Under-reporting bicycle accidents to police in the COST TU1101 international survey: Cross-country comparisons and associated factors

Understanding Cyclists' Visual Behavior Using Eye-Tracking Technology: A Systematic Review

Eye-tracking technologies are emerging in research aiming to understand the visual behavior of cyclists to improve their safety. These technologies gather real-time information to reveal what the cyclists look at and how they respond at a specific location and time. This systematic review investigates the use of eye-tracking systems to improve cyclist safety. An extensive search of the SCOPUS and WoS databases, following the PRISMA 2020 guidelines, found 610 studies published between 2010 and 2024. After filtering these studies according to predefined inclusion and exclusion criteria, 25 were selected for final review. The included studies were conducted in real traffic or virtual environments aiming to assess visual attention, workload, or hazard perception. Studies focusing on other types of road users or participants not involved in active cycling were excluded. Results reveal the important impact of road elements' design, traffic density, and weather conditions on cyclists' gaze patterns. Significant visual workload is imposed mainly by intersections. Along with the valuable insights into cyclist safety, potential biases related to small sample sizes and technological limitations were identified. Recommendations for future research are discussed to address these challenges through more diverse samples, advanced technologies, and a greater focus on peripheral vision.

Utilization of recommended safe-landing strategies during falls in mountain biking

Falls are common in mountain biking (MTB), and often involve high speeds, large descent heights, and rough landing terrains. However, most falls in MTB do not cause serious injury. This may be due, in part, to protective movements used by MTB riders to avoid injury. Such "safe-landing strategies" are commonly discussed in the MTB community. However, studies have not synthesized or examined the validity of the recommended strategies. Our goal in this study was to determine whether riders utilize recommended safe-landing strategies during real-life falls in MTB. To address this goal, we identified 11 recommended safe-landing strategies through online content analysis and experienced MTB rider surveys. We then analyzed videos of 300 real-life MTB falls using a structured questionnaire to determine whether riders utilized the recommended strategies. The most commonly used strategies were upper limb bracing (58.3 %), elbow flexion at landing (48.0 %), stepping (47.0 %), knee flexion at landing (43.0 %), and bike separation (40.0 %). The least utilized strategies were reach-to-grasp (4.7 %), use of the arms to shield the face (6.3 %) and dismounting from the bike (6.7 %). Moderately utilized strategies included body rolling (26.7 %), neck rotation (26.7 %), and tucking (18.3 %). In 96 % of falls, rider utilized at least 1 recommended landing strategy. On average, riders utilized 3.04 (SD 1.6) recommended landing strategies when falling. Our results indicate that falls in MTB elicit common movement strategies that align with recommended techniques for avoiding injury during falls. Future research should examine the role of exercise in enhancing safe-landing responses and preventing injuries in MTB.

Traffic collisions and micromobility: A comparison between personal mobility devices and bicycles based on police reports

INTRODUCTION

The recent increase in the use of bicycles and personal mobility devices (PMDs), including mostly E-scooters, is associated with a rapid rise in injuries. Understanding the main crash scenarios leading to these injuries is essential to evaluate and improve preventive and protective measures, especially for PMDs, which are often equated with bicycles. The objective of this study is to identify and compare the most common two-party collision scenarios for bicycles and PMDs, and to identify factors affecting injury severity.

METHOD

Crashes involving at least one PMD or one bicycle and another road user were analyzed from the 2019-2022 French police-reported road crashes database. We investigated the rider, the other vehicle, the road, and the crash scenarios characteristics (pre-crash maneuvers, impact zone on vehicles) and their joint effect on injury severity (hospitalization or fatality: yes/no).

RESULTS

We included 16,302 bicycle crashes and 4,118 PMD crashes in the analysis. Most of these collisions (75%) were against a car. The most frequent and the most severe collision scenario was the side-on-head for both bicycles (51%) and PMDs (58%); 67% of both bicycles and PMDs were going straight before the collision. Main factors associated with increased injury severity included colliding with a greater size vehicle, age above 50, and riding on roads with a higher speed limit. Bicycles remained at a higher risk of severe injury than PMDs after accounting for adjustment factors.

CONCLUSIONS

Although collision scenarios appear similar for bicycles and PMDs, differences in other crash characteristics and injury severity suggest that these two modes of transportation should not be equated in crash investigations.

PRACTICAL IMPLICATIONS

These findings emphasize the need to primarily investigate side-on-head collisions with a moving car for both PMDs and bicycles in order to develop, evaluate, and improve protective devices to reduce the risk of injuries.

Baseline vulnerable road user injury risk in multiple U.S. dense urban driving environments

OBJECTIVE

Understanding and modeling baseline driving safety risk in dense urban areas represents a crucial starting point for automated driving system (ADS) safety impact analysis. The purpose of this study was to leverage naturalistic vulnerable road user (VRU) collision data to quantify collision rates, crash severity, and injury risk distributions in the absence of objective injury outcome data.

METHODS

From over 500 million vehicle miles traveled, a total of 335 collision events involving VRUs were video verified and reconstructed (126 pedestrians, 144 cyclists, and 65 motorcyclists). Data consisted of anonymized video and sensor data (Global Positioning System and accelerometer) from vehicles equipped with Nexar dash cameras. Each event was qualitatively evaluated to assess the collision geometries and other extrinsic collision factors (e.g., time of day, roadway location, presence of occlusions). Previously published injury risk models were utilized to provide estimates of Maximum Abbreviated Injury Scale MAIS2+ and MAIS3+ injury potential. Collision severity distributions and aggregated injury risk estimates were compared.

RESULTS

Vehicle speeds at the time of impact ranged from 0 to 39 mph, and VRU speeds ranged from 0 to 27 mph. In general, vehicles were traveling at lower speeds at the time of impact when turning in comparison to straight travel. Nearly all events (95%) were associated with MAIS3+ injury risk estimates below 10%. Collisions involving a potential occlusion or the vehicle responding to surprising behavior by the VRU were associated with higher estimates of injury risk than those without occlusion or with the vehicle initiating the conflict. Based on accumulated risk for each event, it can be estimated that approximately 55 persons could be moderately injured (MAIS2+) and approximately 6 persons could be seriously injured (MAIS3+).

CONCLUSION

These data indicate that responding to surprising VRU behavior, having visibility be potentially occluded, and vehicle travel behavior were associated with differences in collision speed and injury risk estimation. The specific comparisons made in this study are not intended to be comprehensive but serve as a starting point for considering baseline driving risk associated with VRU collisions in dense urban areas.

