The relationship between visibility aid use and motor vehicle related injuries among bicyclists presenting to emergency departments

Integrating visual factors in crash rate analysis at Intersections: An AutoML and SHAP approach towards cycling safety

Cycling crashes constitute a significant and rising share of traffic accidents. Consequently, exploring factors affecting cycling safety has become a priority for both governmental bodies and scholars. However, most existing studies have neglected the vision factors capable of quantitatively describing the city-level cycling environment. Moreover, they have relied on limited models that lack interpretability and fail to capture the spatial variations in the contribution of factors. To address these gaps, this research proposed a framework that used origin-destination-based cycling flow and vision factors generated from Google Street View images to identify the leading factors. It also employed the comparative Automatic Machine Learning and interpretable SHAP value-based geospatial analysis to explain each factor's contribution to the cycling crash risk, with a particular focus on the spatial variations in the influence of vision factors. The effectiveness of this framework was validated by a case study in Manhattan, which examined the leading risk factors of cycling crash rates at intersections. The results showed that the LightGBM model, with selected subsets of factors, outperformed other models. Through SHAP explanations of global feature importance, the study identified the proportion of road barriers, the proportion of open sky, and the number of visible trucks as the leading visual risk factors. Additionally, using SHAP-based geospatial analysis, the study revealed the local variations in the effects of these three factors and identified eight areas with higher cycling crash rates. Based on these findings, the study provided practical measures for a safer cycling environment in Manhattan.

Effectiveness of road safety interventions: An evidence and gap map

Background

Road Traffic injuries (RTI) are among the top ten leading causes of death in the world resulting in 1.35 million deaths every year, about 93% of which occur in low- and middle-income countries (LMICs). Despite several global resolutions to reduce traffic injuries, they have continued to grow in many countries. Many high-income countries have successfully reduced RTI by using a public health approach and implementing evidence-based interventions. As many LMICs develop their highway infrastructure, adopting a similar scientific approach towards road safety is crucial. The evidence also needs to be evaluated to assess external validity because measures that have worked in high-income countries may not translate equally well to other contexts. An evidence gap map for RTI is the first step towards understanding what evidence is available, from where, and the key gaps in knowledge.

Objectives

The objective of this evidence gap map (EGM) is to identify existing evidence from all effectiveness studies and systematic reviews related to road safety interventions. In addition, the EGM identifies gaps in evidence where new primary studies and systematic reviews could add value. This will help direct future research and discussions based on systematic evidence towards the approaches and interventions which are most effective in the road safety sector. This could enable the generation of evidence for informing policy at global, regional or national levels.

Search Methods

The EGM includes systematic reviews and impact evaluations assessing the effect of interventions for RTI reported in academic databases, organization websites, and grey literature sources. The studies were searched up to December 2019.

Selection Criteria

The interventions were divided into five broad categories: (a) human factors (e.g., enforcement or road user education), (b) road design, infrastructure and traffic control, (c) legal and institutional framework, (d) post-crash pre-hospital care, and (e) vehicle factors (except car design for occupant protection) and protective devices. Included studies reported two primary outcomes: fatal crashes and non-fatal injury crashes; and four intermediate outcomes: change in use of seat belts, change in use of helmets, change in speed, and change in alcohol/drug use. Studies were excluded if they did not report injury or fatality as one of the outcomes.

Data Collection and Analysis

The EGM is presented in the form of a matrix with two primary dimensions: interventions (rows) and outcomes (columns). Additional dimensions are country income groups, region, quality level for systematic reviews, type of study design used (e.g., case-control), type of road user studied (e.g., pedestrian, cyclists), age groups, and road type. The EGM is available online where the matrix of interventions and outcomes can be filtered by one or more dimensions. The webpage includes a bibliography of the selected studies and titles and abstracts available for preview. Quality appraisal for systematic reviews was conducted using a critical appraisal tool for systematic reviews, AMSTAR 2.

