Built environment effects on bike crash frequency and risk in Beijing

IntroductionBuilding a safe biking environment is crucial to encouraging bicycle use. In developed areas with higher density and more mixed land use, the built environment factors that pose a crash risk may vary. This study investigates the connection between biking risk factors and the compact built environment, using data for Beijing.MethodIn the context of China, this paper seeks to answer two research questions. First, what types of built environment factors are correlated with bike-automobile crash frequency and risk? Second, how do risk factors vary across different types of bikes? Poisson lognormal random effects models are employed to examine how land use and roadway design factors are associated with the bike-automobile crashes.ResultsThe main findings are: (1) bike-automobile crashes are more likely to occur in densely developed areas, which is characterized by higher population density, more mixed land use, denser roads and junctions, and more parking lots; (2) areas with greater ground transit are correlated with more bike-automobile crashes and higher risks of involving in collisions; (3) the percentages of wider streets show negative associations with bike crash frequency; (4) built environment factors cannot help explain factors contributing to motorcycle-automobile crashes.Practical ApplicationsIn China's dense urban context, important policy implications for bicycle safety improvement drawn from this study include: prioritizing safety programs in urban centers, applying safety improvements to areas with more ground transit, placing bike-automobile crash countermeasures at road junctions, and improving bicycle safety on narrower streets.     

Automobile injury trends in the contemporary fleet: Belted occupants in frontal collisions

Objective: As vehicle safety technologies and evaluation procedures advance, it is pertinent to periodically evaluate injury trends to identify continuing and emerging priorities for intervention. This study examined detailed injury distributions and injury risk trends in belted occupants in frontal automobile collisions (10 o’clock to 2 o’clock) using NASS-CDS (1998–2015).

Methods: Injury distributions were examined by occupant age and vehicle model year (stratified at pre- and post-2009). Logistic regression models were developed to examine the effects of various factors on injury risk (by body region), controlling for delta-V, sex, age, height, body mass index (BMI), vehicle model year (again stratified at 2009).

Results: Among other observations, these analyses indicate that newer model year vehicles (model year [MY] 2009 and later) carry less risk of Abbreviated Injury Scale (AIS) 2+ and AIS 3+ injury compared to older model year vehicles, with odds ratios of 0.69 (AIS 2+) and 0.45 (AIS 3+). The largest reductions in risk between newer model year vehicles and older model year vehicles occur in the lower extremities and in the risk of skull fracture. There is no statistically significant change in risk of AIS 3+ rib fracture or sternum injury between model year categories. Females are at greater risk of AIS 2+ and AIS 3+ injury compared to males, with increased risk across most injury types.

Conclusions: For belted occupants in frontal collisions, substantial reductions in injury risk have been realized in many body regions in recent years. Risk reduction in the thorax has lagged other body regions, resulting in increasing prevalence among skeletal injuries in newer model year vehicles (especially in the elderly). Injuries also remain common in the arm and hand/wrist for all age ranges studied. These results provide insight into where advances in the field have made gains in occupant protection and what injury types remain to be addressed.     

Analyzing injury severity of bus passengers with different movements

Objective: Though public transport vehicles are rarely involved in mass casualty accidents, when they are, the number of injuries and fatalities is usually high due to the high passenger capacity. Of the few studies that have been conducted on bus safety, the majority focused on vehicle safety features, road environmental factors, as well as driver characteristics. Nevertheless, few studies have attempted to investigate the underlying risk factors related to bus occupants. This article presents an investigation aimed at identifying the risk factors affecting injury severity of bus passengers with different movements.

Method: Three different passenger movement types including standing, seated, and boarding/alighting were analyzed individually using classification and regression tree (CART) method based on publicly available accident database of Great Britain.

Results: According to the results of exploratory analyses, passenger age and vehicle maneuver are associated with passenger injury severity in all 3 types of accidents. Moreover, the variable “skidding and overturning” is associated with injury severity of seated passengers and driver age is correlated with injury severity of standing and boarding/alighting passengers.

Conclusions: The CART method shows its ability to identify and easily explain the complicated patterns affecting passenger injury severity. Several countermeasures to reduce bus passenger injury severity are recommended.     