International Classification of Disease based Injury Severity Score (ICISS): a comparison of methodologies applied to linked data from New South Wales, Australia

BACKGROUND

The International Classification of Disease Injury Severity Score (ICISS) provides an efficient method to determine injury severity in hospitalised injury patients. Injury severity metrics are of particular interest for the tracking of road transport injury rates and trends. The aims of this study were to calculate ICISS using linked morbidity and mortality datasets and to compare predictive ability of various methods and metrics.

METHODS

This was a retrospective analysis of Admitted Patient Data Collection records from New South Wales, Australia, linked with mortality data. Using a split sample approach, design data (2008-2014; n=1 035 174 periods of care) was used to derive survival risk ratios and calculate various ICISS scales based on in-hospital death and 3-month death. These scales were applied to testing data (2015-2017; n=575 306). Logistic regression modelling was used to determine model discrimination and calibration.

RESULTS

There were 12 347 (1.19%) in-hospital deaths and 29 275 (2.83%) 3-month deaths in the design data. Model discrimination ranged from acceptable to excellent (area under the curve 0.75-0.88). Serious injury (ICISS≤0.941) rates in the testing data varied, with a range of 10%-31% depending on the methodology. The 'worst injury' ICISS was always superior to 'multiplicative injury' ICISS in model discrimination and calibration.

CONCLUSIONS

In-hospital death and 3-month death were used to generate ICISS; the former is recommended for settings with a focus on short-term threat to life, such as in trauma care settings. The 3-month death approach is recommended for outcomes beyond immediate clinical care, such as injury compensation schemes.

Comparison of single bicycle crashes and collisions among severely injured cyclists-A 16-year analysis based on the Helsinki Trauma Registry (HTR)

OBJECTIVE

The Finnish national Traffic Safety Strategy 2022-2026 seeks to halve the number of road fatalities and serious injuries from 2020 to 2030. The strategy states that better information on bicycle crashes is needed for safety promotion. The aim of this study was to describe the demographics, injury characteristics, alcohol involvement, and helmet use of severely injured cyclists and to compare single bicycle crashes (falling alone or hitting a fixed object) to collisions.

MATERIAL AND METHODS

We identified all bicycle crashes between 2006 and 2021 from the Helsinki Trauma Registry (HTR). Variables analysed were basic patient demographics, Abbreviated Injury Scale (AIS) codes, AIS 3+ injuries, injured body regions, patient Injury Severity Score (ISS) and New Injury Severity Score (NISS), 30-day in-hospital mortality, ICU length of stay, injury mechanism, alcohol use by the injured cyclists, and helmet use.

RESULTS

Of the 325 severe (NISS >15) cycling injury patients in the HTR, 53.5 % were injured in single crashes and 46.5 % in collisions with a moving object. Most (71.4 %) patients were men and mean age of all patients was 54.1 years (SD 16.7). Alcohol was detected in 23.1 % of cases and more often in single crashes (32.8 %) than in collisions (11.9 %). Less than a third (29.2 %) of all cyclists wore a helmet; those who wore a helmet had fewer serious (AIS 3+) head injuries than those who did not. Cyclists injured in collisions had higher ISS and NISS scores than those injured in single crashes. Serious (AIS 3+) injuries in extremities or in pelvic girdle were more common in collisions than in single crashes.

CONCLUSIONS

Among severely injured cyclists, single bicycle crashes were more common; alcohol was more often detected in single bicycle crashes than in collisions. Overall injury severity was higher in collisions than in single crashes. Helmet users had less AIS 3+ head injuries than non-users. Attention should be focused on preventing alcohol-related cycling injuries, promoting use of bicycle helmets, and more precise and comprehensive documentation of bicycle crashes in health care units.

Safe System approach for cyclists in the Netherlands: Towards zero fatalities and serious injuries?

More than one third of all road deaths in the Netherlands and more than two thirds of seriously injured casualties are cyclists. In recent years these shares have increased, despite the fact that the implementation of Safe System principles has been leading in road safety policy and has been successful in reducing the total number of road deaths. However, the annual number of fatalities among cyclists failed to decline and the number of injuries among cyclists has been increasing, especially in single-bicycle crashes. This raises the question why until now Safe System implementation has failed to contribute to the reduction of the number of casualties among cyclists. This question is urgent because of the goal to reduce the number of road deaths and serious traffic injuries in the Netherlands to (virtually) ZERO by 2050. This ambition is in line with the objectives of the European Union. The causes of the unfavourable developments in road safety for cyclists in the Netherlands and which problems require a solution are examined. This raises two questions: can improved implementation of Safe System measures reverse the negative trend, and can this result in ZERO cycling casualties in the future. The discussion involves investigating three dimensions: exposure, crash risk, and injury risk. The opportunities that technological developments may offer in future decades are also considered. It is concluded that Safe System implementation will include opportunities to make cycling considerably safer in the Netherlands. However, we face too many uncertainties to allow for developing scenarios that show how close the Netherlands will be to ZERO cyclists casualties.

Forward dynamics computational modelling of a cyclist fall with the inclusion of protective response using deep learning-based human pose estimation

Single bicycle crashes, i.e., falls and impacts not involving a collision with another road user, are a significantly underestimated road safety problem. The motions and behaviours of falling people, or fall kinematics, are often investigated in the injury biomechanics research field. Understanding the mechanics of a fall can help researchers develop better protective gear and safety measures to reduce the risk of injury. However, little is known about cyclist fall kinematics or dynamics. Therefore, in this study, a video analysis of cyclist falls is performed to investigate common kinematic forms and impact patterns. Furthermore, a pipeline involving deep learning-based human pose estimation and inverse kinematics optimisation is created for extracting human motion from real-world footage of falls to initialise forward dynamics computational human body models. A bracing active response is then optimised for using a genetic algorithm. This is then applied to a case study of a cyclist fall. The kinematic forms characterised in this study can be used to inform initial conditions for computational modelling and injury estimation in cyclist falls. Findings indicate that protective response is an important consideration in fall kinematics and dynamics, and should be included in computational modelling. Furthermore, the novel reconstruction pipeline proposed here can be applied more broadly for traumatic injury biomechanics tasks. The tool developed in this study is available at https://kevgildea.github.io/KinePose/.

