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.     

Differences in Long-term Medical Consequences Depending on Impact Direction Involving Passenger Cars

Objective: There is limited knowledge of the long-term medical consequences for occupants injured in car crashes in various impact directions. Thus, the objective was to evaluate whether injuries leading to permanent medical impairment differ depending on impact direction.

Methods: In total, 36,743 injured occupants in car crashes that occurred between 1995 and 2011 were included. All initial injuries (n = 61,440) were classified according to the Abbreviated Injury Scale (AIS) 2005. Injured car occupants were followed for at least 3 years to assess permanent medical impairment. The data were divided into different groups according to impact direction and levels of permanent impairment. The risk of permanent medical impairment was established for different body regions and injury severity levels, according to AIS.

Results: It was found that almost 12% of all car occupants sustained a permanent medical impairment. Given an injury, car occupants involved in rollover crashes had the highest overall risk to sustain a permanent medical impairment. Half of the head injuries leading to long-term consequences occurred in frontal impacts. Far-side occupants had almost the same risk as near-side occupants. Occupants who sustained a permanent medical impairment from cervical spine injuries had similar risk in all impact directions (13%) except from rollover (17%). However, these injuries occurred more often in rear crashes. Most of the injuries leading to long-term consequences were classified as minor injuries by AIS for all impact directions.

Conclusions: Studying crash data from a perspective of medical impairment is important to identify injuries that might not be prioritized only considering the AIS but might lead to lower quality of life for the occupant and also costs for society. These results can be used for road transport system strategies and for making priority decisions in vehicle design.     

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.     

The use and safety of medications known to affect driving in Jordan: A cross-sectional study

Objectives: The aim of this study was to estimate the main driving-impairing medications used by drivers in Jordan, the reported frequency of medication side effects, the frequency of motor vehicle crashes (MVCs) while using driving-impairing medicines, as well as factors associated with MVCs.

Methods: A cross-sectional study involving 1,049 individuals (age 18–75 years) who are actively driving vehicles and taking at least one medication known to affect driving (anxiolytics, antidepressants, hypnotics, antiepileptics, opioids, sedating antihistamines, hypoglycemic agents, antihypertensives, central nervous system [CNS] stimulants, and herbals with CNS-related effects) was conducted in Amman, Jordan, over a period of 8 months (September 2013–May 2014) using a structured validated questionnaire.

Results: Sixty-three percent of participants noticed a link between a medicine taken and feeling sleepy and 57% stated that they experience at least one adverse effect other than sleepiness from their medication. About 22% of the participants reported having a MVC while on medication. Multiple logistic regression analysis showed that among the participants who reported having a crash while taking a driving-impairing medication, the odds ratios were significantly higher for the use of inhalant substance (odds ratio [OR] = 2.787, P = .014), having chronic conditions (OR = 1.869, P = .001), and use of antiepileptic medications (OR = 2.348, P = .008) and significantly lower for the use of antihypertensives (OR = 0.533, P = .008).

Conclusion: The study results show high prevalence of adverse effects of medications with potential for driving impairment, including involvement in MVCs. Our findings highlight the types of patient-related and medication-related factors associated with MVCs in Jordan, such as inhalant use, presence of chronic conditions, and use of antiepileptics.     

An empirical bayes safety evaluation of tram/streetcar signal and lane priority measures in Melbourne

Objective: Streetcars/tram systems are growing worldwide, and many are given priority to increase speed and reliability performance in mixed traffic conditions. Research related to the road safety impact of tram priority is limited. This study explores the road safety impacts of tram priority measures including lane and intersection/signal priority measures.

Method: A before–after crash study was conducted using the empirical Bayes (EB) method to provide more accurate crash impact estimates by accounting for wider crash trends and regression to the mean effects. Before–after crash data for 29 intersections with tram signal priority and 23 arterials with tram lane priority in Melbourne, Australia, were analyzed to evaluate the road safety impact of tram priority.

Results: The EB before–after analysis results indicated a statistically significant adjusted crash reduction rate of 16.4% after implementation of tram priority measures. Signal priority measures were found to reduce crashes by 13.9% and lane priority by 19.4%. A disaggregate level simple before–after analysis indicated reductions in total and serious crashes as well as vehicle-, pedestrian-, and motorcycle-involved crashes. In addition, reductions in on-path crashes, pedestrian-involved crashes, and collisions among vehicles moving in the same and opposite directions and all other specific crash types were found after tram priority implementation.

Conclusions: Results suggest that streetcar/tram priority measures result in safety benefits for all road users, including vehicles, pedestrians, and cyclists. Policy implications and areas for future research are discussed.