Severity of e-scooter rider injuries associated with trip characteristics

Introduction: E-scooter rider injuries have been growing, but little is known about how trip and incident characteristics contribute to their severity. Method: We enrolled 105 adults injured while riding e-scooters who presented to an emergency department in Washington, DC, during 2019. Enrolled participants completed an interview during the emergency department visit, and their charts were abstracted to document their injuries and treatment. Logistic regression examined the association of incident location and circumstances with the likelihood of sustaining an injury on the Abbreviated Injury Scale (AIS) ≥ 2, while controlling for rider characteristics. Results: The most common locations of e-scooter injuries in our study sample occurred on the sidewalk (58%) or road (23%). Accounting for other trip and rider attributes, e-scooter riders injured on the road were about twice as likely as those injured elsewhere to sustain AIS ≥ 2 injuries (RR, 1.96; 95% CI, 1.23–2.36) and those who rode at least weekly more often sustained AIS ≥ 2 injuries compared with less frequent riders (RR, 1.86; 95% CI, 1.11–2.32). Conclusions: Greater injury severity for riders injured on the road may reflect higher travel speeds. Practical applications: Injury severity associated with riding in the road is one factor that jurisdictions can consider when setting policy on where e-scooters should be encouraged to ride, but the risk of any crash or fall associated with facilities should also be examined. Although injuries are of lower severity on sidewalks, sharing sidewalks with slower moving pedestrians could potentially lead to more conflicts.     

Why are powered two wheeler riders still fatally injured in road junction crashes? – A causation analysis

Introduction: Powered Two Wheeler (PTW) crashes continue to be a road safety concern with a plateauing of the number of associated fatalities. Method: Forty one UK fatal or serious injury crashes involving a PTW and another vehicle at a junction were examined. Crash causation was analysed using the Driver Reliability and Error Analysis Method (DREAMv3.2). Crashes were split into two groups: Group A, where the other vehicle was travelling in the opposite direction to the PTW and commenced a right turn across the PTW’s path; and Group B where the other vehicle turned right out of a side road (or entrance) across the PTW’s path. Results: Overall, the factor that led directly to the crash (phenotype) was most commonly ‘too high speed’ or ‘too late action’ for the motorcyclist and ‘too early action’ for the other driver. Missed or late observations were contributory factors for both PTW riders and other vehicle drivers. Some differences between groups were observed with the PTW riders in Group B more likely to have ‘insufficient skills’ and the other vehicle drivers in Group A more likely to have ‘attention allocation’ as a causation factor. For both groups the crashes occurred because the other vehicle failed to give way to the PTW with causation chains that suggest ‘looked but failed to see’ is still an issue in this type of crash. The excessive speed of the PTW contributed to some crashes. Conclusions: This analysis suggests that drivers failing to give way to PTW riders at junctions is still a problem. This may relate to the ‘looked but did not see’ phenomenon. Causation differences were observed between the examined groups. Practical considerations: The DREAM methodology is an effective tool in analysing crash data from police collision investigation reports. Different countermeasures may be necessary to prevent different types of junction crashes.     

A multinomial logit model of motorcycle crash severity at Australian intersections

Introduction: Motorcyclists are exposed to more fatalities and severe injuries per mile of travel as compared to other vehicle drivers. Moreover, crashes that take place at intersections are more likely to result in serious or fatal injuries as compared to those that occur at non-intersections. Therefore, the purpose of this study is to evaluate the contributing factors to motorcycle crash severity at intersections. Method: A data set of 7,714 motorcycle crashes at intersections in the State of Victoria, Australia was analyzed over the period of 2006–2018. The multinomial logit model was used for evaluating the motorcycle crashes. The severity of motorcycle crashes was divided into three categories: minor injury, serious injury and fatal injury. The risk factors consisted of four major categories: motorcyclist characteristics, environmental characteristics, intersection characteristics and crash characteristics. Results: The results of the model demonstrated that certain factors increased the probability of fatal injuries. These factors were: motorcyclists aged over 59 years, weekend crashes, midnight/early morning crashes, morning rush hours crashes, multiple vehicles involved in the crash, t-intersections, crashes in towns, crashes in rural areas, stop or give-way intersections, roundabouts, and uncontrolled intersections. By contrast, factors such as female motorcyclists, snowy or stormy or foggy weather, rainy weather, evening rush hours crashes, and unpaved roads reduced the probability of fatal injuries. Practical Applications: The results from our study demonstrated that certain treatment measures for t-intersections may reduce the probability of fatal injuries. An effective way for improving the safety of stop or give-way intersections and uncontrolled intersections could be to convert them to all-way stop controls. Further, it is recommended to educate the older riders that with ageing, there are physiological changes that occur within the body which can increase both crash likelihood and injury severity.     