Computer vision-based assessment of cyclist-tram track interactions for predictive modeling of crossing success

INTRODUCTION

Single Bicycle Brashes (SBCs) are common, and underreported in official statistics. In urban environments, light rail tram tracks are a frequent factor, however, they have not yet been the subject of engineering analysis.

METHOD

This study employs video-based analysis at nine Dublin city centre locations and introduces a predictive model for crossing success on tram tracks, utilising cyclist crossing angles within a Surrogate Measure of Safety (SMoS) framework. Additionally, Convolutional Neural Networks (CNNs) were explored for automatic estimation of crossing angles.

RESULTS

Modelling results indicate that cyclist crossing angle is a strong predictor of crossing success, and that cyclist velocity is not. Findings also highlight the prevalence of external factors which limit crossing angles for cyclists. In particular, kerbs are a common factor, along with passing/approaching vehicles or other cyclists. Furthermore, results indicate that further training on a relatively small sample of 100 domain-specific examples can achieve substantial accuracy improvements for cyclist detection (from 0.31AP0.5 to 0.98AP0.5) and crossing angle inference from traffic camera footage.

CONCLUSIONS

Ensuring safe crossing angles is important for cyclist safety around tram tracks. Infrastructural planners should aim for intuitive, self-explainable road layouts that allow for and encourage crossing angles of 60° or more - ideally 90°.

PRACTICAL APPLICATIONS

The SMoS framework and the open-source SafeCross1 application offer actionable insights and tools for enhancing cyclist safety around tram tracks.

Development of German pedelec (and bicycle) crashes between 2013 and 2021

INTRODUCTION

The continuous growth in the use of e-bikes (in Germany mostly pedelecs that support pedaling up to 25 km/h) raises questions about the use of historic crash data for the development of road safety measures. The aim of this study was to address this issue, by conducting a longitudinal analysis of pedelec and bicycle crash data over a period of nine years to identify trends and to clarify whether such trends are specific to pedelecs.

METHOD

We analyzed 95,338 police reported pedelec and bicycle injury crashes from 2013 through 2021. The dataset consisted of crashes from three federal states of Germany: Brandenburg, Hesse and Saxony. Data were analyzed with respect to sex and age distribution, time, location and type of accident, conflict partner, cause of crash and injury severity.

RESULTS

Many of the analyzed variables showed a considerable degree of temporal stability, with differences as well as similarities between the two bicycle types staying quite consistent over the years. One notable difference was the mean age of the involved riders, with crashed pedelec riders being significantly older than conventional cyclists. At the same time, however, the mean age of these pedelec riders has decreased by eight years over time. Single vehicle crashes were consistently more common for pedelec riders than for cyclists. Similarly, pedelec rider crashes went with a higher injury severity over all the years.

CONCLUSIONS AND PRACTICAL APPLICATIONS

While, on a more detailed level, we found differences between the two bicycle types, overall crash characteristics were remarkably similar and consistent over time. Our findings provide no clear argument for road safety measures that are specifically designed to target pedelecs. Instead, the stable crash total, and the increases in ridership of both bicycles and pedelecs, highlight the demand for new, innovative solutions to improve cycling safety in general.

Wider view over bicycle crashes: Complementing and extending bicycle crash statistics in urban areas using surveys

INTRODUCTION

In pursuit of sustainability goals, many cities are introducing measures to increase the usage of bicycles as a means of transportation. City planners aim to ensure that this increase does not lead to an increase in crashes, but must make corresponding infrastructure decisions with limited information. Sufficient data to perform a statistical analysis of location-specific crash frequencies is rarely available. For example, only approximately 10% of all bicycle crashes are reported to the police (Shinar et al., 2018). Therefore, urban planners often rely on expert opinion, which may lead to suboptimal prioritization and realization of infrastructure improvements.

METHOD

This paper demonstrates how surveys on bicycle crashes can be used to aid urban planners in making infrastructure decisions. In addition to confirming the location and characteristics of reported crashes, surveys can uncover characteristics of crashes that are not reported to the police, situations in which a crash almost occurred, and locations perceived by cyclists to be dangerous. Surveys also allow urban planners to investigate non-infrastructure related causes of crashes, such as the frequency with which individual cyclists use other modes of transportation.

PRACTICAL APPLICATIONS

The usefulness of surveys in the determination of urban cycling safety is demonstrated in this paper through analysis of survey results from the city of Zurich in 2018.

Detection of anomalies in cycling behavior with convolutional neural network and deep learning

Background

Cycling has always been considered a sustainable and healthy mode of transport. With the increasing concerns of greenhouse gases and pollution, policy makers are intended to support cycling as commuter mode of transport. Moreover, during Covid-19 period, cycling was further appreciated by citizens as an individual opportunity of mobility. Unfortunately, bicyclist safety has become a challenge with growing number of bicyclists in the 21st century. When compared to the traditional road safety network screening, availability of suitable data for bicycle based crashes is more difficult. In such framework, new technologies based smart cities may require new opportunities of data collection and analysis.

Methods

This research presents bicycle data requirements and treatment to get suitable information by using GPS device. Mainly, this paper proposed a deep learning-based approach "BeST-DAD" to detect anomalies and spot dangerous points on map for bicyclist to avoid a critical safety event (CSE). BeST-DAD follows Convolutional Neural Network and Autoencoder (AE) for anomaly detection. Proposed model optimization is carried out by testing different data features and BeST-DAD parameter settings, while another comparison performance is carried out between BeST-DAD and Principal Component Analysis (PCA).

Result

BeST-DAD over perform than traditional PCA statistical approaches for anomaly detection by achieving 77% of the F-score. When the trained model is tested with data from different users, 100% recall is recorded for individual user's trained models.

Conclusion

The research results support the notion that proper GPS trajectory data and deep learning classification can be applied to identify anomalies in cycling behavior.

