Mixed logit analysis of bicyclist injury severity resulting from motor vehicle crashes at intersection and non-intersection locations

Critical risk factors associated with fatal/severe crash outcomes in personal mobility device rider at-fault crashes: A two-step inter-cluster rule mining technique

Personal Mobility Devices (PMDs) have witnessed an extraordinary surge in popularity, emerging as a favored mode of urban transportation. This has sparked significant safety concerns, paralleled by a stark increase in PMD-involved crashes. Research indicates that PMD user behavior, especially in urban areas, is crucial in these crashes, underscoring the need for an extensive investigation into key factors, particularly those causing fatal/severe outcomes. Remarkably, there exists a noticeable gap in the research concerning the analysis of determinants behind fatal/severe PMD crashes, specifically in PMD rider-at-fault collisions. This study addresses this gap by identifying uniform groups of PMD rider-at-fault crashes and investigating cluster-specific key factor associations and determinants of fatal/severe crash outcomes using Seoul's PMD rider-at-fault crash data from 2017 to 2021. A comprehensive two-step framework, integrating Cluster Correspondence Analysis (CCA) and Association Rules Mining (ARM) techniques is employed to segment PMD rider-at-fault crash data into homogeneous groups, revealing unique risk factor patterns within each cluster and further exploring the combination of factors associated with fatal/severe PMD rider-at-fault crash outcomes. CCA revealed three distinct groups: PMD-vehicle, PMD-pedestrian, and single-PMD crashes. From the ARM, it was found that fatal/severe crashes were linked to dry road conditions, male PMD users, and weekdays, irrespective of the cluster. Whereas speeding violations and side collisions were associated with fatal/severe PMD-vehicle rider-at-fault crashes, traffic control violations were related to fatal/severe PMD-pedestrian rider-at-fault crashes at pedestrian crossings. Unsafe riding practices predominantly caused single-PMD crashes during daytime hours. From the findings, engineering improvements, awareness campaigns, education, and law enforcement actions are recommended. The new insights gleaned from this research provide a foundation for informed decision-making and the implementation of policies designed to enhance PMD safety.

A correlated random parameters ordered probit approach to analyze the injury severity of bicycle-motor vehicle collisions at intersections

Bicycle-motor vehicle (BMV) accidents hold paramount importance due to their substantial impact on public safety. Specifically, road intersections, being critical conflict points, demand focused attention to reduce BMV crashes effectively and mitigate their severity. The existing research on the severity analysis of these crashes appears to have certain gaps that warrant further contribution. To address the mentioned limitations, this study first integrates multiple pre-collision features of the bicycles and vehicles to classify crash types based on the mechanism of the crashes. Then, the correlated random parameters ordered probit (CRPOP) model is employed to examine the factors influencing injury severity among bicyclists involved in intersection BMV crashes in Pennsylvania from 2013 to 2018. To gain deeper insights, this study conducts a separate analysis of crash data from 3-leg intersections, 4-leg intersections, and their combined scenarios, followed by a comparative examination of the results. The findings revealed that the presented crash typing approach yields new insights regarding injury severity outcomes. Moreover, in addition to exhibiting a comparable statistical performance contrasting to the more restricted models, the CRPOP model identified hidden correlations between three random parameters. Furthermore, the study demonstrated that analyzing combined crash data from the two intersection types obscured certain factors that were found significantly influential in the injury outcomes through analyzing sub-grouped data. Consequently, it is recommended to implement tailored countermeasures for each type of intersection.

Factors affecting injury severity in motorcycle crashes: Different age groups analysis using Catboost and SHAP techniques

OBJECTIVE

Motorcycle crashes often result in severe injuries on roads that affect people's lives physically, financially, and psychologically. These injuries could be notably harmful to drivers of all age groups. The main objective of this study is to investigate the risk factors contributing to the severity of crash injuries in different age groups.

METHODS

This Objective is achieved by developing accurate machine learning (ML) based prediction models. This research examines the relationship between potential risk factors of motorcycle-associated crashes using (ML) and Shapley Additive explanations (SHAP) technique. The SHAP technique further helped interpreting ML methods for traffic injury severity prediction. It indicates the significant non-linear interactions between dependent and independent variables. The data for this study was collected from the Provincial Emergency Response Service RESCUE 1122 for the Rawalpindi region (Pakistan) over three years (from 2017 to 2020). The Synthetic Minority Oversampling Technique (SMOTE) is employed to balance injury severity classes in the pre-processing phase.

RESULTS

The results demonstrate that age, gender, posted speed limit, the number of lanes, and month of the year are positively associated with severe and fatal injuries. This research also assesses how the modeling framework varies between the ML and classical statistical methods. The predictive performance of proposed ML models was assessed using several evaluation metrics, and it is found that Catboost outperformed the XGBoost, Random Forest (RF) and Multinomial Logit (MNL) model.

CONCLUSION

The findings of this study will assist road users, road safety authorities, stakeholders, policymakers, and decision-makers in obtaining substantial and essential guidance for reducing the severity of crash injuries in Pakistan and other countries with prevailing conditions.

Exploring temporal instability effects on bicyclist injury severities determinants for intersection and non-intersection-related crashes

Cycling is a sustainable and healthy mode of transportation with direct links to reducing traffic congestion, lowering greenhouse gas emissions, and improving air quality. However, from a safety perspective, bicyclists represent a risky road user group with a higher likelihood of sustaining severe injuries when involved in vehicle crashes. With various determinants known to affect bicyclist injury severity and vary across locations, this study investigates the factors affecting bicyclist injury severity and temporal instability, considering the location of crashes. More specifically, the objective of this study is to understand differences in injury severities of intersection and non-intersection-related single-bicycle-vehicle crashes using four year crash data from the state of Florida. Random parameters logit models with heterogeneity in the means and variances are developed to model bicyclist injury severity outcomes (no injury, minor injury, and severe injury) for intersection and non-intersection crashes. Several variables affecting injury severities are considered in model estimation, including weather, roadway, vehicle, driver, and bicyclist characteristics. The temporal stability of the model parameters is assessed for different locations and years using a series of likelihood ratio tests. Results indicate that the determinants of bicyclist injury severities change over time and location, resulting in different injury severities of bicyclists, with non-intersection crashes consistently resulting in more severe bicyclist injuries. Using a simulation-based out-of-sample approach, predictions are made to understand the benefits of replicating driving behaviour and facilities similar to intersections for non-intersection locations, which could benefit in reducing bicyclist injury severity probabilities.