Teens' distracted driving behavior: Prevalence and predictors

IntroductionTeen drivers' over-involvement in crashes has been attributed to a variety of factors, including distracted driving. With the rapid development of in-vehicle systems and portable electronic devices, the burden associated with distracted driving is expected to increase. The current study identifies predictors of secondary task engagement among teenage drivers and provides basis for interventions to reduce distracted driving behavior. We described the prevalence of secondary tasks by type and driving conditions and evaluated the associations between the prevalence of secondary task engagement, driving conditions, and selected psychosocial factors.MethodsThe private vehicles of 83 newly-licensed teenage drivers were equipped with Data Acquisition Systems (DAS), which documented driving performance measures, including secondary task engagement and driving environment characteristics. Surveys administered at licensure provided psychosocial measures.ResultsOverall, teens engaged in a potentially distracting secondary task in 58% of sampled road clips. The most prevalent types of secondary tasks were interaction with a passenger, talking/singing (no passenger), external distraction, and texting/dialing the cell phone. Secondary task engagement was more prevalent among those with primary vehicle access and when driving alone. Social norms, friends' risky driving behaviors, and parental limitations were significantly associated with secondary task prevalence. In contrast, environmental attributes, including lighting and road surface conditions, were not associated with teens' engagement in secondary tasks.ConclusionsOur findings indicated that teens engaged in secondary tasks frequently and poorly regulate their driving behavior relative to environmental conditions. Practical applications: Peer and parent influences on secondary task engagement provide valuable objectives for countermeasures to reduce distracted driving among teenage drivers.     

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.     

Investigation of injury severities in single-vehicle crashes in North Carolina using mixed logit models

IntroductionRoadway departure (RwD) crashes, comprising run-off-road (ROR) and cross-median/centerline head-on collisions, are one of the most lethal crash types. According to the FHWA, between 2015 and 2017, an average of 52 percent of motor vehicle traffic fatalities occurred each year due to roadway departure crashes. An avoidance maneuver, inattention or fatigue, or traveling too fast with respect to weather or geometric road conditions are among the most common reasons a driver leaves the travel lane. Roadway and roadside geometric design features such as clear zones play a significant role in whether human error results in a crash. Method: In this paper, we used mixed-logit models to investigate the contributing factors on injury severity of single-vehicle ROR crashes. To that end, we obtained five years' (2010–2014) of crash data related to roadway departures (i.e., overturn and fixed-object crashes) from the Federal Highway Administration's Highway Safety Information System Database. Results: The results indicate that factors such as driver conditions (e.g., age), environmental conditions (e.g., weather conditions), roadway geometric design features (e.g., shoulder width), and vehicle conditions significantly contributed to the severity of ROR crashes. Conclusions: Our results provide valuable information for traffic design and management agencies to improve roadside design policies and implementing appropriately forgiving roadsides for errant vehicles. Practical applications: Our results show that increasing shoulder width and keeping fences at the road can reduce ROR crash severity significantly. Also, increasing road friction by innovative materials and raising awareness campaigns for careful driving at daylight can decrease the ROR crash severity.     

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.     

Workers' compensation insurer risk control systems: Opportunities for public health collaborations

IntroductionWorkers' compensation (WC) insurers offer services and programs for prospective client selection and insured client risk control (RC) purposes. Toward these aims, insurers collect employer data that may include information on types of hazards present in the workplace, safety and health programs and controls in place to prevent injury/illness, and return-to-work programs to reduce injury/illness severity. Despite the potential impact of RC systems on workplace safety and health and the use of RC data in guiding prevention efforts, few research studies on the types of RC services provided to employers or the RC data collected have been published in the peer-reviewed literature.MethodsResearchers conducted voluntary interviews with nine private and state-fund WC insurers to collect qualitative information on RC data and systems.ResultsInsurers provided information describing their RC data, tools, and practices. Unique practices as well as similarities including those related to RC services, policyholder goals, and databases were identified.ConclusionsInsurers collect and store extensive RC data, which have utility for public health research for improving workplace safety and health.Practical applicationsIncreased public health understanding of RC data and systems and an identification of key collaboration opportunities between insurers and researchers will facilitate increased use of RC data for public health purposes.     

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.     

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.     