Main Results

The EGM identified 1859 studies of which 322 were systematic reviews, 7 were protocol studies and 1530 were impact evaluations. Some studies included more than one intervention, outcome, study method, or study region. The studies were distributed among intervention categories as: human factors (n = 771), road design, infrastructure and traffic control (n = 661), legal and institutional framework (n = 424), post-crash pre-hospital care (n = 118) and vehicle factors and protective devices (n = 111). Fatal crashes as outcomes were reported in 1414 records and non-fatal injury crashes in 1252 records. Among the four intermediate outcomes, speed was most commonly reported (n = 298) followed by alcohol (n = 206), use of seatbelts (n = 167), and use of helmets (n = 66). Ninety-six percent of the studies were reported from high-income countries (HIC), 4.5% from upper-middle-income countries, and only 1.4% from lower-middle and low-income countries. There were 25 systematic reviews of high quality, 4 of moderate quality, and 293 of low quality.

Authors' Conclusions

The EGM shows that the distribution of available road safety evidence is skewed across the world. A vast majority of the literature is from HICs. In contrast, only a small fraction of the literature reports on the many LMICs that are fast expanding their road infrastructure, experiencing rapid changes in traffic patterns, and witnessing growth in road injuries. This bias in literature explains why many interventions that are of high importance in the context of LMICs remain poorly studied. Besides, many interventions that have been tested only in HICs may not work equally effectively in LMICs. Another important finding was that a large majority of systematic reviews are of low quality. The scarcity of evidence on many important interventions and lack of good quality evidence-synthesis have significant implications for future road safety research and practice in LMICs. The EGM presented here will help identify priority areas for researchers, while directing practitioners and policy makers towards proven interventions.

How do bicyclists respond to vehicles with adaptive headlamp systems? A nighttime study in an immersive virtual environment

INTRODUCTION

The risk of motor vehicle-bicyclist crashes and fatalities is greater during nighttime than daytime lighting conditions, even though there are fewer cyclists on roadways at night. Vehicle Adaptive Headlamp Systems (AHS) aim to increase the visibility of bicyclists for drivers by directing a spotlight to illuminate bicyclists on or near the roadway. AHS technology also serves to alert bicyclists to the approaching vehicle by illuminating the road beneath the rider and by projecting a warning icon on the roadway.

METHOD

Here, we examined how bicyclists respond to different AHS designs using a large screen, immersive virtual environment. Participants bicycled along a virtual road during nighttime lighting conditions and were overtaken by vehicles with and without an AHS system. The experiment included five treatment conditions with five different AHS designs. In each design a box of white light was projected beneath the rider; in four of the designs an icon was also projected on the road that varied in color (white or red) and position (to the left of the rider at midline or to the left of the front wheel). Participants in the control condition experienced only non-AHS vehicles.

RESULTS

We found that riders in all AHS treatment conditions moved significantly farther away from overtaking vehicles with AHS systems, whereas riders in the control condition did not significantly move away from overtaking vehicles without AHS systems.

PRACTICAL APPLICATIONS

The experiment demonstrates that AHS has potential to increase bicycling safety by influencing riders to steer away from overtaking vehicles.

Seeing through eyes of Drivers: Space consideration in investigating visibility of Vulnerable road users involved in crashes from Driver's perspective

Vulnerable Road User's (VRUs) invisibility by vehicle drivers hasn't been well explored despite having a substantial influence on crash involvement and resulting severity level. Additionally, obtaining comparison crashes for analysis of the VRU invisibility has been a challenge. For that reason, this study used crashes that occurred between 2017 and 2022 in Ohio to understand VRU invisibility from the driver's perspective. The study further proposes the comparison of crashes as those that occurred within 250 feet of the crashes involving drivers not seeing the VRU. Two logistic regression models, one for the entire dataset (full model) and the second for only crashes that occurred within 250 feet (space-constrained model), were developed. It was found that the results from the full model and space-constrained model differ significantly in terms of the magnitude and the direction of the effect. Using the space-constrained model, the topmost key factors associated with the highest likelihood of VRU invisibility are lighting conditions, pre-action of the driver, and senior VRU involvement. Further, text network analysis was performed to understand the key reasons for VRU invisibility. The text network revealed that the VRU invisibility related to left turning pre-action was due to the driver's failure to yield at an intersection's pedestrian crossing. Further, the most invisible VRUs in the dark conditions were on the side of the roadway. Additionally, drivers backing up were more likely to report that they did not see pedestrians walking behind them. Lastly, senior-related crashes were associated with crossing in front of turning vehicles. The findings can be utilized to enhance VRU visibility at various locations to improve safety.