Injury severity prediction of cyclist crashes using random forests and random parameters logit models

Cycling provides numerous benefits to individuals and to society but the burden of road traffic injuries and fatalities is disproportionately sustained by cyclists. Without awareness of the contributory factors of cyclist death and injury, the capability to implement context-specific and appropriate measures is severely limited. In this paper, we investigated the effects of the characteristics related to the road, the environment, the vehicle involved, the driver, and the cyclist on severity of crashes involving cyclists analysing 72,363 crashes that occurred in Great Britain in the period 2016-2018. Both a machine learning method, as the Random Forest (RF), and an econometric model, as the Random Parameters Logit Model (RPLM), were implemented. Three different RF algorithms were performed, namely the traditional RF, the Weighted Subspace RF, and the Random Survival Forest. The latter demonstrated superior predictive performances both in terms of F-measure and G-mean. The main result of the Random Survival Forest is the variable importance that provides a ranked list of the predictors associated with the fatal and severe cyclist crashes. For fatal classification, 19 variables showed a normalized importance higher than 5% with the second involved vehicle manoeuvring and the gender of the driver of the second vehicle having the greatest predictive ability. For serious injury classification, 13 variables showed a normalized importance higher than 5% with the bike leaving the carriageway having the greatest normalized importance. Furthermore, each path from the root node to the leaf nodes has been retraced the way back generating 361 if-then rules with fatal crash as consequent and 349 if-then rules with serious injury crash as consequent. The RPLM showed significant unobserved heterogeneity in the data finding four normal distributed indicator variables with random parameters: cyclist age ≥ 75 (fatal prediction), cyclist gender male (fatal and serious prediction), and driver aged 55-64 (serious prediction). The model's McFadden Pseudo R2 is equal to 0.21, indicating a very good fit. Furthermore, to understand the magnitude of the effects and the contribution of each variable to injury severity probabilities the pseudo-elasticity was assessed, gaining valuable insights into the relative importance and influence of the variables. The RF and the RPLM resulted complementary in identifying several roadways, environmental, vehicle, driver, and cyclist-related factors associated with higher crash severity. Based on the identified contributory factors, safety countermeasures useful to develop strategies for making bike a safer and more friendly form of transport were recommended.

A hybrid clustering and random forest model to analyse vulnerable road user to motor vehicle (VRU-MV) crashes

The main goal of this study is to investigate the unobserved heterogeneity in VRU-MV crash data and to determine the relatively important contributing factors of injury severity. For this end, a latent class analysis (LCA) coupled with random parameters logit model (LCA-RPL) is developed to segment the VRU-MV crashes into relatively homogeneous clusters and to explore the differences among clusters. The random-forest-based SHapley Additive exPlanation (RF-SHAP) approach is used to explore the relative importance of the contributing factors for injury severity in each cluster. The results show that, vulnerable group (VG), intersection or not (ION) and road type (RT) clearly distinguish the crash clusters. Moto-vehicle type and functional zone have significant impact on the injury severity among all clusters. Several variables (e.g. ION, crash type [CT], season and RT) demonstrate a significant effect in a specific sub-cluster model. Results of this study provide specific and insightful countermeasures that target the contributing factors in each cluster for mitigating VRU-MV crash injury severity.

Bicyclist crashes with cars and SUVs: Injury severity and risk factors

OBJECTIVE

The popularity of bicycle travel has increased in recent years alongside a comparable increase in the risk of injury or death for those cyclists. The current study was conducted to investigate the differences in injury outcomes between bicyclists struck by SUVs and those struck by cars and to uncover the mechanisms behind injury patterns that have been observed in past research.

METHODS

We analyzed 71 single-vehicle crashes from the Vulnerable Road User Injury Prevention Alliance pedestrian crash database, focusing on crashes involving an SUV or car. Each crash from this database included an in-depth analysis of police reports, bicyclist medical records, crash reconstructions, and injury attribution by a panel of experts.

RESULTS

Bicyclist injuries from crashes with SUVs were more severe than those from crashes with cars, particularly with respect to head injuries. The greater injury severity associated with SUVs was related to these vehicles' tendency to produce injuries from ground contact or from vehicle components near the ground. In contrast, cars were much less likely to produce ground injuries and instead tended to distribute less severe injuries across multiple vehicle components.

CONCLUSIONS

The pattern of results suggest that the size and shape of SUV front ends are responsible for the differences in bicyclist injury outcomes. In particular, we found that SUV crashes inflicted more severe head injuries compared with car crashes and that SUVs were disproportionately likely to throw bicyclists to the ground and run them over.

Injury severity analysis of time-of-day fluctuations and temporal volatility in reverse sideswipe collisions: A random parameter model with heterogeneous means and heteroscedastic variances

PROBLEM

Sideswipe collisions in the opposite direction often result in more severe injuries than the typical same-direction crashes, especially when light trucks are involved. This study investigates the time-of-day fluctuations and temporal volatility of potential factors that affect the injury severity of reverse sideswipe collisions.

METHODS

A series of random parameters logit models with heterogeneous means and heteroscedastic variances are developed and utilized to explore unobserved heterogeneity inherent in variables and preclude biased parameter estimation. The segmentation of estimated results is also examined through temporal instability tests.

RESULTS

Based on crash data in North Carolina, a number of contributing factors are identified that have profound associations with obvious and moderate injuries. Meanwhile, significant temporal volatility is observed in the marginal effects of several factors such as driver restraint, alcohol or drugs impact, Sport Utility Vehicle (SUV) at fault, and adverse road surface across three different periods. Fluctuations in the time of day indicate that restraint with belts is more effective in mitigating the obvious injury in the nighttime, and high-class roadway sustains a higher probability of resulting in more serious injury compared to the daytime.

PRACTICAL APPLICATIONS

The findings of this study could help further guide the implementation of safety countermeasures related to atypical sideswipe collisions.

