MM had significantly fewer crashes in the past 2 years and reported significantly fewer driving-related violations and lapses compared to participants who did not practice MM (crashes: 9.3% vs. 18.8%, P < .05; violations: M = 6.66 [SD = 3.44] vs. M = 7.68 [SD = 4.53], P < .05; errors: M = 5.17 [SD = 3.44] vs. M = 6.19 [SD = 4.12], P < .05).

Conclusions: More research is needed to understand whether MM results in more mindful and attentive drivers or whether individuals who practice MM may have other traits or behaviors that are linked to improved safety.     

Michigan's graduated driver licensing program: evaluation of the first four years

INTRODUCTION: To evaluate the four-year outcome of Michigan's graduated driver licensing (GDL) program, motor-vehicle crash data for 16-year-old drivers in 1996 (pre-GDL), and 1998-2001 (post-GDL) were analyzed. METHOD: Relative risks and 95% confidence intervals for several crash types were computed, and pre-post-GDL population-based crash rates were compared. Reductions in crash risks among 16-year-olds previously found in 1998 and 1999 were generally maintained in 2000 and 2001. RESULTS: Reductions in crash risk among 16-year-olds from 1996 to 2001 were 29% for all, 44% for fatal, 38% each for nonfatal-injury and fatal-plus-nonfatal-injury, 32% for day, 31% for evening, 59% for night, 32% for single-vehicle, and 28% for multi-vehicle crashes. Even after adjusting for more general population-wide changes among drivers 25 years and older that might have contributed to changes in 16-year-old crash risk, reductions remained impressive (19% for all crashes in 2001). IMPACT ON INDUSTRY: As one approach to reducing teenage motor-vehicle morbidity and mortality, GDL remains promising.     

The role of enforcement programs in increasing seat belt use

Seat belt laws by themselves led to increased belt use in the United States and Canada, but initial effects were limited. Canadian provincial officials launched highly publicized enforcement campaigns in the early 1980s that resulted in substantially increased belt use. Canadian-style enforcement programs subsequently were adopted in the United States, and the use of such programs has grown in recent years. Lessons from these efforts include the importance of police leadership, focused publicity about enforcement, and sustained rather than single-shot efforts. What is needed in the United States to achieve a national belt use rate of 90% or greater is widespread, methodical, and sustained application of enforcement programs augmented by creative publicity. Enhanced penalties-in particular drivers license points-likely will be needed to reach hard-core nonusers.     

Responsibility of drivers, by age and gender, for motor-vehicle crash deaths

Problem: Motor-vehicle crash rate comparisons by age and gender usually are based on the extent to which drivers in a particular age/gender category are themselves injured or involved in crashes (e.g., the number of 20-year-old females in crashes). Basing comparisons instead on the extent to which drivers in various age/gender groups are responsible for deaths (including themselves) in their crashes is more revealing of their overall contribution to the problem. Methods: Data from the Fatality Analysis Reporting System (FARS, 1996–2000) were used in the analysis, which was based on crashes that involved one or two vehicles only. Drivers in fatal single-vehicle crashes were assumed to have responsibility for the crash. In fatal two-vehicle crashes, driver operator errors reported by police were used to assign crash responsibility. Results: When all crashes were considered, both the youngest and oldest drivers were most likely to be responsible for deaths in their crashes. In two-vehicle crashes, the oldest drivers were more likely than young drivers to be responsible. Young males were more likely than young females to be responsible for crash deaths, whereas females in their 50s and older were more likely than same-age males to be responsible. In terms of responsibility for deaths per licensed driver, young drivers, especially males, had the highest rates because of their high involvement rates and high responsibility rates. The majority of deaths for which young drivers were responsible occurred to people other than themselves, especially passengers in their vehicles, whereas the bulk of the deaths for which older drivers were responsible were their own. Discussion: The results highlight the contribution of young drivers to the motor-vehicle crash problem, the need for measures such as passenger restrictions in graduated licensing systems, and the need for vehicle modifications to better protect older occupants.     

