Social environment versus cycling competency predicting risk-taking in 11- to 13-year-old cyclists in The Netherlands

Abstract

Objectives: From age 12 onwards, cycling injuries begin rising in The Netherlands. A known contributing factor is younger children’s underdeveloped competency to deal with complex and hazardous traffic situations, and their exposure to such situations strongly increases after transitioning to secondary school. Little is known about intentional risk-taking as a contributing factor. In this developmental stage, children become increasingly vulnerable because of intentional risk-taking, affecting their safety and health. The incidence, predictors in the child’s social environment, and trends of such risks are systematically monitored; for instance, for alcohol use, smoking, and cyber bullying. Such monitors do not include risky road behavior. This exploratory field study examined the frequency of intentional risky cycling, its relationship with the perceived social environment, and relative to cycling competency measured as the ability to detect emerging hazards quickly.

Methods: Three hundred thirty-five students between 11 and 13 years of age (51% male) completed computerized tests of hazard perception skill and surveys on crashes, risk-taking, peer pressure, perceived risk-taking by parents or friends, and exposure to risky driving as passenger.

Results: Frequent risk-taking was associated with higher crash frequency. Stepwise regression confirmed that children who more often took risks on the road were also more sensitive to peer pressure, had more often been passengers of risky drivers, had parents and friends who exhibited risky behaviors in traffic more often, and perceived hazards as less dangerous but, in contrast to expectations, did not do worse on the detection of hazards. The predictors explained 28% of the variance in total risk-taking but varied from 6 to 20% depending on the specific risk-taking behavior concerned.

Conclusions: At least 20% of children sometimes or more often take risks in traffic. Children who feel peer pressure to behave in a risky manner, observe parents and friends behaving in a risky manner in traffic, and have been exposed as passengers to risky driving more often take risks in traffic themselves. These results provide support for including items on risky road behavior in health monitors and to design interventions that address the risk factors in the child’s perceived social environment.     

E-bikers’ braking behavior: Results from a naturalistic cycling study

Abstract

Objective: The number of e-bike users has increased significantly over the past few years and with it the associated safety concerns. Because e-bikes are faster than conventional bicycles and more prone to be in conflict with road users, e-bikers may need to perform avoidance maneuvers more frequently. Braking is the most common avoidance maneuver but is also a complex and critical task in emergency situations, because cyclists must reduce speed quickly without losing balance. The aim of this study is to understand the braking strategies of e-bikers in real-world traffic environments and to assess their road safety implications. This article investigates (1) how cyclists on e-bikes use front and rear brakes during routine cycling and (2) whether this behavior changes during unexpected conflicts with other road users.

Methods: Naturalistic data were collected from 6 regular bicycle riders who each rode e-bikes during a period of 2 weeks, for a total of 32.5?h of data. Braking events were identified and characterized through a combined analysis of brake pressure at each wheel, velocity, and longitudinal acceleration. Furthermore, the braking patterns obtained during unexpected events were compared with braking patterns during routine cycling.

Results: In the majority of braking events during routine cycling, cyclists used only one brake at a time, favoring one of the 2 brakes according to a personal pre-established pattern. However, the favored brake varied among cyclists: 66% favored the rear brake and 16% the front brake. Only 16% of the cyclists showed no clear preference, variously using rear brake, front brake, or combined braking (both brakes at the same time), suggesting that the selection of which brake to use depended on the characteristics of the specific scenario experienced by the cyclist rather than on a personal preference. In unexpected conflicts, generally requiring a larger deceleration, combined braking became more prevalent for most of the cyclists; still, when combined braking was not applied, cyclists continued to use the favored brake of routine cycling. Kinematic analysis revealed that, when larger decelerations were required, cyclists more frequently used combined braking instead of single braking.

Conclusions: The results provide new insights into the behavior of cyclists on e-bikes and may provide support in the development of safety measures including guidelines and best practices for optimal brake use. The results may also inform the design of braking systems intended to reduce the complexity of the braking operation.     