Crash risk factors for interstate large trucks in North Carolina

Introduction: Provide an updated examination of risk factors for large truck involvements in crashes resulting in injury or death. Methods: A matched case–control study was conducted in North Carolina of large trucks operated by interstate carriers. Cases were defined as trucks involved in crashes resulting in fatal or non-fatal injury, and one control truck was matched on the basis of location, weekday, time of day, and truck type. The matched-pair odds ratio provided an estimate of the effect of various driver, vehicle, or carrier factors. Results: Out-of-service (OOS) brake violations tripled the risk of crashing; any OOS vehicle defect increased crash risk by 362%. Higher historical crash rates (fatal, injury, or all crashes) of the carrier were associated with increased risk of crashing. Operating on a short-haul exemption increased crash risk by 383%. Antilock braking systems reduced crash risk by 65%. All of these results were statistically significant at the 95% confidence level. Other safety technologies also showed estimated benefits, although not statistically significant. Conclusions: With the exception of the finding that short-haul exemption is associated with increased crash risk, results largely bolster what is currently known about large truck crash risk and reinforce current enforcement practices. Results also suggest vehicle safety technologies can be important in lowering crash risk. This means that as safety technology continues to penetrate the fleet, whether from voluntary usage or government mandates, reductions in large truck crashes may be achieved. Practical application: Results imply that increased enforcement and use of crash avoidance technologies can improve the large truck crash problem.     

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.     

Crash risk factors for novice motorcycle riders

Background: Motorcycle riders have the highest injury and fatality rates among all road users. This research sought in-depth understanding of crash risk factors to help in developing targeted measures to reduce motorcycle crash injuries and fatalities. Methods: We used interview data from a study of 2,399 novice motorcycle riders in Victoria, Australia from 2010 to 2012 linked with their police-recorded crash and offence data. The outcome measure was self and/or police reported crash. The association between potential risk factors and crashes was explored in multivariable logistic regression models. Results: In the multivariable analysis, riders who reported being involved in three or more near crashes had 1.74 times (95% CI 1.11–2.74) higher odds of crashing compared to riders who reported no near-crash events, and riders who participated in a pre-learner course had 1.41 times higher odds of crashing (95% CI 1.07–1.87) compared with riders who did not attend a pre-learner course. Riders who had been involved in a crash before the study had 1.58 times (95% CI 1.14–2.19) higher odds of crashing during the study period compared with riders who were not involved in a crash. Each additional month of having held a license and learner permit decreased the odds of crashing by 2%, and each additional 1,000 km of riding before the study increased the odds of crashing by 2%. Conclusion: Measures of pre-learner training and riding experience were the strongest predictors of crashing in this cohort of novice motorcycle riders. At the time of the study there was no compulsory rider training to obtain a learner permit in Victoria and no on-road courses were available. It may be plausible that riders who voluntarily participated in an unregulated pre-learner course became or remained at high risk of crash after obtaining a rider license. We suggest systematically reviewing the safety benefits of voluntary versus mandatory pre-learner and learner courses and the potential need to include on-road components.     