Caught in the blind spot of a truck: A choice model on driver glance behavior towards cyclists at intersections

Vulnerable road users (VRUs) constitute an increasing proportion of the annual road fatalities across Europe. One of the crash types involved in these fatalities are blind spot crashes between trucks and bicyclists. Despite the presence of mandatory blind spot mirrors, truck drivers are often reported to have overlooked the presence of a bicyclist. This raises the question if and when truck drivers check their blind spot mirrors for the presence of bicyclists, and which factors contribute to such glance behavior. The current study presents the results of an analysis of naturalistic glance behavior by 39 truck drivers in 1,903 right-turning maneuvers at urban intersections, where in each maneuver there was a chance of crossing the path of a bicyclist. The descriptive analysis revealed that most often truck drivers did not cast a glance upon their blind spot mirrors as recommended by the examination guidelines. Furthermore, a choice model was developed with the main factors that have an impact on glance behavior. Drivers were more likely to glance with a priority regulation that allowed conflicts, with lower speed limits, with zebra crossings, without cyclist facilities, without a lead vehicle making the same maneuver, in presence of VRUs, without adverse sight conditions, in lower age groups, without certain non-driving related activities, when driving a truck with more direct vision on VRUs, and without a camera providing a view on the blind spot, and with less time between a standstill and starting the maneuver. Three factors did not significantly improve the choice model and were therefore left out, despite showing significant effects in bivariate tests: intersection layout (e.g., three vs. four legs), presence of advanced stopping lanes, and visual obstruction. Implications of the choice model are discussed for driver education (in terms of timely glances, reducing inattention, and hazard anticipation) and vehicle design (in terms of direct vision).

Evaluating common approaches to improve visibility of wheelchair users

Introduction: Wheelchair users deploy a variety of strategies to increase their visibility to motorists. Unfortunately, there is no empirical evidence supporting the use of any of these visibility strategies with this population.Objectives: To investigate the effectiveness of four different visibility strategies (i.e., light emitting diode (LED) lighting system, reflective safety vest, orange flag, or control (i.e., black clothing)) for wheelchair users in nighttime and daytime conditions.Methods: Participants were shown videos from the driver's perspective depicting a wheelchair user waiting to cross the street while using one of the four visibility strategies in isolation during nighttime and daytime. Participants indicated when, if at all, they first saw the wheelchair user at the roadside. Results were compared to determine which strategy allowed for the longest reaction time.Results: In nighttime conditions, the most effective strategy was the LED lighting system followed by the reflective vest. Neither the orange flag nor black clothing provided an adequate stopping distance when a vehicle was traveling at 32km/h at night. During daytime conditions, all visibility strategies were effective when a vehicle was traveling at this speed.Conclusions: These results will help clinicians and enable wheelchair users to make informed choices about the best visibility strategies to use.

The built environment and active transportation safety in children and youth: a study protocol

BACKGROUND

Active transportation, such as walking and biking, is a healthy way for children to explore their environment and develop independence. However, children can be injured while walking and biking. Many cities make changes to the built environment (e.g., traffic calming features, separated bike lanes) to keep people safe. There is some research on how effective these changes are in preventing adult pedestrians and bicyclists from getting hurt, but very little research has been done to show how safe various environments are for children and youth. Our research program will study how features of the built environment affect whether children travel (e.g., to school) using active modes, and whether certain features increase or decrease their likelihood of injury.