Impact of Driver's Age and Gender, Built Environment, and Road Conditions on Crash Severity: A Logit Modeling Approach

The purpose of this research is (1) to investigate the relationship between crash severity and the age and gender of the at-fault driver, the socio-economic characteristics of the surrounding environment, and road conditions, and (2) to explain the probability of a bodily injury crash, including fatality, with the alternative being a property damage only crash. In contrast to earlier research that has focused on young and old drivers, age is considered here on its lifetime continuum. A logit model is adopted and the gender and age of the at-fault drivers are part of the independent explanatory variables. The unit of analysis is the individual crash. Since age is a continuous variable, this analysis shows more precisely how age impacts accident severity and identifies when age has little effect. According to the results, the type of vehicle, timing of the crash, type of road and intersection, road condition, regional and locational factors, and socio-economic characteristic have a significant impact on crashes. Regarding the effect of age, when an accident occurs the probability of bodily injury or fatality is 0.703 for female drivers, and 0.718 for male drivers at 15 years of age. These probabilities decline very slightly to 0.696 and 0.711, respectively, around 33 years of age, then very slightly increase to 0.697 and 0.712, respectively, around 47.5 years of age. The results show that age affects crash severity following a polynomial curve. While the overall pattern is one of a downward trend with age, this trend is weak until the senior years. The policy implications of the results are discussed.

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.

Anatomical distribution of mandibular fractures from severe bicycling accidents: A 12-year experience from a Norwegian level 1 trauma center

Background/Aim

The mandible makes up a substantial part of the lower face, and is susceptible to injury. Even in helmeted cyclists, accidents may lead to fractures of the mandible because conventional helmets provide little protection to the lower part of the face. In addition, some studies indicate that helmets may lead to an increased risk of mandibular fractures. Thus, the aim of this study was to examine the anatomic distribution of mandibular fractures in injured cyclists and to assess if helmet use influenced the fracture locations.

Material and Methods

Data from a Norwegian Level 1 trauma center were collected in the Oslo University Hospital Trauma Registry over a 12‐year period. Of 1543 injured cyclists, the electronic patient charts of 62 cyclists with fractures of the mandible were retrospectively evaluated in detail. Demographic data, helmet use, and fracture type were assessed.

Results

Sixty‐two patients (4%) had fractures of the mandible, and women had an increased risk (OR 2.49, 95% CI 1.49–4.16, p < .001). The most common fracture site was the mandibular body, followed by the condyle. Isolated mandibular fractures occurred in 45% of the patients and 55% had other concomitant facial fractures. There were 42% of the patients with fractures in multiple sites of the mandible, and 42% had a concomitant dentoalveolar injury. Half of the cyclists were wearing a helmet at the time of the accident and 39% were not. There was no significant difference in fracture distribution between the helmeted and non‐helmeted groups.

Conclusions

Fracture of the mandibular body was the most prevalent mandibular fracture type following bicycle accidents. Women had an increased risk of mandibular fractures compared with men, whereas helmet wearing did not affect the anatomical fracture site.

Analyzing Safety Concerns of (e-) Bikes and Cycling Behaviors at Intersections in Urban Area

Extensive effort has been devoted to examining the causal relationship between contributing factors and injury severities. Given the important role of riders’ behaviors in traffic conflicts, this paper aims to analyze the causal effects of traffic conflicts resulting from riders’ behaviors at intersections. The authors collected video data on 152 traffic conflicts caused by riders’ dangerous behaviors in Jiangning District, China. This paper proposes a Bayesian-structural equation modeling (BSEM) approach. Based on the obtained BSEM path coefficient diagram, the factor loadings and path coefficients are analyzed to unveil the potential influence of factors, including personal features, dangerous behavior tendency, temporal and spatial characteristics of dangerous behavior, and the external environment. The results show that compared to human factors, environmental factors have a less direct impact on the severity of traffic conflicts; instead, they have an indirect positive impact on traffic conflicts by affecting behaviors. That is, if riders judge that road conditions are not suitable to conduct dangerous behaviors, they become more cautious in view of current road conditions and time revenue. Furthermore, dangerous cycling behaviors that continue to encroach on the time and space of motorized vehicles are prone to be more dangerous. The dangerous behaviors that continuously encroach on the time and space of motor vehicles (e.g., disobeying traffic signals and riding in a motorway) are significant predictors of serious conflicts. Considering the heterogeneity of riding behavior, these findings could be applied to develop effective education and intervention programs for preventing riders’ high-risk behaviors and improving the traffic environment.

Bicyclists injury severities: An empirical assessment of temporal stability

Cyclists are among the most vulnerable participants in road traffic, making their safety a top priority. Riding behavior of bicyclists could shift over time, affecting the level of injuries sustained in bicyclist-involved crashes. Many studies have been done to identify the factors influencing bicyclist injury severity, but the temporal stability of these variables over time needs further study. The temporal instability of components that affect the cyclist injury levels in bicycle collisions is explored in this paper. To obtain potential unobserved heterogeneity, yearly models of cyclist-injury levels (including potential consequences of no, minor, and severe injury) were measured separately applying a random parameters logit model that allows for potential heterogeneity in estimated parameters' means and variances. Employing a data source on bicycle collisions in Los Angeles, California, over the course of six years (January 1, 2012 to December 31, 2017), several variables which may impact the injury level of cyclists were explored. This paper has also employed a set of likelihood ratio tests assessing the temporal instability of the models. The temporal instability of the explanatory parameters has been evaluated with marginal effects. The results of the model assessment indicate that several factors may raise the chances of severe bicyclist injuries in collisions, including cyclists older than 55 years old, cyclists who were identified to be at-fault in crashes, rear-end collisions, cyclists who crossed into opposing lane before the collision, crashes occurring early mornings (i.e., 00:00 to 06:00) and so on. The results also showed that the details and estimated parameters of the model do not remain stable over the years, however the source of this instability is unclear. In addition, the findings of model estimation demonstrate that considering the heterogeneity in the random parameter means and variances will enhance the overall model fit. This study also emphasizes the significance of accounting for the transferability of estimated models and the temporal instability of parameters influencing the injury severity outcomes in order to dynamically examine the collected data and adjust safety regulations according to new observations.