Mobile phone use while riding a motorcycle and crashes among university students

Objective: Motorcycle crashes are a significant road safety challenge, particularly in many low- and middle-income countries where motorcycles represent the vast majority of their vehicle fleet. Though risky riding behaviors, such as speeding and riding under the influence of alcohol, have been identified as important contributors to motorcycle crashes, little is understood about the effect of using a mobile phone while riding on motorcycle crash involvement. This article investigates crash involvement among motorcycle riders with risky riding behaviors, particularly using a mobile phone while riding.

Methods: Data were obtained from an online survey of university students’ risky riding behaviors in Vietnam administered between March and May 2016 (n?=?665).

Results: Results show that 40% of motorcycle riders reported to have experienced a crash/fall and nearly 24% of motorcycle riders indicated that they had been injured in a crash/fall. Effects of mobile phone use while riding on safety of motorcycle riders are highlighted. Specifically, more frequent use of a mobile phone for texting or searching for information while riding is associated with a higher chance of being involved in a crash/fall. The results also show that drink riding is associated with a higher chance of being injured.

Conclusions: Overall this article reveals significant safety issues of using a mobile phone while riding a motorcycle, providing valuable insight for designing education and publicity campaigns.     

Injury-severity analysis of lane change crashes involving commercial motor vehicles on interstate highways

Introduction: One of the challenging tasks for drivers is the ability to change lanes around large commercial motor vehicles. Lane changing is often characterized by speed, and crashes that occur due to unsafe lane changes can have serious consequences. Considering the economic importance of commercial trucks, ensuring the safety, security, and resilience of freight transportation is of paramount concern to the United States Department of Transportation and other stakeholders. Method: In this study, a mixed (random parameters) logit model was developed to better understand the relationship between crash factors and associated injury severities of commercial vehicle crashes involving lane change on interstate highways. The study was based on 2009–2016 crash data from Alabama. Results: Preliminary data analysis showed that about 4% of the observed crashes were major injury crashes and drivers of commercial motor vehicles were at-fault in more than half of the crashes. Acknowledging potential crash data limitations, the model estimation results reveal that there is increased probability of major injury when lane change crashes occurred on dark unlit portions of interstates and involve older drivers, at-fault commercial vehicle drivers, and female drivers. The results further show that lane change crashes that occurred on interstates with higher number of travel lanes were less likely to have major injury outcomes. Practical Applications: These findings can help policy makers and state transportation agencies increase awareness on the hazards of changing lanes in the immediate vicinity and driving in the blind spots of large commercial motor vehicles. Additionally, law enforcement efforts may be intensified during times and locations of increased unsafe lane changing activities. These findings may also be useful in commercial vehicle driver training and driver licensing programs.     

Drivers who don't comply with a minimum passing distance rule when passing bicycle riders

Introduction: Drivers' passing cyclists closely can contribute to crashes, falls, and intimidation, which may discourage cycling. In response, minimum passing distance (MPD) rules have been introduced in many jurisdictions. This study examined the factors associated with non-compliance with a MPD rule. Method: An online survey of 3,769 drivers in Queensland, Australia was administered 1 year after a MPD rule began. It assessed compliance with and attitudes toward the rule. Linear regression modeling was used to examine which attitudinal and demographic factors were associated with non-compliance. Results: The percentage of drivers who reported that they did not comply with the road rule “most of the time” or “almost always” was 35.5% in speed zones of ≤ 60 km/h and 31.8% in speed zones of > 60 km/h. Associated with a greater likelihood of being non-compliant were: only infrequently observing motorists giving bicycle riders more distance when overtaking; greater awareness of bicycle riders when driving on the road; disagreeing that the rule had changed the person's driving; agreeing that the rule was making overtaking bicycle riders difficult; disagreeing that the rule had made it safer for bicycle riders; agreeing that it was difficult to judge 1 or 1.5 m when overtaking a bicycle rider; and agreeing that giving 1.5 m clearance in > 60 km/h zones to bicycle riders was annoying (p < .05). In high speed zones, drivers aged 18–39 years were more likely than those aged 50 + years to be non-compliant (p < .05). Compliance was not associated with driver sex, amount of driving, or perceived level of enforcement. Conclusions: Reported non-compliance with the MPD rule is widespread and is related more to attitudinal than demographic factors. Practical applications: Strategies for helping drivers to judge passing distance and improve their understanding of the importance for cyclist safety of leaving an adequate distance are needed.     