Cycling Skill Inventory: Assessment of motor–tactical skills and safety motives

Abstract

Objective: It is well established within the traffic psychology literature that a distinction can be made between driving skill and driving style. The majority of self-report questionnaires have been developed for car drivers, whereas only limited knowledge exists on the riding skill and style of cyclists. Individual differences in cycling skills need to be understood in order to apply targeted interventions.

Methods: This study reports on a psychometric analysis of the Cycling Skill Inventory (CSI), a self-report questionnaire that asks cyclists to rate themselves from definitely weak to definitely strong on 17 items. Herein, we administered the CSI using an online crowdsourcing method, complemented with respondents who answered the questionnaire using paper and pencil (n?=?1,138 in total). Our analysis focuses on understanding the major sources of variance of the CSI and its correlates with gender, age, exposure, and self-reported accident involvement as a cyclist.

Results: The results showed that 2 components underlie the item data: Motor–tactical skills and safety motives. Correlational analyses indicated that participants with a higher safety motives score were involved in fewer self-reported cycling accidents in the past 3 years. The analysis also confirmed well-established gender differences, with male cyclists having lower safety motives but higher motor–tactical skills than female cyclists.

Conclusions: The nomological network of the CSI for cyclists is similar to that of the Driving Skill Inventory for car drivers. Safety motives are a predictor of self-reported accident involvement among cyclists.     

An in-depth analysis of self-reported cycling injuries in single and multiparty bicycle crashes in Denmark

Introduction: Cycling is one of the main forms of transportation in Denmark. However, while the number of traffic crash fatalities in the country has decreased over the past decade, the frequency of cyclists killed or seriously injured has increased. The high rate of serious injuries and fatalities associated with cycling emphasizes the increasing need for mitigating the severity of such crashes. Method: This study conducted an in-depth analysis of cyclist injury severity resulting from single and multiparty bicycle-involved crashes. Detailed information was collected using self-reporting data undertaken in Denmark for a 12-month period between 1 November 2012 and 31 October 2013. Separate multilevel logistic (MLL) regression models were applied to estimate cyclist injury severity for single and multiparty crashes. The goodness-of-fit measures favored the MLL models over the standard logistic models, capturing the intercorrelation among bicycle crashes that occurred in the same geographical area. Results: The results also showed that single bicycle-involved crashes resulted in more serious outcomes when compared to multiparty crashes. For both single and multiparty bicycle crash categories, non-urban areas were associated with more serious injury outcomes. For the single crashes, wet surface condition, autumn and summer seasons, evening and night periods, non-adverse weather conditions, cyclists aged between 45 and 64 years, male sex, riding for the purpose of work or educational activities, and bicycles with light turned-off were associated with severe injuries. For the multiparty crashes, intersections, bicycle paths, non-winter season, not being employed or retired, lower personal car ownership, and race bicycles were directly related to severe injury consequences. Practical Applications: The findings of this study demonstrated that the best way to promote cycling safety is the combination of improving the design and maintenance of cycling facilities, encouraging safe cycling behavior, and intensifying enforcement efforts.     

The use and risk of portable electronic devices while cycling among different age groups

Introduction: In the Netherlands, a survey was set up to monitor the extent of the use of portable, electronic devices while cycling amongst different age groups of cyclists and to estimate the possible consequences for safety. Method: The main research questions concerned age differences in the self-reported use of electronic devices while cycling, self-reported crash involvement and risk, and self-reported compensatory behaviour. Teen cyclists (12–17 years) and young adult cyclists (18–34 years) were more frequent users, and also more indiscriminate users of portable devices while cycling than middle-aged and older adult cyclists (35–49 years; 50 + years). Results: After statistical correction for influences on crash risk of urbanization level, weekly time spent cycling, and cycling in more demanding traffic situations, the odds of being involved in a bicycle crash were estimated to be higher for teen cyclists and young adult cyclists who used electronic devices on every trip compared to same age groups cyclists who never used these devices. For middle-aged and older adult cyclists, the use of portable electronic devices was not a significant predictor of bicycle crashes, but frequency of cycling in demanding traffic situations was. Possible implications for education or legal measures are discussed. Impact on Industry: Results may inform researchers, policy makers, and cyclists themselves. Educational campaigns may use risk information to warn young cyclists about risk of device use while cycling.     