Injury severity analysis of two-vehicle crashes at unsignalized intersections using mixed logit models

The severity of the two-vehicle crash is closely related to the characteristics of both the struck and striking vehicles. Ignoring vehicle roles may lead to biased results. Thus, this study used mixed logit models to determine the factors that influence injury severity in the two-vehicle crash, taking into account the vehicle characteristics of the different crash roles. The data used is collected from Pennsylvania Department of Transportation (PennDOT) Open Data Portal. First, the synthetic minority oversampling technique and nearest neighbors (SMOTE-ENN) strategy was selected to address the class imbalance problem of crash data. Then, two separated mixed logit models were developed for four- and three-legged unsignalized intersections. The results suggest that the type and movement of vehicles have significant effects on crash severity. For example, right-turn vehicles being struck can lead to more serious crashes than striking other vehicles. Large trucks striking other vehicles are found to increase crash severity, but being struck is found to decrease crash severity. Additionally, several factors were also identified to affect crash severity in both models and effective countermeasures suggestions were proposed to mitigate crash severity.Supplemental data for this article is available online at at .

An application of in-vehicle recording technologies to analyze injury severity in crashes between taxis and two-wheelers

Conventionally, the crash data used in traffic safety analysis have been collected by the police dispatched to the crash scene. Therefore, crash information inevitably includes errors that influence traffic safety analysis. Such errors can include the crash speed, crash time, crash location, and other crash characteristics. The advances in in-vehicle video recording (IVVR) technologies have recently enabled traffic safety professionals to use more accurate crash information based on crash data reconstruction methods. Although a few studies have been conducted to identify the factors affecting the crash injury severity using such detailed crash data, there was no effort to analyze the factors affecting the injury severity in crashes between taxis and two-wheelers (TWs), including bicycles and motorcycles. Therefore, this study analyzes the injury severity of TW riders in taxi-TW crashes with the accurate crash data collected by taxis equipped with IVVR devices in Incheon, Korea. Two hundred and forty-eight crash data from two years (2010-2011) were used to perform this objective. The factors affecting the injury severity to TW riders were identified based on a partial proportional odds model for these data. Seven variables were found to affect the injury severity significantly: crash speed, second collision, third collision, Delta-V, crashes that occurred with a non-helmeted motorcycle rider, crashes where the collision type was sideswipe, and crashes under rainy or snowy weather conditions. On the other hand, two variables regarding crashes, where the taxi driver behavior helped reduce visible and severe injuries, were changing lanes and the young TW riders (<18 years).

Influence factors on injury severity of bicycle-motor vehicle crashes: A two-stage comparative analysis of urban and suburban areas in Beijing

OBJECTIVE

More attention should be given to bicycle-motor vehicle (BMV) crashes, as cyclists are at a higher risk of suffering injuries than motor vehicle users in a crash. This study aims to explore the factors influencing the injury severity of bicycle-motor vehicle (BMV) crashes in Beijing (China) and discusses the commonalities and differences between the urban and suburban areas.

METHODS

Information regarding 1,136 crashes between bicycles and motor vehicles were collected using police reported data from 2014 to 2015. A two-stage approach integrating random parameters logit (RP-logit) model and two-step clustering (TSC) algorithm was proposed to investigate the significant influence factors and their combination characteristics. Specifically, the RP-logit model was first used to identify the significant influence factors of urban and suburban areas, and then the TSC algorithm was applied to reveal the combination characteristics of significant influence factors for the fatal crashes.

RESULTS

Five factors were found to be statistically significant and had random effects on the injury severity in urban areas, i.e., type of motor vehicle, motor vehicle license ownership, type of bicycle, signal control mode and lighting condition; and seven factors were found to be statistically significant on the injury severity in suburban areas, i.e., type of motor vehicle, motor vehicle license ownership, physical isolation facility, signal control mode, weather, visibility and lighting condition. Based on TSC, the combination of significant factors showed different characteristics for fatal crashes in urban and suburban areas, in which two types of the scene including five factors should be concerned in urban areas while one type of scene containing four factors in suburban areas.

CONCLUSIONS

The results suggest that different influence factors and individual heterogeneity exist in the RP-logit model for injury severity analysis of BMV crashes in urban and suburban areas. It shows that in urban areas, heavy truck, light truck and bus significantly increase the likelihood of fatal injury than that of suburban areas. These findings can provide valuable reference information for BMV crashes response, such as heavy truck restriction, to facilitate regional safety measures for urban and suburban areas.

The Analysis of the Factors Influencing the Severity of Bicyclist Injury in Bicyclist-Vehicle Crashes

Transportation and technological development have for centuries strongly influenced the shaping of urbanized areas. On one hand, it undoubtedly brings many benefits to their residents. However, also has a negative impact on urban areas and their surroundings. Many transportation and technological solutions lead, for example, to increased levels of pollution, noise, excessive energy use, as well as to traffic accidents in cities. So, it is important to safe urban development and sustainability in all city aspects as well as in the area of road transport safety. Due to the long-term policy of sustainable transport development, cycling is promoted, which contributes to the increase in the number of this group of users of the transport network in road traffic for short-distance transport. On the one hand, cycling has a positive effect on bicyclists’ health and environmental conditions, however, a big problem is an increase in the number of serious injuries and fatalities among bicyclists involved in road incidents with motor vehicles. This study aims to identify factors that influence the occurrence and severity of bicyclist injury in bicyclist-vehicle crashes. It has been observed that the factors increasing the risk of serious injuries and deaths of bicyclists are: vehicle driver gender and age, driving under the influence of alcohol, exceeding the speed limit by the vehicle driver, bicyclist age, cycling under the influence of alcohol, speed of the bicyclist before the incident, vehicle type (truck), incident place (road), time of the day, incident type. The obtained results can be used for activities aimed at improving the bicyclists’ safety level in road traffic in the area of analysis.

The unintended impact of helmet use on bicyclists' risk-taking behaviors

INTRODUCTION

Safety is a critical factor in promoting sustainable urban non-motorized travel modes like bicycles. Helmets have shown to be effective in reducing injury severity in bicycle crashes, however, their effects on bicyclists' behaviors still requires deeper understanding, especially amid the emerging trend of using shared bicycles. Risk compensation effects suggest that bicyclists may offset perceived gains in safety from wearing a helmet by increasing risk-taking behaviors. A better understanding of these compensation effects can be useful in assessing various bicycle safety related programs.