Highway safety assessment and improvement through crash prediction by injury severity and vehicle damage using Multivariate Poisson-Lognormal model and Joint Negative Binomial-Generalized Ordered Probit Fractional Split model

Introduction: Predicting crash counts by severity plays a dominant role in identifying roadway sites that experience overrepresented crashes, or an increase in the potential for crashes with higher severity levels. Valid and reliable methodologies for predicting highway accidents by severity are necessary in assessing contributing factors to severe highway crashes, and assisting the practitioners in allocating safety improvement resources. Methods: This paper uses urban and suburban intersection data in Connecticut, along with two sophisticated modeling approaches, i.e. a Multivariate Poisson-Lognormal (MVPLN) model and a Joint Negative Binomial-Generalized Ordered Probit Fractional Split (NB-GOPFS) model to assess the methodological rationality and accuracy by accommodating for the unobserved factors in predicting crash counts by severity level. Furthermore, crash prediction models based on vehicle damage level are estimated using the same two methodologies to supplement the injury severity in estimating crashes by severity when the sample mean of severe injury crashes (e.g., fatal crashes) is very low. Results: The model estimation results highlight the presence of correlations of crash counts among severity levels, as well as the crash counts in total and crash proportions by different severity levels. A comparison of results indicates that injury severity and vehicle damage are highly consistent. Conclusions: Crash severity counts are significantly correlated and should be accommodated in crash prediction models. Practical application: The findings of this research could help select sound and reliable methodologies for predicting highway accidents by injury severity. When crash data samples have challenges associated with the low observed sampling rates for severe injury crashes, this research also confirmed that vehicle damage can be appropriate as an alternative to injury severity in crash prediction by severity.     

Continued trends in older driver crash involvement rates in the United States: Data through 2017–2018

Introduction: With the growing older adult population due to the aging baby-boom cohort, there was concern that increases in fatal motor-vehicle crashes would follow. Yet, previous analyses showed this to be untrue. The purpose of this study was to examine current trends to determine if previous declines have persisted or risen with the recent increase in fatalities nationwide. Methods: Trends among drivers ages 70 and older were compared with drivers 35–54 for U.S. passenger vehicle fatal crash involvements per 100,000 licensed drivers from 1997 to 2018, fatal and all police-reported crash involvements per vehicle miles traveled using the 1995, 2001, 2009, and 2017 National Household Travel Surveys, and driver deaths per 1,000 crashes. Results: Since the mid-1990s, fatal crashes per licensed driver trended downward, with greater declines for drivers ages 70 and older than for middle-aged drivers (43% vs. 21%). Fatal crash rates per 100,000 licensed drivers and police-reported crash rates per mile traveled for drivers ages 70–79 are now less than those for drivers ages 35–54, but their fatal crash rates per mile traveled and risk of dying in a crash remain higher as they drive fewer miles. As the economy improved over the past decade, fatal crash rates increased substantially for middle-aged drivers but decreased or remained stable among older driver age groups. Conclusions: Fatal crash involvements for adults ages 70 and older has recently increased, but they remain down from their 1997 peak, even as the number of licensed older drivers and the miles they drive have increased. Health improvements likely contributed to long-term reductions in fatal crash rates. As older drivers adopt vehicles with improved crashworthiness and safety features, crash survivability will improve. Practical Application: Older adults should feel confident that their independent mobility needs pose less risk than previously expected.     