Analysis of motor-vehicle crashes at stop signs in four US cities

Problem: Nearly 700,000 police-reported motor vehicle crashes occur annually at stop signs, and approximately one-third of these crashes involve injuries. The purpose of this study was to develop a better understanding of the crashes that occur at stop signs and to identify potential countermeasures. Method: Police reports of crashes at stop sign-controlled intersections during 1996–2000 in four U.S. cities were examined in detail. At total of 1,788 crash reports for intersections with two-way stop signs were included in the study. Results: Stop sign violations accounted for about 70% of all crashes. Typically these crashes were angular collisions. Among crashes not involving stop violations, rear-end crashes were most common, accounting for about 12% of all crashes. Stop sign violation crashes were classified into several subtypes — driver stopped, driver did not stop, snow/wet/ice, and other/unknown. In about two-thirds of stop sign violation crashes, drivers said they had first come to a stop. In these cases, inability or failure to see approaching traffic often was cited as the cause of the crash. Drivers younger than 18 as well as drivers 65 and older were disproportionately found to be at fault in crashes at stop signs. Impact on industry: Potential countermeasures include changing traffic control and intersection design, improving intersection sight distance, and increasing conspicuity of stop signs through supplemental pavement markings and other devices.     

Some determinants of possible injuries in crashes at signalized intersections

INTRODUCTION: This paper identifies some determinants of possible injuries from crashes that occur at signalized intersections using a conditional probability model. METHOD: It uses longitudinal data for one city and calculates the marginal effects of these determinants. RESULTS: Among its findings, an airbag deploying in a crash reduces the conditional probability of sustaining possible injury. The size of this decrease is similar for the front airbag or both side airbags deploying. When crashes occur because a driver suddenly falls ill, or because a driver has an existing medical condition that flares up, the results show that the conditional probability of sustaining possible injury increases by 0.1414 and 0.1217, respectively. In comparison the conditional probability of sustaining possible injury increases by 0.0706 when the crash is because of impairment from drugs or alcohol. Additionally, the probability of sustaining possible injury in the event of a crash is 0.0435 and 0.0459 higher in passenger cars and vans respectively than in other vehicles. CONCLUSIONS: Comparing these results to those in the traffic safety literature, the marginal effects from national data are similar to those from local level data.     

Identifying crash propensity using specific traffic speed conditions

INTRODUCTION: In spite of recent advances in traffic surveillance technology and ever-growing concern over traffic safety, there have been very few research efforts establishing links between real-time traffic flow parameters and crash occurrence. This study aims at identifying patterns in the freeway loop detector data that potentially precede traffic crashes. METHOD: The proposed solution essentially involves classification of traffic speed patterns emerging from the loop detector data. Historical crash and loop detector data from the Interstate-4 corridor in the Orlando metropolitan area were used for this study. Traffic speed data from sensors embedded in the pavement (i.e., loop detector stations) to measure characteristics of the traffic flow were collected for both crash and non-crash conditions. Bayesian classifier based methodology, probabilistic neural network (PNN), was then used to classify these data as belonging to either crashes or non-crashes. PNN is a neural network implementation of well-known Bayesian-Parzen classifier. With its superb mathematical credentials, the PNN trains much faster than multilayer feed forward networks. The inputs to final classification model, selected from various candidate models, were logarithms of the coefficient of variation in speed obtained from three stations, namely, station of the crash (i.e., station nearest to the crash location) and two stations immediately preceding it in the upstream direction (measured in 5 minute time slices of 10-15 minutes prior to the crash time). RESULTS: The results showed that at least 70% of the crashes on the evaluation dataset could be identified using the classifiers developed in this paper.     

Modeling faults among e-bike-related fatal crashes in China

Objectives: This article aims to model fault in e-bike fatal crashes in a county-level city in China.

Method: Three-year crash data are retrieved from the crash reports (2012–2014) from the Taixing Police Department. A mixed logit model is introduced to explore significant factors associated with fault assignment, as well as accounting for similarity among fault assignment and heterogeneity within unobserved variables.

Results: The modeling results indicate some interesting new findings. First, precrash behaviors of both drivers and e-bike riders are found to be significant to fault assignment. Second, bike lane and median type are significantly associated with e-bike rider fault commitment. Third, specific groups of e-bike riders (low-educated and older) and drivers (heavy good vehicles) are more likely to be at fault in e-bike crashes. Last, crash location and the built environment have significant correlations with faulty behaviors of e-bike riders.

Conclusions: Safety countermeasures are proposed including (1) the deployment of traffic design and control elements including physically separated bike lanes, medians, video surveillance systems for e-bike riders, and left-turning treatments for nonmotorists (e.g., a 2-step e-bike left turning); (2) the amendment of the current traffic regulations on drunk e-bike riders and child e-bike passengers; (3) the development of a license system for specific e-bike rider groups (older and low-educated) and a safety campaign for drivers (to increase safety awareness when parking on-street or driving heavy good vehicles). Some interesting future research topics are also suggested: e-bike riders' behaviors at unsignalized intersections and mid-block openings, e-bike safety in suburban areas, and an in-depth study of the effect of the built environment on e-bike safety.