METHOD

Using a sample of 131 bicyclists from the San Francisco Bay area, this research studies how bicyclists respond with respect to risk-taking behaviors under various urban-street conditions, as a function of helmet use. Study participants were each shown 12 videos, shot in Berkeley, California, from the perspective of a bicyclist riding behind another bicyclist. A fractional factorial experiment design was used to systematically vary contextual attributes (e.g., speed, bike lane facilities, on-street parking, passing vehicles) across the videos. After each video, participants were asked to indicate if they would overtake the bicyclist in the video. With the help of data adaptive estimation techniques, targeted maximum likelihood estimation (TMLE) was applied to estimate the average risk difference between helmeted users and non-users, controlling for self-selection effects. Individual-based nonparametric bootstrap was performed to assess the uncertainty associated with the estimator.

RESULTS

Our findings suggest, on average, individuals more likely to wear a helmet are 15.6% more likely to undertake a risky overtaking maneuver. Practical Applications: This study doesn't try to oppose mandatory helmet laws, but rather serves as a cautionary warning that road safety programs may need to consider strategies in which unintended impact of bicycle helmet use can be mitigated. Moreover, our findings also provide additional evaluation component when it comes to the cost-benefit assessment of helmet-related laws.

Correlated mixed logit modeling with heterogeneity in means for crash severity and surrogate measure with temporal instability

This study employs the correlated mixed logit models with heterogeneity in means by accounting for temporal instability to estimate both injury severity and vehicle damage. Two years of intersection crash data from Connecticut were analyzed based on driver characteristics, highway and traffic attributes, environmental variables, vehicle and crash types. These elements were used as independent variables to explore the contributing factors to crash outcome. The likelihood ratio test highlights that the temporal instability exists in both injury severity and vehicle damage models. The model estimation results illustrate that the means of some random parameters are different among crashes. The correlation coefficients of random parameters verify that these random parameters are not always independent, and their correlations should be considered and accounted for in crash severity estimation models. The investigation and comparison between injury severity models and vehicle damage models verify that the injury severity and vehicle damage are highly correlated, and the effects of contributing factors on vehicle damage are consistent with the results of injury severity models. This finding demonstrates that vehicle damage can be used as a potential surrogate measure to injury severity when suffering from a low sample of severe injury crashes in crash severity prediction models. It is anticipated that this study can shed light on selecting appropriate statistical models in crash severity estimation, identifying intersection crash contributing factors, and help develop effective countermeasures to improve intersection safety.

Injury severity analysis of rollover crashes for passenger cars and light trucks considering temporal stability: A random parameters logit approach with heterogeneity in mean and variance

PROBLEM

The rollover crash is a serious crash type that often causes higher injury severities. Moreover, factors that contribute to the injury severities of rollover crashes may show instabilities in different vehicle types and time periods, which requires further investigations. This study utilizes the rollover crash data in North Carolina from Highway Safety Information System (HSIS) to study the effect instabilities of factors in vehicle type and time periods in rollover crashes.

METHODS

The injury severities of drivers are estimated using the random parameters logit (RPL) model with heterogeneity in means and variances. Available factors in HSIS have been categorized into three groups, which are drivers, road, and environment, respectively. This study also justifies the segmentations through transferability tests. The effects of identified significant factors are evaluated using marginal effects.

RESULTS

Factors such as FWP (farm, wood, and pasture areas), unhealthy physical condition, impaired physical condition, road adverse, and so forth have shown instabilities in marginal effects among vehicle types and time periods. Practical Applications: The finding of this research could provide important references for policy makers and automobile manufactures to help mitigate the injury severity of rollover crashes.

Hamiltonian Monte Carlo with Random Effect for Analyzing Cyclist Crash Severity

Vulnerable traffic users, such as bikers and pedestrians, account for a significant number of fatalities on the roadways. Extensive research has been conducted in the literature review to identify factors to those crashes. Studying factors to those crashes is especially important in the Western state in the US, due to one of the highest fatality rates in the nation and its unique geographic conditions. The first step in identifying factors to the severity of cyclist crashes is to find the underlying factors to that type of crash, while accounting for the heterogeneity in the dataset. Various techniques such as mixed parameter or mixed effect models have been employed in the literature to account for the heterogeneity of the dataset. In the mixed effect model, often the random effect parameter has been assigned subjectively, and based on some attributes and engineering intuitions. Those assignments are expected to account for the heterogeneity in the dataset and enhancement of the model fit. However, a question might arise whether those factors could account for an optimum amount of the heterogeneity in the dataset. A more reasonable way might be to let the algorithm such as the finite mixture model (FMM) to identify those clusters based on parameters of the Gaussian model, means and covariance matrices of the dataset, and allocate each observation to the related clusters. Thus, in this study, to capture optimum amount of heterogeneity, first we implemented the finite mixture model in the context of maximum likelihood, due the label switching issue of the method in the context of the Bayesian method. After assignment of the parameters to the observation, the main method of Hamiltonian Monte Carlo (HMC) with random effect was implemented. The results highlighted a significant improvement in the model fit, in terms of Widely Applicable Information Criterion (WAIC). The results of this study highlighted factors such as older biker age, increased number of lanes, nighttime travelling, increased posted speed limit and driving while under emotional conditions are some factors contributing to an increased severity of bikers’ crash severity. Extensive discussion has been made regarding the methodological algorithms and model parameters estimations.

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.

Temporal instability assessment of injury severities of motor vehicle drivers at give-way controlled unsignalized intersections: A random parameters approach with heterogeneity in means and variances

Unsignalized intersections are highly susceptible to traffic crashes compared to signalized ones. By taking into account temporal stability and unobserved heterogeneity, this study investigates the determinants of the injury severity of drivers involved in crashes at unsignalized intersections controlled by give-way (yield) signs. Mixed logit models with three approaches were employed, namely random parameters, random parameters with heterogeneity in means, and random parameters with heterogeneity in means and variances. The investigation covered four years (2015-2018) of motor vehicle crashes in South Australia, and the injury severity was categorized into no injury, minor injury, and severe injury. Log-likelihood ratio tests revealed that there is a significant temporal instability in the four years of crashes. Thus, each year was considered separately to avoid any potential erroneous conclusions and unreliable countermeasures. The study found 28 indicator variables were temporally unstable over the four years of crashes, such as driver gender, time of the crash, rear-end involvement, sideswipes, right-angle crash type, vehicle movement at crash time, and crash time. Whereas several variables were stable over the same period, for example, crashes within metropolitan areas were temporally stable over four years, crashes in dry pavement condition were temporally stable over three consecutive years. Four factors have temporal stability over two consecutive years: alcohol involvement crashes, hitting fixed objects, hitting cyclists, and crashes during winter. Overall, mixed logit models with heterogeneity in means and with/without variance performed better than the standard one. It is recommended that temporal instability be considered in order to avoid any potential inconsistent countermeasures.