Global bike share: What the data tells us about road safety

IntroductionBike share has emerged as a rapidly growing mode of transport in over 800 cities globally, up from just a handful in the 1990s. Some analysts had forecast a rise in the number of bicycle crashes after the introduction of bike share, but empirical research on bike share safety is rare. The goal of this study is to examine the impact of bike share programs on cycling safety.MethodsThe paper has two substudies. Study 1 was a secondary analysis of longitudinal hospital injury data from the Graves et al. (2014) study. It compared cycling safety in cities that introduced bike share programs with cities that did not. Study 2 combined ridership data with crash data of selected North American and European cities to compare bike share users to other cyclists.ResultsStudy 1 indicated that the introduction of a bike share system was associated with a reduction in cycling injury risk. Study 2 found that bike share users were less likely than other cyclists to sustain fatal or severe injuries.ConclusionsOn a per kilometer basis, bike share is associated with decreased risk of both fatal and non-fatal bicycle crashes when compared to private bike riding.Practical ApplicationsThe results of this study suggest that concerns of decreased levels of cycling safety are unjustified and should not prevent decision makers from introducing public bike share schemes, especially if combined with other safety measures like traffic calming.     

Causal attribution in explanations of near-crash events behind the wheel,and its relationship to comparative judgments

IntroductionThe development of skills essential for avoiding crashes depends, in particular, on how drivers explain the causes of dangerous driving behaviors that resulted in a near crash. Here, we analyze causes attributed to such behaviors by car drivers in a self-report study. We explore the relationships between the dimensions of causal attribution, attribution of responsibility for the near crash, and drivers' comparative judgments.MethodFor approximately two months, drivers used logbooks to document the near crashes that occurred during their trips. The causes attributed in those reports to driving behaviors resulting in near crashes were then coded by two judges on the basis of several causal dimensions. Drivers also estimated their own and an average driver's skill levels, and their risk of being involved, as a driver, in a crash.ResultsWe distinguished main types of causes of the near crashes reported. Drivers had a tendency to more often attribute external causes to their own behaviors resulting in near crashes than to those of others. The probability of attributing a controllable cause increased with overestimation of one's own skills and decreased with underestimation of one's own risk in comparison to other drivers. The probability of attributing a stable cause increased with underestimation of one's own risk.ConclusionsWhen they explained their own behaviors resulting in near crashes, drivers mentioned different causes than when they explained those of others. Overestimation of one's own skills as compared to other drivers could be beneficial for developing crash-avoiding skills, insofar as it seems to foster attribution of controllable causes. By contrast, underestimation of one's own risk could have the opposite effect.Practical applicationsVulnerability to road risks should be stressed in driver’s training and risk communication campaigns. However, self-confidence with respect to one's skills should not always be targeted as a safety problem.     

Development of cycling skills in 7- to 12-year-old children

Objective: Cycling is a complex skill consisting of motor skills such as pedalling, braking, and steering. Because the ability to perform cycling skills is based on the age-related development of the child, experience and age-related reference values are of interest in light of customized testing and training.

Methods: One hundred thirty-eight children from the second (7–8 years), fourth (9–10 years), and sixth (11–12 years) grades performed a practical bicycle test consisting of 13 test items with specific points of interest. Moreover, age at onset of cycling, cycling to and from school, independent mobility, and minutes cycling per week were estimated using a parental questionnaire.

Results: It is found that cycling skills are strongly related to age with 11- to 12-year-old children outperforming 7- to 8-year-old children for 11 test items and 9- to 10-year-old children for 8 test items.

Conclusions: Next to age, age at onset of cycling also contributed to cycling skills. Therefore, our results suggest that cycling skills are associated with physical and mental maturation. Subsequently, age-related reference values are provided to customize testing and training.     