A modelling framework for local thermal comfort assessment related to bicycle helmet use

Thermal discomfort due to accumulated sweat increasing head skin wettedness may contribute to low wearing rates of bicycle helmets. Using curated data on human head sweating and helmet thermal properties, a modelling framework for the thermal comfort assessment of bicycle helmet use is proposed. Local sweat rates (LSR) at the head were predicted as the ratio to the gross sweat rate (GSR) of the whole body or by sudomotor sensitivity (SUD), the change in LSR per change in body core temperature (Δt_re). Combining those local models with Δt_re and GSR output from thermoregulation models, we simulated head sweating depending on the characteristics of the thermal environment, clothing, activity, and exposure duration. Local thermal comfort thresholds for head skin wettedness were derived in relation to thermal properties of bicycle helmets. The modelling framework was supplemented by regression equations predicting the wind-related reductions in thermal insulation and evaporative resistance of the headgear and boundary air layer, respectively. Comparing the predictions of local models coupled with different thermoregulation models to LSR measured at the frontal, lateral and medial head under bicycle helmet use revealed a large spread in LSR predictions predominantly determined by the local models and the considered head region. SUD tended to overestimate frontal LSR but performed better for lateral and medial head regions, whereas predictions by LSR/GSR ratios were lower and agreed better with measured frontal LSR. However, even for the best models root mean squared prediction errors exceeded experimental SD by 18-30%. From the high correlation (R > 0.9) of skin wettedness comfort thresholds with local sweating sensitivity reported for different body regions, we derived a threshold value of 0.37 for head skin wettedness. We illustrate the application of the modelling framework using a commuter-cycling scenario, and discuss its potential as well as the needs for further research.

Maritime accidents in the Yangtze River: A time series analysis for 2011-2020

The theoretical analysis of maritime accidents is a hot topic, but the time characteristics and dynamics of maritime accidents time series are still unclear. It is difficult to draw a clear conclusion from the cause analysis, so the accident is difficult to be predicted. To bridge this gap, this research analyzes the characteristics and evolution mechanism of maritime accidents time series from the perspective of complex network theory. The visual graph algorithm is used to model the complex network of maritime accidents data in 22 jurisdictions of the Yangtze River, map the time series into a complex network, and reveal the time characteristics and dynamics of maritime accidents time series based on the complex system theory. In the empirical analysis, degree distribution, clustering coefficient and network diameter are used to analyze the characteristics of time series. The results show that the degree distribution of maritime accidents time series network presents power-law characteristics in the macro and micro levels, which shows that the maritime accidents time series is scale-free. In addition, according to the clustering coefficient and network diameter, maritime accidents time series in the Yangtze River has the characteristics of small-world and hierarchical structure. The research of this manuscript shows that the occurrence of maritime accidents is not random events and does not follow specific patterns but presents the characteristics of complex systems, and this phenomenon is common. The analysis of maritime accidents time series by complex network theory can provide theoretical support for maritime traffic safety management.

Odds of self-reported minor cycle crashes with conventional and electric assisted cycles adjusted for cycling frequency in Dutch and Belgian adults a retrospective study

BACKGROUND

Cycling for transportation and recreation is gaining in popularity, especially in older age groups. The rise in electric assisted cycles (EAC) may also have a role to play in this. With an increase in the number of cyclists comes an increase in the prevalence of cycle crashes. However, there is a lack of knowledge on EAC crashes and crash studies including cycle use data. An important question is also whether the high number of serious road injuries among older cyclists, is due to increased risk or more serious consequences in the event of a crash.

STUDY AIM

To compare the odds of reporting a cycle crash on a conventional (CC) against electrically assisted cycle (EAC), while controlling for age, gender, BMI, impairments while cycling, cycling frequency and region of residence.

METHODS

A 12-month retrospective cross-sectional survey-based study, including male and female cyclists aged 40+ years, was conducted in Belgium and the Netherlands. Socio-demographics, physical and mental impairments while cycling (such as lower reaction time), crash details and cycling frequency data were collected. Cyclists were grouped into CC, EAC or both (CC + EAC) based on the type of cycle they used during the study period. Logistic regression models were used to calculate the odds of reporting a cycle crash. Main and interaction effects were studied.

RESULTS

1,919 cyclists were included in the data analysis (63.2 ± 11.1 years; 50% women). 319 (17% of the total sample) cyclists reported a crash in the previous 12 months, of which 36% were EAC crashes. Those reporting a crash were significantly younger compared to those not reporting a crash. The following significant main effects were observed: those cycling on an EAC had a higher odds of reporting a cycle crash compared to those cycling on a CC (OR = 1.41, 95% CI = 1.01-1.97); cyclists in the category average and high on mental impairments while cycling had a higher odds of reporting a cycle crash compared to those in the category low (OR = 1.72, 95% CI = 1.23-2.40 and OR = 3.49, 95% CI = 2.51-4.90, respectively); higher cycling frequency is related to higher odds of reporting a cycle crash (OR = 3.25, 95% CI = 2.25-4.90). A significant interaction effect was observed between age category and gender (OR = 1.93, 95% CI = 1.15-3.26). Post-hoc tests revealed that men in the younger age category (40-64 years) had the highest probability (18.95%) of reporting a cycle crash, whereas men in the oldest age category (65+ years) had the lowest probability (9.99%) of reporting a cycle crash. No significant difference between age categories in women was observed.

CONCLUSION

This study indicates that within a cohort of middle aged and older adults living in regions with high to low cycling modal shares, cycle type, mental impairments while cycling, cycling frequency and region of residence play a significant role in the odds of reporting a (minor) cycle crash. Men in the age category 40-64 years have a significantly higher probability of reporting a cycle crash compared to men of 65+ years. Safety campaigns and instructions should pay particular attention to men in the age category 40-64 years and those with a mental impairment while cycling.

Characteristics of Cyclist Crashes Using Polytomous Latent Class Analysis and Bias-Reduced Logistic Regression

Although the number of cyclist crashes is decreasing in Japan, the fatality rate is not. Thus, reducing their severity is a major challenge. We used a polytomous latent class analysis to understand their characteristics and bias-reduced logistic regression to analyze their severity. Specifically, 90,696 combinations and 139,955 cyclist accidents were divided into 17 classes. The variable contributing the most to the classification was the crash location. Common fatality risks included older age groups and rural areas, whereas other factors differed among crash locations. Median strips, stop signs, and boundaries between the sidewalk and roadway affected the severity of crashes at intersections. Moreover, the existence of a median strip, collision partner, and time period affected the severity of crashes between intersections. On the sidewalks, the fatality risk was higher when the front part of the bicycle was subjected to the collision.