Individual and contextual factors associated with bicyclist injury severity in traffic incidents between bicyclists and motorists in Chile

Bicyclists are vulnerable road users who risk incurring severe injuries from traffic incidents involving motorists. However, the prevalence of severe bicycle injuries varies across countries and is not well-documented in Latin American countries. Studies from developed countries outside of Latin America have shown that individual and contextual factors are associated with severe injuries incurred by bicyclists in road traffic incidents with motorists, but it is not clear whether these factors are the same as those incurred by Latin American bicyclists. Moreover, most studies on bicyclist-motorist traffic incidents have treated injury severity as a binary variable for analysis although injuries range widely in severity. The aims of this study were to determine the prevalence of bicycle injuries from incidents between motorists and bicyclists in Chile and examine the associations between individual and contextual factors and bicyclist injury severity, treated as an ordinal outcome variable, in these incidents. Data on road traffic incidents between bicyclists and motorists from the 2016 Traffic Accident of Bicycle Riders and Consequences database of the Chilean Transport Ministry were analysed. Multilevel mixed-effects ordinal regression models were used to examine associations. In total, 81.2 % of 4093 traffic incidents between bicyclists and motorists resulted in nonfatal injuries to bicyclists and another 2.3 % resulted in fatalities. Most incidents involved collisions (84.3 %), and most were due to a motorist being distracted while driving (50.4 %). Severe bicyclist injuries were more likely when the incident involved a stationary cyclist who was struck, a collision between a moving bicycle and a moving motor vehicle, or an overturning motor vehicle striking a bicyclist (p < 0.001). Other factors included the motorist driving under the influence of alcohol (p = 0.05), the incident taking place in a mid-size community (p = 0.04), the incident occurring between 7:00 pm and 4:59 am (p < 0.01), and the injured bicyclist being under 18 years or 45+ years of age (p < 0.05). These findings suggest the need for educational programs that promote safe driving behaviour in the presence of bicyclists, better enforcement of laws and higher penalties for distracted or drunk driving, and provision of high-quality exclusive bicyclist infrastructure to address the vulnerability of the youngest and oldest bicyclists on shared roads, particularly in mid-size communities, and to provide better lighting on bikeways for evening bicycling, to reduce the high incidence of severe bicyclist injuries in motorist-bicyclist incidents.

Sex-based differences in helmet performance in bicycle trauma

OBJECTIVES

To determine the existence of sex-based differences in the protective effects of helmets against common injuries in bicycle trauma.

METHODS

In a retrospective cohort study, we identified patients 18 years or older in the 2017 National Trauma Database presenting after bicycle crash. Sex-disaggregated and sex-combined multivariable logistic regression models were calculated for short-term outcomes that included age, involvement with motor vehicle collision, anticoagulant use, bleeding disorder and helmet use. The sex-combined model included an interaction term for sex and helmet use. The resulting exponentiated model parameter yields an adjusted OR ratio of the effects of helmet use for females compared with males.

RESULTS

In total, 18 604 patients of average age 48.1 were identified, and 18% were female. Helmet use was greater in females than males (48.0% vs 34.2%, p<0.001). Compared with helmeted males, helmeted females had greater rates of serious head injury (37.7% vs 29.9%, p<0.001) despite less injury overall. In sex-disaggregated models, helmet use reduced odds of intracranial haemorrhage and death in males (p<0.001) but not females. In sex-combined models, helmets conferred to females significantly less odds reduction for severe head injury (p=0.002), intracranial bleeding (p<0.001), skull fractures (p=0.001), cranial surgery (p=0.006) and death (p=0.017). There was no difference for cervical spine fracture.

CONCLUSIONS

Bicycle helmets may offer less protection to females compared with males. The cause of this sex or gender-based difference is uncertain, but there may be intrinsic incompatibility between available helmets and female anatomy and/or sex disparity in helmet testing standards.

Analysis of the severity of vehicle-bicycle crashes with data mining techniques

INTRODUCTION

Although cycling is increasingly being promoted for transportation, the safety concern of bicyclists is one of the major impediments to their adoption. A thorough investigation on the contributing factors to fatalities and injuries involving bicyclist.

METHOD

This paper designs an integrated data mining framework to determine the significant factors that contribute to the severity of vehicle-bicycle crashes based on the crash dataset of Victorian, Australia (2013-2018). The framework integrates imbalanced data resampling, learning-based feature extraction with gradient boosting algorithm and marginal effect analysis. The top 10 significant predictors of the severity of vehicle-bicycle crashes are extracted, which gives an area under ROC curve (AUC) value of 0.8236 and computing time as 37.8 s.

RESULTS

The findings provide insights for understanding and developing countermeasures or policy initiatives to reduce severe vehicle-bicycle crashes.

Exploring bicyclist injury severity in bicycle-vehicle crashes using latent class clustering analysis and partial proportional odds models

INTRODUCTION

Bicyclists are more vulnerable compared to other road users. Therefore, it is critical to investigate the contributing factors to bicyclist injury severity to help provide better biking environment and improve biking safety. According to the data provided by National Highway Traffic Safety Administration (NHTSA), a total of 8,028 bicyclists were killed in bicycle-vehicle crashes from 2007 to 2017. The number of fatal bicyclists had increased rapidly by approximately 11.70% during the past 10 years (NHTSA, 2019).

METHODS

This paper conducts a latent class clustering analysis based on the police reported bicycle-vehicle crash data collected from 2007 to 2014 in North Carolina to identify the heterogeneity inherent in the crash data. First, the most appropriate number of clusters is determined in which each cluster has been characterized by the distribution of the featured variables. Then, partial proportional odds models are developed for each cluster to further analyze the impacts on bicyclist injury severity for specific crash patterns.