National evaluation of the effect of graduated driver licensing laws on teenager fatality and injury crashes

ProblemAutomobile crashes remain a prominent cause of death and injury for teenagers in the United States. While it is generally agreed that graduated drivers licensing (GDL) influences crash rates, it is unclear which components have the strongest effect on any specific types of crashes.MethodWe analyze the relative effect of different stages of GDL on teenage fatal and injury crash risk via a negative binomial generalized linear model with random state effects. Overall, nighttime, and crashes with multiple teenage passengers are considered.ResultsThe strongest effects are seen by 16-year-olds, for which a strict permit stage is associated with a 58% reduction in fatal crash risk over a lenient permit stage. Similar reductions are seen for injury crashes. The intermediate stage, involving nighttime and passenger restrictions, is associated with a 44% reduction in fatalities but has relatively little effect on injury crashes. The strongest effects are generally seen for passenger crashes, followed by nighttime, and then overall crashes.Impact on IndustryThis study identifies stronger relationships between GDL and crash risk than has previously been discovered and captures the relative effects of permit and intermediate licensing restrictions, two high-level components of GDL which differ in intent and implementation.     

Analysis of the injury severity of crashes by considering different lighting conditions on two-lane rural roads

IntroductionMany studies have examined different factors contributing to the injury severity of crashes; however, relatively few studies have focused on the crashes by considering the specific effects of lighting conditions. This research investigates lighting condition differences in the injury severity of crashes using 3-year (2009–2011) crash data of two-lane rural roads of the state of Washington.MethodSeparate ordered-probit models were developed to predict the effects of a set of factors expected to influence injury severity in three lighting conditions; daylight, dark, and dark with street lights. A series of likelihood ratio tests were conducted to determine if these lighting condition models were justified.ResultsThe modeling results suggest that injury severity in specific lighting conditions are associated with contributing factors in different ways, and that such differences cannot be uncovered by focusing merely on one aggregate model. Key differences include crash location, speed limit, shoulder width, driver action, and three collision types (head-on, rear-end, and right-side impact collisions).Practical ApplicationsThis paper highlights the importance of deploying street lights at and near intersections (or access points) on two-lane rural roads because injury severity highly increases when crashes occur at these points in dark conditions.     

Pedestrian falls: A review of the literature and future research directions

IntroductionPedestrian falls (PFs) – falls in public spaces without collisions with other road users – are a significant cause of serious transport-related injuries, amounting to three-quarters of all pedestrians admitted to hospital.MethodsThis scoping review examined peer-reviewed research on PFs published between 1995 and 2015. Electronic databases (Scopus, SafetyLit, and PubMed) were used to find studies identifying PFs or outdoor falls (the latter also including falls in gardens).ResultsWe identified only 28 studies reporting relevant information on PFs (i.e., 15 prospective, 10 retrospective, and 3 intervention studies). The results show that more walking is related to a lower risk of PFs. Older people, especially older women, have a higher risk of (injurious) PFs. Outdoor fall victims have equally good or better health characteristics and scores on balance tests compared to those who have not experienced such falls. Road factors such as uneven surfaces, busy junctions, stairs, and slippery surfaces seem to play an important role in PFs, but much of the research on these factors is of a qualitative nature.ConclusionsPF victims are generally in good health (apart from normal age-related problems) but at risk due to road factors.Practical applicationsWe recommend to adopt a human factors approach. The road system should be adapted to human capabilities and limitations including those of pedestrians. Measures such as preventing uneven surfaces and good winter maintenance seem to be effective. However, we advise more quantitative research on road factors to inform design guidelines and standards for public space authorities given the qualitative nature of current research on road factors.     

An examination of the increases in pedestrian motor-vehicle crash fatalities during 2009–2016