A case study analysis to examine motorcycle crashes in Bogota,Colombia

IntroductionContributory factors to motorcycle crashes vary among populations depending on several aspects such as the users' profiles, the composition and density of traffic, and the infrastructure features. A better understanding of local motorcycle crashes can be reached in those places where a comprehensive analysis is performed. This paper presents the results obtained from a case study analysis of 400 police records of accidents involving motorcycles in Bogota.MethodTo achieve a deeper level of understanding of how these accidents occur, we propose a systemic approach that uses available crash data. The methodology is inspired by accident prototypical scenarios, a tool for analysis developed in France.ResultsWhen grouping cases we identified three categories: solo motorcycle accidents, motorcyclist and pedestrian accidents, and accidents involving a motorcycle and another vehicle. Within these categories we undertook in-depth analyses of 32 groups of accidents obtaining valuable information to better comprehend motorcyclists' road crashes in a local context. Recurrent contributory factors in the groups of accidents include: inexperienced motorcyclists, wide urban roads that incite speeding and risky overtaking maneuvers, flowing urban roads that encourage high speed and increased interaction between vehicles, and lack of infrastructure maintenance.Practical ApplicationsThe results obtained are a valuable asset to define measures that will be conveniently adapted to the group of accident on which we want to act. The methodology exposed in this paper is applicable to the study of road crashes that involve all types of actors, not only the motorcyclists, and in contexts different than those presented in Bogota.     

Road traffic crashes among farm vehicle drivers in southern China: A cross-sectional survey

Objective: The objective of this study was to identify the prevalence and potential risk factors of farm vehicle–related road traffic crashes among farm vehicle drivers in southern China.

Methods: A cross-sectional study was used to interview 1,422 farm vehicle drivers in southern China. Farm vehicle–related road traffic crashes that occurred from December 2013 to November 2014 were investigated. Data on farm vehicle–related road traffic crashes and related factors were collected by face-to-face interviews.

Results: The prevalence of farm vehicle–related road traffic crashes among the investigated drivers was 7.2%. Farm vehicle–related road traffic crashes were significantly associated with self-reported vision problem (adjusted odds ratio [AOR] = 6.48, 95% confidence interval [CI], 3.86–10.87), self-reported sleep disorders (AOR = 10.03, 95% CI, 6.28–15.99), self-reported stress (AOR = 20.47, 95% CI, 9.96–42.08), reported history of crashes (AOR = 5.40, 95% CI, 3.47–8.42), reported history of drunk driving (AOR = 5.07, 95% CI, 2.97–8.65), and reported history of fatigued driving (AOR = 5.72, 95% CI, 3.73–8.78). The number of road traffic crashes was highest in the daytime and during harvest season. In over 96% of farm vehicle–related road traffic crashes, drivers were believed to be responsible for the crash. Major crash-causing factors included improper driving, careless driving, violating of traffic signals or signs, and being in the wrong lane.

Conclusion: Findings of this study suggest that farm vehicle–related road traffic crashes have become a burgeoning public health problem in China. Programs need to be developed to prevent farm vehicle–related road traffic crashes in this emerging country.     

Behavioral pathways in bicycle-motor vehicle crashes: From contributing factors,pre-crash actions,to injury severities

Introduction: This study performed a path analysis to uncover the behavioral pathways (from contributing factors, pre-crash actions to injury severities) in bicycle-motor vehicle crashes. Method: The analysis investigated more than 7,000 bicycle-motor vehicle crashes in North Carolina between 2007 and 2014. Pre-crash actions discussed in this study are actions of cyclists and motorists prior to the event of a crash, including “bicyclist failed to yield,” “motorist failed to yield,” “bicyclist overtaking motorist,” and “motorist overtaking bicyclist.” Results: Model results show significant correlates of pre-crash actions and bicyclist injury severity. For example, young bicyclists (18 years old or younger) are 23.5% more likely to fail to yield to motor traffic prior to the event of a crash than elder bicyclists. The “bicyclist failed to yield” action is associated with increased bicyclist injury severity than other actions, as this behavior is associated with an increase of 5.88 percentage points in probability of a bicyclist being at least evidently injured. The path analysis can highlight contributing factors related to risky pre-crash actions that lead to severe injuries. For example, bicyclists traveling on regular vehicle travel lanes are found to be more likely to involve the “bicyclist failed to yield” action, which resulted in a total 44.38% (7.04% direct effect + 37.34% indirect effect) higher likelihood of evident or severe injuries. The path analysis can also identify factors (e.g., intersection) that are not directly but indirectly correlated with injury severity through pre-crash actions. Practical Applications: This study offers a methodological framework to quantify the behavioral pathways in bicycle-motor vehicle crashes. The findings are useful for cycling safety improvements from the perspective of bicyclist behavior, such as the educational program for cyclists.     