Cycling area can be a confounder and effect modifier of the association between helmet use and cyclists' risk of death after a crash

The effect of helmet use on reducing the risk of death in cyclists appears to be distorted by some variables (potential confounders, effect modifiers, or both). Our aim was to provide evidence for or against the hypothesis that cycling area may act as a confounder and effect modifier of the association between helmet use and risk of death of cyclists involved in road crashes. Data were analysed for 24,605 cyclists involved in road crashes in Spain. A multiple imputation procedure was used to mitigate the effect of missing values. We used multilevel Poisson regression with province as the group level to estimate the crude association between helmet use and risk of death, and also three adjusted analyses: (1) for cycling area only, (2) for the remaining variables which may act as confounders, and (3) for all variables. Incidence–density ratios (IDR) and their 95% confidence intervals were calculated. Crude IDR was 1.10, but stratifying by cycling area disclosed a protective, differential effect of helmet use: IDR = 0.67 in urban areas, IDR = 0.34 on open roads. Adjusting for all variables except cycling area yielded similar results in both strata, albeit with a smaller difference between them. Adjusting for cycling area only yielded a strong association (IDR = 0.42), which was slightly lower in the adjusted analysis for all variables (IDR = 0.45). Cycling area can act as a confounder and also appears to act as an effect modifier (albeit to a lesser extent) of the risk of cyclists’ death after a crash.

Configurations of underreported cyclist-motorised vehicle and single cyclist collisions: Analysis of a self-reported survey

Lower severity cycling collisions, and single cyclist collisions (or single bicycle crashes) are significantly underreported in police statistics, introducing biases into the types of collisions that are available for analysis. Furthermore, many lower severity collisions do not appear in other collision data sources (e.g. hospital and insurance data). This in turn affects priorities for cyclist safety and puts an underemphasis on certain collision types. Due to an absence of data, little is known of the configurations of unreported collisions. In this paper, data from a recent self-reporting survey of cycling collisions in Ireland is used to provide details of cyclist collisions with motorised vehicles and single cyclist collisions, with the inclusion of unreported collision types. Pre-crash scenarios and impact configurations for cyclist collisions with bonnet-type vehicles, and collision factors and fall types for single cyclist collisions are coded. Injury patterns and police underreporting levels are compared, and representative collision scenarios are identified. This study highlights the relative importance of collisions resulting from the cyclist and vehicle travelling in the same direction, specifically, nearside-hook, vehicle lane changing, and overtaking manoeuvres are emphasised. Furthermore, cases involving the cyclist struck from the side by vehicle fronts comprise a smaller share than previous studies. Specifically, side to side impacts, impacts between the front of the cyclist/bicycle and the side of the vehicle, and impacts with open(ing) doors emerge as important impact configurations with the inclusion of self-reported cases. For single cyclist collisions, the importance of loss of traction of the tyres due to slippery road conditions and interactions with tram tracks and kerbs are emphasised. Fall types differ between single cyclist collision scenarios and are related to differences in injury severity. These findings add to existing knowledge for fatal and higher severity collisions, demonstrating that cyclist safety priorities change with inclusion of underreported, and lower severity collisions. The findings are particularly relevant to road infrastructural planners, as well as in the fields of injury biomechanics, and automated vehicle safety (ADAS). Representative scenarios for collisions with bonnet-type vehicles and single cyclist collisions have been identified, allowing for their future inclusion in development of collision and injury prevention strategies. The dataset generated in this study is available from the authors on reasonable request.

A Novel Acceleration Signal Processing Procedure for Cycling Safety Assessment

With the growing rate of urban population and transport congestion, it is important for a city to have bike riding as an attractive travel choice but one of its biggest barriers for people is the perceived lack of safety. To improve the safety of urban cycling, identification of high-risk location and routes are major obstacles for safety countermeasures. Risk assessment is performed by crash data analysis, but the lack of data makes that approach less effective when applied to cyclist safety. Furthermore, the availability of data collected with the modern technologies opens the way to different approaches. This research aim is to analyse data needs and capability to identify critical cycling safety events for urban context where bicyclist behaviour can be recorded with different equipment and bicycle used as a probe vehicle to collect data. More specifically, three different sampling frequencies have been investigated to define the minimum one able to detect and recognize hard breaking. In details, a novel signal processing procedure has been proposed to correctly deal with speed and acceleration signals. Besides common signal filtering approaches, wavelet transformation and Dynamic Time Warping (DTW) techniques have been applied to remove more efficiently the instrument noise and align the signals with respect to the reference. The Euclidean distance of the DTW has been introduced as index to get the best filter parameters configuration. Obtained results, both during the calibration and the investigated real scenario, confirm that at least a GPS signal with a sampling frequency of 1Hz is needed to track the rider’s behaviour to detect events. In conclusion, with a very cheap hardware setup is possible to monitor riders’ speed and acceleration.

Causes of pedelec (pedal electric cycle) single accidents and their influence on injury severity

Pedelecs (pedal electric cycles) have become increasingly popular in recent years. This increase may trigger many positive effects, but unfortunately it also results in an increase in accidents, especially single accidents, involving these vehicles. This investigation analyses 1738 free text descriptions of pedelec single accidents written by police officers on site. The accidents occurred in 2016 and 2017 in 14 federal German states. The aim is to evaluate whether pedelec single accidents have specific causes that have not yet been recorded in the official accident statistics. Results show that most cases can be assigned to categories addressing the user (60 %) or the road design (54 %, accidents can be assigned to several categories). With this dataset it is also possible to analyse the severity of injuries using ordered probit analysis. Of the nine categories where severe injuries are expected, most address the user. In addition, serious injuries are expected when problems are encountered on downhill slopes or involve technical faults. An additional ordered probit analysis with a second dataset, based on different sources but using the same categorisation, supports these results.

Tram, rail, bicycle: An unhappy triad? Rising incidence and resource consumption of tramline-associated bicycling accidents in Bern, Switzerland

Bicycle traffic and the number of accidents have been increasing in Switzerland in recent years. However, little is known about specific types of bicycling accidents - such as tramline-associated bicycling accidents (TABA) - that are potentially avoidable. This retrospective single center study of emergency department (ED) consultations analyzed TABA in the city of Bern, Switzerland. We analyzed the medical records of adult patients who presented to the ED after any bicycle accident. Patient and consultation characteristics were extracted. Incidence, important characteristics and ED resource consumption of TABA were compared with bicycle accidents that did not involve tramlines (BA). Furthermore, injury patterns and predictors of resource consumption were determined in TABA. We included a total of 298 TABA and 2351 BA over the 5.6-year study period. TABA accounted for 11.2 % of all bicycling accidents. Descriptive analysis revealed a significant increase between 2013 (lowest) and 2016 (highest) of 33.0 % in the total number of BA and 132 % in the total number of TABA. Compared to BA, TABA patients were significantly older, more often female, had a less urgent triage, and less often needed resuscitation bay treatment, hospitalization or ICU-admission (all p < 0.05). In multivariable analysis, TABA were associated with greater needs for ED resources. Most TABA injuries were to limbs (70.5 %) or the head (53.0 %), including intracranial hemorrhage (2.3 %). The radiological work-up consumed most of the resources (37.5 %), followed by physicians' work (25.8 %). Statistically significant predictors of ED resource consumption in TABA were age, triage, resuscitation bay treatment, injuries to head/clavicle, and intoxication. One out of ten patients was intoxicated. Although TABA is associated with less severe trauma than BA, it bears the risk of significant morbidity and high ED resource needs. Intoxications contribute to this problem. Our findings underline the need for preventive measures to reduce TABA in the future.