METHODS

First, we will use a cross-sectional study design to estimate associations between objectively measured built environment and objectively measured active transportation to school among child elementary students. We will examine the associations between objectively measured built environment and child and youth pedestrian-motor vehicle collisions (MVCs) and bicyclist-MVCs. We will also use these data to determine the space-time distribution of pedestrian-MVCs and bicyclist-MVCs. Second, we will use a case-crossover design to compare the built environment characteristics of the site where child and youth bicyclists sustain emergency department reported injuries and two randomly selected sites (control sites) along the bicyclist's route before the injury occurred. Third, to identify implementation strategies for built environment change at the municipal level to encourage active transportation we will conduct: 1) an environmental scan, 2) key informant interviews, 3) focus groups, and 4) a national survey to identify facilitators and barriers for implementing built environment change in municipalities. Finally, we will develop a built environment implementation toolkit to promote active transportation and prevent child pedestrian and bicyclist injuries.

DISCUSSION

This program of research will identify the built environment associated with active transportation safety and form an evidence base from which municipalities can draw information to support change. Our team's national scope will be invaluable in providing information regarding the variability in built environment characteristics and is vital to producing evidence-based recommendations that will increase safe active transportation.

Reflective Tape Applied to Bicycle Frame and Conspicuity Enhancement at Night

OBJECTIVE

Four studies were conducted to assess bicyclist conspicuity enhancement at night by the application of reflective tape (ECE/ONU 104) to the bicycle rear frame and to pedal cranks.

BACKGROUND

Previous studies have tested the benefits of reflective markings applied to bicyclist clothing. Reflective jackets however need to be available and worn while reflective markings enhance conspicuity without any active behavior by the bicyclist.

METHOD

In the first study, reflective tape was applied to the rear frame. Detection distance was compared in four conditions: control, rear red reflector, high visibility jacket, and reflective tape. In the second study, the same conditions were studied with night street lighting on and off. In the third study, detection and recognition distances were evaluated in rainy conditions. In the fourth study, visibility was assessed with the reflective tape applied to pedal cranks.

RESULTS

In the first study, the application of reflective markings resulted in a detection distance of 168.28 m. In the second study, the detection distance with reflective markings was 229.74 m with public street light on and 256.41 m with public street light off. In rainy conditions, detection distance using the reflective markings was 146.47 m. Reflective tape applied to pedal cracks resulted in a detection distance of 168.60 m.

CONCLUSION

Reflective tape applied to the rear bicycle frame can considerably increase bicyclist conspicuity and safety at night.

APPLICATION

Reflective tape is highly recommended to complement anterior and rear lights in bicycle riding at night.

Using data mining techniques to predict the severity of bicycle crashes

To investigate the factors predicting severity of bicycle crashes in Italy, we used an observational study of official statistics. We applied two of the most widely used data mining techniques, CHAID decision tree technique and Bayesian network analysis. We used data provided by the Italian National Institute of Statistics on road crashes that occurred on the Italian road network during the period ranging from 2011 to 2013. In the present study, the dataset contains information about road crashes occurred on the Italian road network during the period ranging from 2011 to 2013. We extracted 49,621 road accidents where at least one cyclist was injured or killed from the original database that comprised a total of 575,093 road accidents. CHAID decision tree technique was employed to establish the relationship between severity of bicycle crashes and factors related to crash characteristics (type of collision and opponent vehicle), infrastructure characteristics (type of carriageway, road type, road signage, pavement type, and type of road segment), cyclists (gender and age), and environmental factors (time of the day, day of the week, month, pavement condition, and weather). CHAID analysis revealed that the most important predictors were, in decreasing order of importance, road type (0.30), crash type (0.24), age of cyclist (0.19), road signage (0.08), gender of cyclist (0.07), type of opponent vehicle (0.05), month (0.04), and type of road segment (0.02). These eight most important predictors of the severity of bicycle crashes were included as predictors of the target (i.e., severity of bicycle crashes) in Bayesian network analysis. Bayesian network analysis identified crash type (0.31), road type (0.19), and type of opponent vehicle (0.18) as the most important predictors of severity of bicycle crashes.

Risk Factors for Bicycling Injuries in Children and Adolescents: A Systematic Review

CONTEXT

Child and adolescent bicycling is beneficial, but injuries occur and can be severe and costly.

OBJECTIVE

To systematically review the individual and environmental factors associated with bicycling injury risk in children and adolescents.

DATA SOURCES

Fourteen electronic databases were searched.