Analysis of bicycle crashes in Sweden involving injuries with high risk of health loss

Objective: The objectives of the present article were to (a) describe the main characteristics of bicycle crashes with regard to the road environment, crash opponent, cyclist, and crash dynamics; (b) compare individuals who describe their health after the crash as declined with those who describe their health as not affected; and (c) compare the number of injured cyclists who describe their health as declined after the crash with the predicted number of permanent medical impairments within the same population.

Methods: A sample of individuals with specific injury diagnoses was drawn from the Swedish Traffic Accident Data Acquisition (STRADA) database (n?=?2,678). A survey form was used to collect additional information about the crash and the health-related outcomes. The predicted number of impaired individuals was calculated by accumulating the risk for all individuals to sustain at least a 1% permanent medical impairment, based on the injured body region and injury severity.

Results: Nine hundred forty-seven individuals (36%) responded, of whom 44% reported declined health after the crash. The majority (68%) were injured in single bicycle crashes, 17% in collisions with motor vehicles, and 11% in collisions with another cyclist or pedestrian. Most single bicycle crashes related to loss of control (46%), mainly due to skidding on winter surface conditions (14%), followed by loss of control during braking (6%). There was no significant difference in crash distribution comparing all crashes with crashes among people with declined health. The predicted number of impaired individuals (n?=?427) corresponded well with the number of individuals self-reporting declined health (n?=?421).

Conclusions: The types of crashes leading to health loss do not substantially differ from those that do not result in health loss. Two thirds of injuries leading to health loss occur in single bicycle crashes. In addition to separating cyclists from motorized traffic, other preventive strategies are needed.     

Are you an ambitious cyclist? Results of the cyclist profile questionnaire in Germany

Abstract

Objective: A number of studies have already grouped cyclists according to different aspects of their mobility behavior. This could be used e.g., to improve the bicycle infrastructure planning, to detect critical spots and, to reduce obstacles for cycling. This wide, preexisting, range of cyclist typologies usually concentrates on one or two influence factors and differs, content-wise, in both factors used, as well as, methodically. Based on existing cyclist typologies we extracted all possible influence factors to integrate them in one single questionnaire. The objective of this study, using an empirical, based approach, is to compare this typology of cyclists with existing ones, integrating all known influence factors of recent studies.

Methods: To address these issues, we conducted a Germany-wide online survey on cycling behavior, covering all relevant aspects we derived from both literature and especially, former cyclist typology studies including: social factors; the impact of environmental, individual; and route factors; as well as motives. The main goal was to identify distinct types of cyclists, and describe them as detailed as possible. The heterogeneous sample included a total of 10,294 responses.

Results: Using factor and cluster analyses, a multidimensional typology with four groups of cyclists was derived which were interpreted as: ambitious, functional, pragmatic, and passionate cyclists. In addition, socio-economic factors, cyclist’s motivation, and crash history were analyzed.

Conclusion: The results produced by grouping different characteristics of cyclists can lead to policy recommendations or communal bicycle traffic planning. Policy planners can estimate reactions of the different types on interventions and adjust their decisions which can serve to support already passionate cyclists or, encourage normally under-represented infrequent cyclists to cycle more. The extent of perceived safety plays here an important role in the classification, e.g., the handling of high-risk areas for crashes.     