Characteristics of cyclist collisions in Ireland: Analysis of a self-reported survey

As both a utility mode of transport and recreational activity, cycling has well-known health, environmental, and economic benefits. For these reasons it has been encouraged in many countries, including the Republic of Ireland. However, with increasing popularity there have been concurrent increases in road traffic related cyclist injuries. This study aims to characterise cyclist collisions, which are known to be underreported in Police statistics. For data collection, a survey addressing collisions was distributed to cyclists across the country in 2018. Univariable testing was used to identify differences in collision factors and injury outcomes for cyclist collisions with motorised vehicles, and those where a motorised vehicle is not involved as a collision partner i.e. single cyclist, cyclist-pedestrian, or cyclist-cyclist collisions. Furthermore, binary logistic regression modelling was used to clarify biasing factors for Police reporting of collisions. The largest proportion of collisions was between cyclists and motorised vehicles (56%), followed by single cyclist collisions (29%), collisions with other cyclists (8%), and pedestrians (7%). The odds of Police reporting for collisions with motorised vehicles in this study was 20 times greater than single cyclist collisions, 10 times greater than cyclist-cyclist collisions, and 4 times greater than collisions with pedestrians. The odds of Police reporting of serious injury collisions was 7 times greater than minor injury collisions. There were several differences in road, environmental, and human factors, and injury patterns between cyclist-motorised vehicle collisions and non-motorised vehicle collisions. The findings of this study indicate that greater attention should be paid to the following underreported collision types: 1) those that do not involve collisions with motorised vehicles (single cyclist collisions in particular), which have been shown to have differing collision characteristics to motorised vehicle collisions, and 2) less severe injuries, which have been shown to be a substantial contributor to the cyclist safety problem. Furthermore, surveys have been shown to be a valuable mechanism for investigation of lower severity cyclist injuries, which are largely unrecorded in Police or hospital data.

Speed characteristics of speed pedelecs, pedelecs and conventional bicycles in naturalistic urban and rural traffic conditions

To assess the potential impact of the higher speeds of pedal-assisted bicycles on safety, this study compared conventional bicycles, pedelecs and speed pedelecs (hereafter called s-pedelecs) on mean speeds, speed variability, harsh braking events (decelerations > 2 m/s²), and mean speeds above the speed limit (MSAL) in rural and urban areas in the Netherlands Data were collected in daily traffic, while the legal maximum speed for speed-pedelecs was 25 km/h, and pedelecs and s-pedelecs shared the infrastructure with conventional bicycles. Data were collected, using two-wheelers equipped with accelerometers and GPS. Personality factors - sensation seeking and risk taking - were measured with surveys. Regular commuters used one of the three bicycle types for two weeks. Participant bias was intentionally included by allowing participants to select a bicycle type of their preference, resulting in 12 conventional bicycle riders (71 % women), 14 pedelec riders (67 % women) and 20 s-pedelec riders (25 % women). S-pedelecs were much faster than conventional bicycles, amounting to a speed difference with conventional bicycles of 10.4 km/h in urban areas (M =28.2 km/h vs. 17.8 km/h) and of 13.2 km/h in rural areas (M = 31.4 km/h vs. 18.2 km/h). The speed differences between pedelecs and conventional bicycles were much smaller: 2.3 km/h in urban areas (20.1 km/h vs 17.8 km/h) and 4 km/h in rural areas (22.2 km/h vs. 18.2 km/h). Compared to conventional bicycles, s-pedelecs varied their speed to a greater extent and also braked harshly more frequently, showing a greater need for speed adjustment. These adjustments were larger at higher speeds. In contrast, pedelecs did not differ from conventional bicycles on speed variation. MSAL for s-pedelec riders differed by gender. For men the MSAL was 87 % on urban sections and 91 % on rural sections. For women, the MSAL was lower, respectively 23 and 69 %. None of the personality factors were associated with speed variability, harsh braking or MSAL. However, sensation seeking was associated with higher mean speeds on all three bicycle types. To conclude, pedelecs and conventional bicycles are similar in speed patterns, whereas the speed patterns of s-pedelecs differ significantly from the former two. The safety implications are discussed.

Are Latin American cycling commuters "at risk"? A comparative study on cycling patterns, behaviors, and crashes with non-commuter cyclists

INTRODUCTION

As part of the transformation of urban transportation dynamics, commuter cycling has acquired a high relevance as an alternative mode of transport in different countries, and Latin America seems to be one of the main focus of this worldwide "revolution". However, the high rates of crashes and injuries suffered by commuters have become a relevant issue in the field of road safety, especially in emerging regions with low cycling tradition, where social and infrastructural gaps may endanger the cyclists' safety.

OBJECTIVES

This study had two objectives. First, to compare key safe cycling-related variables between cycling commuters and non-commuters; and second, to differentially asses the effect of individual and cycling-related variables on their self-reported crash rates.

METHOD

For this cross-sectional research, the data provided by 577 Latin American urban cyclists from three countries (Argentina, Colombia and Mexico) with a mean age of 32.7 years was used. They answered a questionnaire on cycling habits, risk perception, rule knowledge, cycling behaviors and riding crashes.

RESULTS

The outcomes of this study showed that, despite having a higher risk perception, cycling commuters perform deliberate risky cycling behaviors (traffic violations) more frequently, and they suffer more crashes; cycling commuters report higher rates of psychological distress, and a lower degree of rule knowledge and protective behaviors than non-commuters. Furthermore, structural similarities and differences in the explanation of cycling crashes were found across commuters and non-commuters.