IntroductionPedestrian fatalities increased 46% in the United States during 2009–2016. This study identified circumstances under which the largest increases in deaths occurred during this period.MethodAnnual counts of U.S. pedestrian fatalities and crash involvements were extracted from the Fatality Analysis Reporting System and General Estimates System. Poisson regression examined if pedestrian fatalities by various roadway, environmental, personal, and vehicle factors changed significantly during 2009–2016. Linear regression examined changes over the study period in pedestrian deaths per 100 crash involvements and in horsepower per 1000 pounds of weight among passenger vehicles involved in fatal single-vehicle pedestrian crashesResultsPedestrian deaths per 100 crash involvements increased 29% from 2010, when they reached their lowest point, to 2015, the most recent year for which crash involvement data were available. The largest increases in pedestrian deaths during 2009–2016 occurred in urban areas (54% increase from 2009 to 2016), on arterials (67% increase), at nonintersections (50% increase), and in dark conditions (56% increase). The rise in the number of SUVs involved in fatal single-vehicle pedestrian crashes (82% increase) was larger than the increases in the number of cars, vans, pickups, or medium/heavy trucks involved in these crashes. The power of passenger vehicles involved in fatal single-vehicle pedestrian crashes increased over the study period, with larger increases in vehicle power among more powerful vehicles.ConclusionsEfforts to turn back the recent increase in pedestrian fatalities should focus on the conditions where the rise has been the greatest.Practical applicationsTransportation agencies can improve urban arterials by investing in proven countermeasures, such as road diets, median crossing islands, pedestrian hybrid beacons, and automated speed enforcement. Better road lighting and vehicle headlights could improve pedestrian visibility at night.     

Analysis of hourly crash likelihood using unbalanced panel data mixed logit model and real-time driving environmental big data

IntroductionDriving environment, including road surface conditions and traffic states, often changes over time and influences crash probability considerably. It becomes stretched for traditional crash frequency models developed in large temporal scales to capture the time-varying characteristics of these factors, which may cause substantial loss of critical driving environmental information on crash prediction.MethodCrash prediction models with refined temporal data (hourly records) are developed to characterize the time-varying nature of these contributing factors. Unbalanced panel data mixed logit models are developed to analyze hourly crash likelihood of highway segments. The refined temporal driving environmental data, including road surface and traffic condition, obtained from the Road Weather Information System (RWIS), are incorporated into the models.ResultsModel estimation results indicate that the traffic speed, traffic volume, curvature and chemically wet road surface indicator are better modeled as random parameters. The estimation results of the mixed logit models based on unbalanced panel data show that there are a number of factors related to crash likelihood on I-25. Specifically, weekend indicator, November indicator, low speed limit and long remaining service life of rutting indicator are found to increase crash likelihood, while 5-am indicator and number of merging ramps per lane per mile are found to decrease crash likelihood.ConclusionsThe study underscores and confirms the unique and significant impacts on crash imposed by the real-time weather, road surface, and traffic conditions. With the unbalanced panel data structure, the rich information from real-time driving environmental big data can be well incorporated.     

Predicting reduced visibility related crashes on freeways using real-time traffic flow data

ObjectivesThe main objective of this paper is to investigate whether real-time traffic flow data, collected from loop detectors and radar sensors on freeways, can be used to predict crashes occurring at reduced visibility conditions. In addition, it examines the difference between significant factors associated with reduced visibility related crashes to those factors correlated with crashes occurring at clear visibility conditions.MethodRandom Forests and matched case-control logistic regression models were estimated.ResultsThe findings indicated that real-time traffic variables can be used to predict visibility related crashes on freeways. The results showed that about 69% of reduced visibility related crashes were correctly identified. The results also indicated that traffic flow variables leading to visibility related crashes are slightly different from those variables leading to clear visibility crashes.Impact on IndustryUsing time slices 5–15 minutes before crashes might provide an opportunity for the appropriate traffic management centers for a proactive intervention to reduce crash risk in real-time.     

Understanding factors associated with misclassification of fatigue-related accidents in police record

IntroductionFatigue is one of the riskiest causes of traffic accidents threatening road safety. Due to lack of proper criteria, the identification of fatigue-related accidents by police officers largely depends on inferential evidence and their own experience. As a result, many fatigue-related accidents are misclassified and the harmfulness of fatigue on road safety is misestimated.MethodIn this paper, a joint model framework is introduced to analyze factors contributing to misclassification of a fatigue-related accident in police reports. Association rule data mining technique is employed to identify the potential interactions of factors, and logistic regression models are applied to analyze factors that hinder police officers' identification of fatigue-related accidents. Using the fatigue-related crash records from Guangdong Province during 2005–2014, factors contributing to the false positive and false negative detection of the fatigue-related accident have been identified and compared.ResultsSome variables and interactions were identified to have significant impacts on fatigue-related accident detection.ConclusionsBased on the results, it can be inferred that the stereotype of certain groups of drivers, crash types, and roadway conditions affects police officers' judgment on fatigue-related accidents.Practical applicationsThis finding can provide useful information for training police officers and build better criteria for fatigue identification.