RESULTS

Marginal effects are calculated and used to evaluate and interpret the effect of each significant explanatory variable. The model results reveal that variables could have different influence on the bicyclist injury severity between clusters, and that some variables only have significant impacts on particular clusters.

CONCLUSIONS

The results clearly indicate that it is essential to conduct latent class clustering analysis to investigate the impact of explanatory variables on bicyclist injury severity considering unobserved or latent features. In addition, the latent class clustering is found to be able to provide more accurate and insightful information on the bicyclist injury severity analysis. Practical Applications: In order to improve biking safety, regulations need to be established to prevent drinking and lights need to be provided since alcohol and lighting condition are significant factors in severe injuries according to the modeling results.

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.

Applying fast and frugal tree heuristic algorithm to identify factors influencing crash severity of bicycle-vehicle crashes in Tamilnadu

Though bicycle as a mode of transport has many environmental and societal benefits as well as health benefits, bicyclists are one of the most vulnerable road users. According to the report by the Ministry of Road Transport and Highways (MoRTH, 2017), there is a sharp increase in the number of fatal victims in respect of bicyclists in 2017 over 2016. The number of cyclists killed jumped from 2585 in 2016 to 3559 in 2017, a 37.7% increase. In the present study, we present the analysis of the effect of the crash, geometric, environmental and cyclist characteristics on the bicycle-vehicle involved collisions by using the crash dataset of nine years (2009-2017) from Tamilnadu RADMS (Road Accident Data Management System) database with the application of fast and frugal tree (FFT) heuristic algorithm. The complete dataset (9978 crashes) was divided into two separate datasets: training data (6984 crashes) for the development of model and testing data (2984 crashes) for the performance evaluation. FFT algorithm identifies five major hues or variable attributes that influence the severity of bicycle crashes. The five major hues include the number of lanes, road separation, intersection, colliding vehicle type and road category. From the results of the present study, FFT acts as a complementary tool to other complex machine learning algorithms such as support vector machines, random forest, logistic regression and CART. The findings of the present study provide important insights for reducing the severity of bicycle-involved crashes at the planning and operations levels.

Modelling injury severity of bicyclists in bicycle-car crashes at intersections

Bicyclists are vulnerable road users as they are not protected during a road collision. Although numerous studies have been conducted to understand the parameters contributing to bicyclist's injury severity, most of these studies have focused on the relationship between crash severity and road, environmental, vehicle and human demographic parameters. No study has been found that investigated the relationship of bicyclist's injury severity with speed and mass of both vehicles, as well as other crash dynamics aspects. This study developed a modelling framework to investigate the effect of variables such as speed, mass and crash angle on bicyclist's injury severity in bicycle-car crashes at intersections. A combination of Newtonian Mechanics and statistical analysis was utilised to develop this theory. This modelling process followed a two-step approach. In the first step, Newtonian Mechanics was used to develop numerical models to estimate the impact force applied to the bicyclist. Variables affecting the associated impact forces were then identified. In the second step, a mixed binary logistic regression model was developed to estimate injury severity of a bicycle-vehicle crash as a function of mass of both vehicles, speed of both vehicles before and after the crash, restraint use and age of bicyclist. Transport Accident Commission (TAC) validated crash data was used to develop the model. The results of the numerical models showed that kinetic energy of the car before crash and kinetic energy of the bicycle after crash are important parameters affecting the injury severity of the cyclist in bicycle-vehicle crashes. The results of the mixed binary logistic regression model confirmed that the addition of kinetic energy of the car before crash and the kinetic energy of the bicycle post-crash had a statistically significant effect on injury severity of bicyclist. The results further showed that older bicyclists were involved in higher severity crashes and helmet-wearing reduced the injury severity of the bicyclist.

Fifty Years of Accident Analysis & Prevention: A Bibliometric and Scientometric Overview

Accident Analysis & Prevention (AA&P) is a leading academic journal established in 1969 that serves as an important scientific communication platform for road safety studies. To celebrate its 50^th anniversary of publishing outstanding and insightful studies, a multi-dimensional statistical and visualized analysis of the AA&P publications between 1969 and 2018 was performed using the Web of Science (WoS) Core Collection database, bibliometrics and mapping-knowledge-domain (MKD) analytical methods, and scientometric tools. It was shown that the annual number of AA&P's publications has grown exponentially and that over the course of its development, AA&P has been a leader in the field of road safety, both in terms of innovation and dissemination. By determining its key source countries and organizations, core authors, highly co-cited published documents, and high burst-strength publications, we showed that AA&P's areas of focus include the "effects of hazard and risk perception on driving behavior", "crash frequency modeling analysis", "intentional driving violations and aberrant driving behavior", "epidemiology, assessment and prevention of road traffic injuries", and "crash-injury severity modeling analysis". Furthermore, the key burst papers that have played an important role in advancing research and guiding AA&P in new directions - particularly those in the fields of crash frequency and crash-injury severity modeling analyses were identified. Finally, a modified Haddon matrix in the era of intelligent, connected and autonomous transportation systems is proposed to provide new insights into the emerging generation of road safety studies.

Bicycle safety in Bogotá: A seven-year analysis of bicyclists' collisions and fatalities

Road safety research in low- and middle-income countries is limited, even though ninety percent of global road traffic fatalities are concentrated in these locations. In Colombia, road traffic injuries are the second leading source of mortality by external causes and constitute a significant public health concern in the city of Bogotá. Bogotá is among the top 10 most bike-friendly cities in the world. However, bicyclists are one of the most vulnerable road-users in the city. Therefore, assessing the pattern of mortality and understanding the variables affecting the outcome of bicyclists' collisions in Bogotá is crucial to guide policies aimed at improving safety conditions. This study aims to determine the spatiotemporal trends in fatal and nonfatal collision rates and to identify the individual and contextual factors associated with fatal outcomes. We use confidence intervals, geo-statistics, and generalized additive mixed models (GAMM) corrected for spatial correlation. The collisions' records were taken from Bogotá's Secretariat of Mobility, complemented with records provided by non-governmental organizations (NGO). Our findings indicate that from 2011 to 2017, the fatal bicycling collision rates per bicyclists' population have remained constant for females while decreasing 53 % for males. Additionally, we identified high-risk areas located in the west, southwest, and southeast of the city, where the rate of occurrence of fatal events is higher than what occurs in other parts of the city. Finally, our results show associated risk factors that differ by sex. Overall, we find that fatal collisions are positively associated with factors including collisions with large vehicles, the absence of dedicated infrastructure, steep terrain, and nighttime occurrence. Our findings support policy-making and planning efforts to monitor, prioritize, and implement targeted interventions aimed at improving bicycling safety conditions while accounting for gender differences.