CONCLUSION

The results of this study suggest that non-commuters, whose purposes for cycling are more aimed at leisure and occasional trips, perform less risky behaviors but suffer more cycling distractions, whereas commuters are comparatively more exposed to behavioral-based safety risks, and suffer more frequent crashes. Since recent evidence forecasts that urban cycling will keep growing in Latin American cities, it is necessary to implement policies and educational/training improvements that may enhance the safety and health of cyclists in these countries.

The number of patients hospitalized with bicycle injuries is increasing - A cry for better road safety

INTRODUCTION

Norwegian authorities encourage people to commute by bicycle to improve public health, decrease rush-hour traffic jams and reduce pollution. However, increasing the number of bicyclists, especially in the rush-hour traffic, may increase the number of serious bicycle injuries.

OBJECTIVE

To explore trends in hospitalized bicycle injuries at a Norwegian level I trauma centre during the last decade.

METHODS

Data was extracted from the prospectively registered institutional trauma registry. We identified patients admitted after bicycle injuries between 2005 and 2016.

RESULTS

A total of 1543 patients were identified. Median age was 40 years (range 3-91) and 73 % were males. The majority of weekday injuries occurred in the morning and during the afternoon rush-hour, peaking at 8 am. and 4 pm. The annual number of admitted bicycle injuries increased from 79 to 184 during the study period. Also, an increase in the share of bicyclists using helmets was observed. The median Injury Severity Score (ISS) of 10 remained unchanged. 63 % had serious trauma (ISS ≥ 9), while 34 % suffered severe trauma (ISS ≥ 16). The absolute number of both serious and severe trauma increased annually. 36 % had head and neck injuries, while 16 % had chest injuries graded with Abbreviated Injury Scale ≥3. Loss of consciousness with Glasgow Coma Scale score <9 was seen in 7%. Median length of hospitalization was 3 days, and 39 % had surgery in one or more body regions. The 30-day mortality was 2.3 %.

CONCLUSIONS

The number of admitted bicycle injuries to our trauma centre is increasing. Rush-hour injuries dominate during weekdays. Bicycle injuries can be devastating and deserve more public attention to promote road safety.

Characteristics of Commuters’ Single-Bicycle Crashes in Insurance Data

In order to maximize the public health benefits of cycling, the negative impacts of cycling, such as the number and types of crashes, should be identified. Single-bicycle crashes, in which other road users are not collided with, are one of the main safety concerns in cycling, but comprehensive knowledge on these crashes is not available due to poor data sources. This study aimed to identify characteristics of commuters’ single-bicycle crashes in Finland. Firstly, insurance data covering 9268 commuter bicycle crashes in 2016 and 2017 were analyzed to find single-bicycle crashes. The insurance data are based on self-reported crashes. In total, 3448 single-bicycle crashes were found with crash descriptions that were informative enough for investigation of their characteristics. According to the results, 62.9% (95% confidence interval +/− 1.6%) of the crashes were related to the infrastructure. In the majority of infrastructure-related crashes, the road surface was slippery. The slippery road surface was typically due to icy or snowy conditions. The lack of proper data complicates the recognition of single-bicycle crashes, and hence policy actions and research projects are needed to develop better data sources for proper investigation of cycling safety.

Cyclist Injury Severity in Spain: A Bayesian Analysis of Police Road Injury Data Focusing on Involved Vehicles and Route Environment

This study analyses factors associated with cyclist injury severity, focusing on vehicle type, route environment, and interactions between them. Data analysed was collected by Spanish police during 2016 and includes records relating to 12,318 drivers and cyclist involving in collisions with at least one injured cyclist, of whom 7230 were injured cyclists. Bayesian methods were used to model relationships between cyclist injury severity and circumstances related to the crash, with the outcome variable being whether a cyclist was killed or seriously injured (KSI) rather than slightly injured. Factors in the model included those relating to the injured cyclist, the route environment, and involved motorists. Injury severity among cyclists was likely to be higher where an Heavy Goods Vehicle (HGV) was involved, and certain route conditions (bicycle infrastructure, 30 kph zones, and urban zones) were associated with lower injury severity. Interactions exist between the two: collisions involving large vehicles in lower-risk environments are less likely to lead to KSIs than collisions involving large vehicles in higher-risk environments. Finally, motorists involved in a collision were more likely than the injured cyclists to have committed an error or infraction. The study supports the creation of infrastructure that separates cyclists from motor traffic. Also, action needs to be taken to address motorist behaviour, given the imbalance between responsibility and risk.

Gender differences in accident risk with e-bikes-Survey data from Norway

E-bikes are becoming increasingly popular, and are given an important role in the green mobility of the future. However, some have raised concerns that the increased speed and the increased weight of the e-bike can lead to more accidents among cyclists riding an e-bike, as compared to conventional bicycles. Furthermore, it has been suggested that e-bikes may appeal to new groups of cyclists with little cycling experience, which may further impede cyclist safety. Previous research has not provided a clear picture. We investigate these questions with data from three surveys carried out in Norway (N = 7752). A logistic regression analysis comparing conventional and electric bicycles, controlling for age gender and exposure, shows an overall risk increase (all accidents) for e-bike users. The results suggest that this increased risk derives from females having a higher accident risk on e-bikes. For men there is no risk difference between e-bikes and conventional bikes. Some, but not all, of this elevated risk can be attributed to being unfamiliar with the bicycle. E-bikes are not more likely to cause serious accidents than conventional bicycles. In-depth analysis of accident causation showed that there was no difference in the factors leading to accidents, except that there was a somewhat higher prevalence of accidents resulting from balance problems with e-bikes.

Individual and environmental factors associated with death of cyclists involved in road crashes in Spain: a cohort study

OBJECTIVE

To quantify the magnitude of associations between cyclist fatalities and both cyclist and environment related characteristics in Spain during the first 24 hours after a crash.

DESIGN

Cohort study.

SETTING

Spain.

PARTICIPANTS

65 977 cyclists injured in road crashes recorded between 1993 and 2013 in the Spanish Register of Road Crashes with Victims.

MAIN OUTCOME

Death within the first 24 hours after the crash.

METHODS

A multiple imputation procedure was used to mitigate the effect of missing values. Differences between regions were assumed and managed with multilevel analysis at the cyclist and province levels. Incidence density ratios (IDR) with 95% CI were calculated with a multivariate Poisson model.