Ordered logistic models of influencing factors on crash injury severity of single and multiple-vehicle downgrade crashes: A case study in Wyoming

Introduction: The state of Wyoming, like other western United States, is characterized by mountainous terrain. Such terrain is well noted for its severe downgrades and difficult geometry. Given the specific challenges of driving in such difficult terrain, crashes with severe injuries are bound to occur. The literature is replete with research about factors that influence crash injury severity under different conditions. Differences in geometric characteristics of downgrades and mechanics of vehicle operations on such sections mean different factors may be at play in impacting crash severity in contrast to straight, level roadway sections. However, the impact of downgrades on injury severity has not been fully explored in the literature. This study is thus an attempt to fill this research gap. In this paper, an investigation was carried out to determine the influencing factors of crash injury severities of downgrade crashes. Method: Due to the ordered nature of the response variable, the ordered logit model was chosen to investigate the influencing factors of crash injury severities of downgrade crashes. The model was calibrated separately for single and multiple-vehicle crashes to ensure the different factors influencing both types of crashes were captured. Results: The parameter estimates were as expected and mostly had signs consistent with engineering intuition. The results of the ordered model for single-vehicle crashes indicated that alcohol, gender, road condition, vehicle type, point of impact, vehicle maneuver, safety equipment use, driver action, and annual average daily traffic (AADT) per lane all impacted the injury severity of downgrade crashes. Safety equipment use, lighting conditions, posted speed limit, and lane width were also found to be significant factors influencing multiple-vehicle downgrade crashes. Injury severity probability plots were included as part of the study to provide a pictorial representation of how some of the variables change in response to each level of crash injury severity. Conclusion: Overall, this study provides insights into contributory factors of downgrade crashes. The literature review indicated that there are substantial differences between single- and multiple vehicle crashes. This was confirmed by the analysis which showed that mostly, separate factors impacted the crash injury severity of the two crash types. Practical applications: The results of this study could be used by policy makers, in other locations, to reduce downgrade crashes in mountainous areas.     

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

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

Severity and patterns of injury in helmeted vs. non-helmeted motorcyclists in a rural state

Introduction: Under current law in our rural state, there is no universal requirement for motorcyclists to wear helmets. Roughly 500 motorcycle crashes are reported by the state each year and only a fraction of those riders wear helmets. We sought to determine the difference in injury patterns and severity in helmeted versus non-helmeted riders. Methods: Retrospective review (2014–2018) of a single level 1 trauma center’s registry was done for subjects admitted after a motorcycle collision. Demographic, injury and patient outcome data were collected. Patients were stratified by helmet use (n = 81), no helmet use (n = 144), and unknown helmet use (n = 194). Statistical analysis used Student’s t-test or Pearson’s χ² p-value ≤0.05 as significant. State Department of Transportation data registry for state level mortality and collision incidence over the same time period was also obtained. Results: Of the 2,022 state-reported motorcycle collisions, 419 individuals admitted to our trauma center were analyzed (21% capture). State-reported field fatality rate regardless of helmet use was 4%. Our inpatient mortality rate was 2% with no differences between helmet uses. Helmeted riders were found to have significantly fewer head and face injuries, higher GCS, lower face, neck, thorax and abdomen AIS, fewer required mechanical ventilation, shorter ICU length of stay, and had a greater number of upper extremity injuries and higher upper extremity AIS. Conclusions: Helmeted motorcyclists have fewer head, face, and cervical spine injuries, and lower injury severities: GCS and face, neck, thorax, abdomen AIS. Helmeted riders had significantly less mechanical ventilation requirement and shorter ICU stays. Non-helmeted riders sustained worse injuries. Practical Applications: Helmets provide safety and motorcycle riders have a 34-fold higher risk of death following a crash. Evaluating injury severities and patterns in motorcycle crash victims in a rural state with no helmet laws may provide insight into changing current legislation.     

Do NHTSA vehicle safety ratings affect side impact crash outcomes?