STUDY SELECTION

Two authors independently assessed potentially relevant articles for eligibility. The inclusion criteria were as follows: bicyclists younger than 20 years old; examined individual and environmental characteristics of bicycling crashes; compared injured and uninjured bicyclists or bicyclists with different types or severity of injury; study designs with a predetermined comparison group; and published in English from January 1990 to May 2015. The exclusion criteria were outcomes related to helmet use, helmet legislation, or mountain biking, and comparisons of census-based injury rates.

DATA EXTRACTION

Data on study design, setting, population, injury definitions, injury risk factors, and results were extracted. Risk of bias was assessed by using the Newcastle-Ottawa Scales.

RESULTS

Fourteen articles were included. Lower socioeconomic status, riding on the road, riding in rural compared with urban areas, and riding on the sidewalk were associated with bicycling injury. Bicycling safety education did not protect children against future injury. Injuries related to a motor vehicle collision were more severe than other bicycling injuries.

LIMITATIONS

Study heterogeneity prevented meta-analyses. Study quality was affected by inadequate definitions of study groups and self-reported data.

CONCLUSIONS

Lower socioeconomic status and riding location were associated with bicycling injury and severity increased with motor vehicle collisions. The bicycling environment is a promising avenue for prevention.

Mechanisms, injuries and helmet use in cyclists presenting to an inner city emergency department

OBJECTIVES

The objectives of the present study were to describe the injury profiles of cyclists presenting to an ED and determine the risk of significant head injury associated with bicycle helmet use.

METHODS

This was a retrospective single trauma centre study of all adult cyclists presenting to an inner city ED and undergoing a trauma team review between January 2012 and June 2014. The outcome of interest was significant head injury defined as any head injury with an Abbreviated Injury Scale score of two or more. Variables analysed included demographic characteristics, helmet use at time of incident, location, time and the presence of intoxication.

RESULTS

The most common body regions were upper limb injuries (57%), followed by head injuries (43%), facial injuries (30%) and lower limb injuries (24%). A lower proportion of people wearing helmets had significant head injury (17% vs 31%, P = 0.018) or facial injury (26% vs 48%, P = 0.0017) compared with non-helmet users. After adjustment for important covariates, helmet use was associated with a 70% decrease in the odds of significant head injury (odds ratio 0.34, 95% confidence interval 0.15, 0.76, P = 0.008).

CONCLUSIONS

Head injuries were common after inner city cycling incidents. The use of helmets was associated with a reduction in significant head injury.

Severe bicycling injury risk factors in children and adolescents: a case-control study

BACKGROUND

Bicycling is the most common cause of sports and recreation injury in children and adolescents; yet, there is limited evidence on the factors associated with severe bicycling injuries in youth.

METHODS

Case-control study of injured bicyclists less than 18 years old seen in seven emergency departments (EDs) from May 2008 to October 2010. Cases were bicyclists hospitalized after their ED visit (severe injury). Controls were bicyclists seen and discharged from the ED (non-severe injury). Personal, environmental, and crash characteristics were collected by interview. Injury data were collected from medical charts. Crude and adjusted odds ratios (ORs) and 95% confidence intervals (CIs) from logistic regression were used to estimate the odds of hospitalization associated with risk factors. Multiple imputation techniques were employed to address missing data.

RESULTS

There were 1470 participants including 119 cases. Those ages 13-17 had the highest proportion (23%) of severe injuries resulting from motor vehicle [MV] collision. In models including age, sex and MV collision, being male (OR: 2.02; 95% CI: 1.21-3.38), not wearing a helmet (OR: 2.18; 95% CI: 1.43-3.31) and MV collision (OR: 3.91; 95% CI: 2.26-6.78) were significant risk factors for severe injury. Riding on a paved surface (OR: 0.63; 95% CI: 0.41-0.97) and utilitarian (school, work) bicycling (OR: 0.44; 95% CI: 0.2-0.94) decreased injury risk. Results were similar, apart from utilitarian bicycling (OR: 0.49; 95% CI: 0.22-1.06), after imputation for missing data.

CONCLUSION

Bicycle-MV collisions increase severe injury risk in youth, and adolescents are often injured in these events. This suggests separating bicyclists from MVs or traffic calming strategies could improve safety.