RESULTS

Non-use of a helmet was directly associated with death (IDR 1.43, 95% CI 1.25 to 1.64). Among other cyclist characteristics, age after the third decade of life was also directly associated with death, especially in older cyclists ('over 74' category, IDR 4.61, 95% CI 3.49 to 6.08). The association with death did not differ between work-related cycling and other reasons for cycling.There was an inverse association with death for crashes in urban areas and on community roads. Any adverse meteorological condition also showed a direct association with death, whereas altered road surfaces showed an inverse association. Crashes during nighttime were directly associated with death, with a peak between 3:00 and 5:59 am (IDR 1.58, 95% CI 1.03 to 2.41).

CONCLUSIONS

We found strong direct and inverse associations between several cyclist and environment related variables and death. These variables should be considered in efforts to prioritise public health measures aimed at reducing the number of cycling-related fatalities.

Safer cycling and the urban road environment: A case control study

This study aimed to identify features of the road environment that increased the risk of on-road bicycle crashes in Perth, Australia between 2014-2017. This case-control study used a combination of an in-depth crash study and naturalistic study to compare the road environment characteristics of 100 case (crash) sites and 300 control sites where no crash occurred using conditional logistic regression. For intersection sites, roundabouts (adjusted OR: 2.98, 95% CI: 1.18, 7.56) and traffic lights (adjusted OR: 3.86, 95% CI 1.29, 11.63) significantly increased the risk of a bicycle crash, compared to priority control/uncontrolled intersections. For midblock (non-intersection) sites, roads with an incline (upwards/downwards) significantly increased the risk of a crash (adjusted OR: 3.39, 95% CI: 1.02, 11.22), compared to level roads. This study highlighted the risk of roundabouts, traffic lights and roads with an incline for bicycle crashes. Treatments that reduce vehicle speeds and encourage cyclists to claim the lane at roundabouts, as well as careful road design and road maintenance at traffic lights, may reduce the risk of crashes for cyclists. While it is impossible to remove hills and slopes from the topography, it is possible to select routes to target for bicycle infrastructure which are predominantly level.

Crash causes, countermeasures, and safety policy implications

There are interrelationships between crash causes, countermeasures, and policy implications, but they are not necessarily direct and obvious. Part of the problem is the definition of a cause. The seminal 1979 Indiana University "Study of Accident Causes" has cemented some false assumptions that must be overcome to yield an effective crash countermeasures policy. The taxonomy of crash causes and the prevalence of different causes are determined by the investigators, who are biased in different ways. The prevalent notion that approximately 90 percent of the crashes are due to human errors or failures is due to a threshold bias, and the implied notion that 90 percent of the countermeasures should be directed at changing these behaviors is based on an erroneous assumption that the cure must be directly linked to the stated cause. A more balanced approach to the definition of a cause and to the search for crash countermeasures is needed, and the safe system approach appears to be a most promising one.

Physical, psychological and economic burden of two-wheel users after a road traffic injury: Evidence from intensive care units of three EU countries

INTRODUCTION

This study aimed to assess the physical, psychological, and economic burden shouldered by severely injured two-wheel users in three European countries as well as the cost resulting from their hospitalization.

METHODS

A total of seven public hospitals were involved in three countries: Greece, Italy, and Germany. Participants enrolled during a 12-month period starting in April 2013. Eligibility criteria included an injury sustained at Road Traffic Crashes (RTC) irrespective of the type of vehicle, hospitalization 1 day in the Intensive Care Unit (ICU) or sub-ICU, and age 18 years or over. Patients were interviewed at 1, 6, and 12 months upon admission. The study used widely recommended classifications for injury severity (Abbreviated Injury Severity [AIS]; Maximum Abbreviated Injury Severity [MAIS]) and standardized measures such as the Disability Assessment Schedule II (WHODAS 2.0), "Impact of Event Scale" (IES-R), Center for Epidemiological Studies Depression Scale (CES-D Scale). Health Care Expenditure was assessed through the Monash University Accident Research Centre (MUARC's) framework, which included measures of 'Direct' and 'Indirect' costs. Diagnosis-related groups (DRGs) were used to estimate hospitalization costs.

RESULTS

A total of 54 two-wheel users enrolled in the study in all the countries and 32 completed all follow-up questionnaires. Physical disability increased over 12 months following the injury. Post Traumatic Stress Disorder (PTSD) symptoms of avoidance remained at high levels over the study period. PTSD symptoms of intrusion improved significantly during the second half of the year under investigation. The total annual cost of injury for the two-wheel users who were hospitalized in the selected ICU of all the partner countries for severe injury in 2013/2014, was estimated at €714,491 made up of €123,457 direct and €591,034 indirect costs. Men, aged 50-64 years and those who sustained slight injuries primarily at the lower extremities presented higher indirect costs per person. A total of €1032.092 was spent on hospitalization payments. Women, aged 65+ and those who sustained severe injuries at the central body region presented higher direct costs per person. Women, aged 50-64 years, those with severe injuries and a major injury at the central body and the upper body region presented the highest hospitalization costs per person.

CONCLUSIONS

There is a need for effective strategies to early detect and treat groups at risk of being confronted with prolonged psychosocial and economic consequences.

PRACTICAL IMPLICATIONS

A holistic understanding of the impact of injury on individuals is important in order to achieve effective treatment of psychological co-morbidities in a timely manner.

Why women do not use the helmet when riding a bicycle

Women seem to use the helmet when riding a bicycle less frequently than men. Two possible explanations for this behavior are that 1) it is less appalling to them because of lack of comfort or other reasons, or 2) they use bicycles in a more cautious way than men so they feel that they do not need the helmet as much. The present paper explores these two explanations in 5,691 cyclists that responded to an online survey conducted in 17 countries as part of an EU COST project. Answers to questions related to the two aforementioned explanations were analyzed graphically and three questions that showed the most conspicuous differences between males and females were identified. These were: ‘Helmets are a problem because they disturb your hair’, ‘I am a fast rider’, and ‘I am a skilled rider’. The responses to these three questions plus their interactions with the gender of the respondent were used as predictors of the proportion of helmet wear. The results showed that: 1) the three questions predicted the use of the helmet, 2) the interaction between gender and hair disturbance was not significant, and 3) the interactions between gender and being a fast cyclist and being a skilled rider were both statistically significant showing that women that regard themselves as slow riders or skillful riders use relatively less the helmet than men in similar conditions.