Exploring the severity of bicycle–vehicle crashes using latent class clustering approach in India

IntroductionBicyclists are vulnerable users in the shared asset like roadways. However, people still prefer to use bicycles for environmental, societal, and health benefits. In India, the bicycle plays a role in supporting the mobility to more people at lower cost and are often associated with the urban poor. Bicyclists represents one of the road user categories with highest risk of injuries and fatalities. According to the report by the Ministry of Road Transport and Highways (Accidents, 2017) in India, there is a sharp increase in the number of fatal victims for bicyclists in 2017 over 2016. The number of cyclists killed jumped from 2,585 in 2016 to 3,559 in 2017, a 37.7% increase. Method: Few studies have only investigated the crash risk perceived by the bicyclists while interacting with other road users. The present paper investigates the injury severity of bicyclists in bicycle-vehicle crashes that occurred in the state of Tamilnadu, India during the nine year period (2009–2017). The analyses demonstrate that dividing bicycle-vehicle collision data into five clusters helps in reducing the systematic heterogeneity present in the data and identify the hidden relationship between the injury severity levels of bicyclists and cyclists demographics, vehicle, environmental, temporal cause for the crashes. Results: Latent Class Clustering (LCC) approach was used in the present study as a preliminary tool for the segmentation of 9,978 crashes. Later, logistic regression analysis was used to identify the factors that influence bicycle crash severity for the whole dataset as well as for the clusters that were obtained from the LCC model. Results of this study show that combined use of both techniques reveals further information that wouldn’t be obtained without prior segmentation of the data. Few variables such as season, weather conditions, and light conditions were significant for certain clusters that were hidden in the whole dataset. This study can help domain experts or traffic safety researchers to segment traffic crashes and develop targeted countermeasures to mitigate injury severity.     

Fatal and serious collisions involving pedal cyclists and trucks in London between 2007 and 2011

Objective: Increased numbers of people riding pedal cycles have led to a greater focus on pedal cycle safety. The aim of this article is to explore factors that are associated with fatal and a small number of serious-injury pedal cyclist crashes involving trucks that occurred in London between 2007 and 2011.

Methods: Data were collected from police collision files for 53 crashes, 27 of which involved a truck (≥3.5 tonnes) and a pedal cycle. A systematic case review approach was used to identify the infrastructure, vehicle road user, and management factors that contributed to these crashes and injuries and how these factors interacted.

Results: Trucks turning left conflicting with pedal cyclists traveling straight ahead was a common crash scenario. Key contributory factors identified included the pedal cyclists not being visible to the truck drivers, road narrowing, and inappropriate positioning of pedal cyclists.

Conclusions: Crashes involving trucks and pedal cyclists are complex events that are caused by multiple interacting factors; therefore, multiple measures are required to prevent them from occurring.     

The effect of speed limit reductions in urban areas on cyclists’ injuries in collisions with cars

Abstract

Objective: Impact speed is one of the most important factors explaining the severity of injuries to cyclists when they collide with passenger cars. To reduce injury severity (especially for vulnerable road users), since 2008, Swedish municipalities have the authority to lower the speed limit to 30 or 40?km/h in urban areas as appropriate. The aim of this study was to evaluate how this speed limit reduction has influenced the injury severity for cyclists in this type of collision.

Method: Data from 1,953 collisions between bicycles and passenger cars were collected using information from third-party-liability insurance claims from 2005 to 2017. The change of speed limit distribution, influenced by the reduction of speed limits in urban areas, where car-to-cyclist collisions occurred was studied. Following that, injury severity for cyclists was evaluated regarding collisions occurring in areas with different speed limits.

Results: The results show that, in collisions with cars, cyclists have a significantly lower risk of a moderate-to-fatal (MAIS 2+) injury when the speed limit is 30–40?km/h compared to 50–60?km/h. During the last decade, while the speed-limit has been lowered on many road-sections in urban areas from 50–60?km/h to 30–40?km/h the risk of a cyclist getting a MAIS 2+ injury decreased by 25%. In 2005 to 2011, 16% of the crashes happened on a road with a speed limit of 30–40?km/h; in 2016–2017, this percentage had increased to approximately 50%. Thus, in recent years more crashes occurred on roads with lower speed limits, and in these crashes, there was a lower risk of severe injuries to cyclists. Unfortunately, it was not possible to evaluate the risk of a crash for specific speed limits; since one limitation of this study was the lack of exposure data, nor do we know the impact speed or the actual speed of the vehicles.