Introduction: Side impact crash injuries tend to be severe, mainly due to the effects of the mechanism of such crashes. This study addresses the relationship between side impact crash injury severities and side impact safety ratings of the passenger cars involved in such crashes. It is motivated by the lack of research on side impact safety ratings in relation to the real-world crash outcomes. Method: Analysis of Crashworthiness Data System’s (CDS) data show the head and thorax are the most common regions of impact of severe injuries, while the neck is the least. Irrespective of body regions, higher-rated vehicles were found to provide better occupant protection to both younger and older driver age groups. Assessment based on injury severity score (ISS) indicates that higher-rated vehicles have an overall lower average ISS compared to lower-rated vehicles. Results: Ultimately, this study shows that vehicles rated with National Highway Traffic Safety Administration’s (NHTSA) new criteria had lower average ISS compared to vehicles rated under the old criteria. The 2011 NHTSA side impact rating criteria being relatively new, it has very few crashes to draw meaningful statistically significant conclusions. However, this paper establishes the fact that vehicles with higher star ratings (under experimental conditions) indeed offer increased occupant protection in the field conditions. Practical applications: Previous studies have found that safety was given priority while buying new vehicles. However, people associated vehicle safety with technologies and specific safety features rather than the vehicle’s crash test results or ratings (Koppel, Charlton, Fildes, & Fitzharris, 2008). The results from this study provide a point of reference for safety advocates to educate the drivers about the importance of considering vehicle safety ratings during a vehicle purchase.     

Influence of built environment and roadway characteristics on the frequency of vehicle crashes caused by driver inattention: A comparison between rural roads and urban roads

Introduction: With prevalent and increased attention to driver inattention (DI) behavior, this research provides a comprehensive investigation of the influence of built environment and roadway characteristics on the DI-related vehicle crash frequency per year. Specifically, a comparative analysis between DI-related crash frequency in rural road segments and urban road segments is conducted. Method: Utilizing DI-related crash data collected from North Carolina for the period 2013–2017, three types of models: (1) Poisson/negative binomial (NB) model, (2) Poisson hurdle (HP) model/negative binomial hurdle (HNB) model, and (3) random intercepts Poisson hurdle (RIHP) model/random intercepts negative binomial hurdle (RIHNB) model, are applied to handle excessive zeros and unobserved heterogeneity in the dataset. Results: The results show that RIHP and RIHNB models distinctly outperform other models in terms of goodness-of-fit. The presence of commercial areas is found to increase the probability and frequency of DI-related crashes in both rural and urban regions. Roadway characteristics (such as non-freeways, segments with multiple lanes, and traffic signals) are positively associated with increased DI-related crash counts, whereas state-secondary routes and speed limits (higher than 35 mph) are associated with decreased DI-related crash counts in rural and urban regions. Besides, horizontal curved and longitudinal bottomed segments and segments with double yellow lines/no passing zones are likely to have fewer DI-related crashes in urban areas. Medians in rural road segments are found to be effective to reduce DI-related crashes. Practical Applications: These findings provide a valuable understanding of the DI-related crash frequency for transportation agencies to propose effective countermeasures and safety treatments (e.g., dispatching more police enforcement or surveillance cameras in commercial areas, and setting more medians in rural roads) to mitigate the negative consequences of DI behavior.     

Risk factors affecting crash injury severity for different groups of e-bike riders: A classification tree-based logistic regression model

IntroductionAs a convenient and affordable means of transportation, the e-bike is widely used by different age rider groups and for different travel purposes. The underlying reasons for e-bike riders suffering from severe injury may be different in each case.MethodThis study aims to examine the underlying risk factors of severe injury for different groups of e-bike riders by using a combined method, integration of a classification tree and a logistic regression model. Three-year of e-bike crashes occurring in Hunan province are extracted, and risk factor including rider’s attribute, opponent vehicle and driver’s attribute, improper behaviors of riders and drivers, road, and environment characteristics are considered for this analysis.ResultsE-bike riders are segmented into five groups based on the classification tree analysis, and the group of non-occupational riders aged over 55 in urban regions is associated with the highest likelihood of severe injury among the five groups. The logistics analysis for each group shows that several risk factors such as high-speed roads have commonly significant effects on injury severity for different groups; while major factors only have significant effects for specific groups.Practical applicationBased on model results, policy implications to alleviate the crash injury for different e-bike riders groups are recommended, which mainly include enhanced education and enforcement for e-bike risky behaviors, and traffic engineering to regulate the use of e-bikes on high speed roads.     

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.