Conclusions: This study is an important follow-up on the implementation of measures that can influence bicycle safety. The insurance data used, made it possible to quantify a positive effect on injury severity for cyclists in passenger car-to-cyclist collisions when the speed limit was reduced in urban areas. Insurance claims cover collisions of all crash severity, so they include data covering all types of injuries—not just the most severe/fatal ones. This aspect is especially important in the speed intervals evaluated here, since moderate (MAIS 2) injuries are very frequent in lower-speed crashes and even these injuries can result in long-term consequences.     

Examination of the severity of two-lane highway traffic barrier crashes using the mixed logit model

IntroductionVehicles in transport sometimes leave the travel lane and encroach onto natural or artificial objects on the roadsides. These types of crashes are called run-off the road crashes, which account for a large proportion of fatalities and severe crashes to vehicle occupants. In the United States, there are about one million such crashes, with roadside features leading to one third of all road fatalities. Traffic barriers could be installed to keep vehicles on the roadways and to prevent vehicles from colliding with obstacles such as trees, boulder, and walls. The installation of traffic barriers would be warranted if the severity of colliding with the barrier would be less severe than colliding with other fix objects on the sides of the roadway. However, injuries and fatalities do occur when vehicle collide with traffic barriers. A comprehensive analysis of traffic barrier features is lacking due to the absence of traffic barrier features data. Previous research has focused on simulation studies or only a general evaluation of traffic barriers, without accounting for different traffic barrier features.MethodThis study is conducted using an extensive traffic barrier features database for the purpose of investigating the impact of different environmental and traffic barrier geometry on this type of crash severity. This study only included data related to two-lane undivided roadway systems, which did not involve median barrier crashes. Crash severity is modeled using a mixed binary logistic regression model in which some parameters are fixed and some are random.ResultsThe results indicated that the effects of traffic barrier height, traffic barrier offset, and shoulder width should not be separated, but rather considered as interactions that impact crash severity. Rollover, side slope height, alcohol involvement, road surface conditions, and posted speed limit are some factors that also impact the severity of these crashes. The effects of gender, truck traffic count, and time of a day were found to be best modeled with random parameters in this study. The effects of these risk factors are discussed in this paper.Practical applicationsResults from this study could provide new guidelines for the design of traffic barriers based upon the identified roadway and traffic barrier characteristics.     

A clustering approach to integrate traffic safety in road maintenance prioritization

Objectives: We combine data on roads and crash characteristics to identify patterns in road traffic crashes with regard to road characteristics. We illustrate how combined analysis of data regarding road maintenance, maintenance costs, road characteristics, crash characteristics, and geographical location can enrich road maintenance prioritization from a traffic safety perspective.

Methods: The study is based on traffic crash data merged with road maintenance data and annual average daily traffic (AADT) collected in Denmark. We analyzed 3,964 crashes that occurred from 2010 to 2015. A latent class clustering (LCC) technique was used to identify crash clusters with different road and crash characteristics. The distribution of crash severity and estimated road maintenance costs for each cluster was found and cluster differences were compared using the chi-square test. Finally, a map matching procedure was used to identify the geographical distribution of the crashes in each cluster.

Results: Results showed that based on road maintenance levels there was no difference in the distribution of crash severity. The LCC technique revealed 11 crash clusters. Five clusters were characterized by crashes on roads with a poor maintenance level (levels 4 and 3). Only a few of these crashes included a vulnerable road user (VRU) but many occurred on roads without barriers. Four clusters included a large share of crashes on acceptably maintained roads (level 2). For these clusters only small variations in road characteristics were found, whereas the differences in crash characteristics were more dominant. The last 2 clusters included crashes that mainly occurred on new roads with no need for maintenance (level 1). Injury severity, estimated maintenance costs, and geographical location were found to be differently distributed for most of the clusters.

Conclusions: We find that focusing solely on road maintenance and crash severity does not provide clear guidance of how to prioritize between road maintenance efforts from a traffic safety perspective. However, when combined with geographical location and crash characteristics, a more nuanced picture appears that allows consideration of different target groups and perspectives.