The novel approaches to classify cyclist accident injury-severity: Hybrid fuzzy decision mechanisms

In this study, two novel fuzzy decision approaches, where the fuzzy logic (FL) model was revised with the C4.5 decision tree (DT) algorithm, were applied to the classification of cyclist injury-severity in bicycle-vehicle accidents. The study aims to evaluate two main research topics. The first one is investigation of the effect of road infrastructure, road geometry, street, accident, atmospheric and cyclist related parameters on the classification of cyclist injury-severity similarly to other studies in the literature. The second one is examination of the performance of the new fuzzy decision approaches described in detail in this study for the classification of cyclist injury-severity. For this purpose, the data set containing bicycle-vehicle accidents in 2013-2017 was analyzed with the classic C4.5 algorithm and two different hybrid fuzzy decision mechanisms, namely DT-based converted FL (DT-CFL) and novel DT-based revised FL (DT-RFL). The model performances were compared according to their accuracy, precision, recall, and F-measure values. The results indicated that the parameters that have the greatest effect on the injury-severity in bicycle-vehicle accidents are gender, vehicle damage-extent, road-type as well as the highly effective parameters such as pavement type, accident type, and vehicle-movement. The most successful classification performance among the three models was achieved by the DT-RFL model with 72.0 % F-measure and 69.96 % Accuracy. With 59.22 % accuracy and %57.5 F-measure values, the DT-CFL model, rules of which were created according to the splitting criteria of C4.5 algorithm, gave worse results in the classification of the injury-severity in bicycle-vehicle accidents than the classical C4.5 algorithm. In light of these results, the use of fuzzy decision mechanism models presented in this study on more comprehensive datasets is recommended for further studies.

Review of road accident analysis using GIS technique

The number of road crashes is significantly growing worldwide. In the transportation sector, accident outcomes are usually the loss of lives and injuries. To avoid further damages, a tool entitled geographical information system (GIS) could be helpful. GIS has the most demanding tools used to analyze road accidents and road design that can be noteworthy in traffic accident prevention. The purpose of this review is to propose the superlative approach of GIS applicable to accident analysis in different circumstances. The reviewed statistical results of accidents are performed by GIS but the numerical study is not consummate by GIS. Mainly, four essential GIS techniques are introduced and discussed in this review paper to simulate road accidents and suggest some prolific accident analysis tools for road safety.

An analysis of risk factors associated with road crash severities in Namibia

Road crash is a leading cause of death and disabilities in Namibia and other developing countries. Based on recent trends, the World Health Organization indicated that progress to realize Sustainable Development Goal (SDG) target 3.6 - which calls for a 50% reduction in the number of road traffic deaths by 2020 - remains far from sufficient. To contribute to efforts in reducing road fatalities in Namibia, this study examined risk factors associated with the severity of crashes recorded in the country. Mixed logit modelling methodology was adopted to address the problem of unobserved heterogeneity in injury severity analysis. Model estimation results reveal that collision with pedestrians, head-on collisions, ran-off road collisions and crashes involving high occupancy passenger vehicles were more likely to result in fatalities and severe injuries. The findings and recommendations of this study are expected to enhance countermeasure implementation to reduce road crashes in Namibia.

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

INTRODUCTION

Bicyclists are vulnerable users in the shared asset like roadways. However, people still prefer to use bicycles for environmental, societal, and health benefits. In India, the bicycle plays a role in supporting the mobility to more people at lower cost and are often associated with the urban poor. Bicyclists represents one of the road user categories with highest risk of injuries and fatalities. According to the report by the Ministry of Road Transport and Highways (Accidents, 2017) in India, there is a sharp increase in the number of fatal victims for bicyclists in 2017 over 2016. The number of cyclists killed jumped from 2,585 in 2016 to 3,559 in 2017, a 37.7% increase.

METHOD

Few studies have only investigated the crash risk perceived by the bicyclists while interacting with other road users. The present paper investigates the injury severity of bicyclists in bicycle-vehicle crashes that occurred in the state of Tamilnadu, India during the nine year period (2009-2017). The analyses demonstrate that dividing bicycle-vehicle collision data into five clusters helps in reducing the systematic heterogeneity present in the data and identify the hidden relationship between the injury severity levels of bicyclists and cyclists demographics, vehicle, environmental, temporal cause for the crashes.

RESULTS

Latent Class Clustering (LCC) approach was used in the present study as a preliminary tool for the segmentation of 9,978 crashes. Later, logistic regression analysis was used to identify the factors that influence bicycle crash severity for the whole dataset as well as for the clusters that were obtained from the LCC model. Results of this study show that combined use of both techniques reveals further information that wouldn't be obtained without prior segmentation of the data. Few variables such as season, weather conditions, and light conditions were significant for certain clusters that were hidden in the whole dataset. This study can help domain experts or traffic safety researchers to segment traffic crashes and develop targeted countermeasures to mitigate injury severity.

Exploring injury severity in head-on crashes using latent class clustering analysis and mixed logit model: A case study of North Carolina

Although only 2 % of crashes are head-on crashes in the United States, they account for over 10 % of all crash-related fatalities. This study aims to investigate the contributing factors that affect the injury severity of head-on crashes and develop appropriate countermeasures. Due to the unobserved heterogeneity inherent in the crash data, a latent class clustering analysis is firstly conducted to segment the head-on crashes into relatively homogeneous clusters. Then, mixed logit models are developed to further explore the unobserved heterogeneity within each cluster. Analyses are performed based on the data collected from the Highway Safety Information System (HSIS) from 2005 to 2013 in North Carolina. The estimated parameters and associated marginal effects are combined to interpret significant variables of the developed models. The proposed method is able to uncover the heterogeneity within the whole dataset and the homogeneous clusters. Results of this research can provide more reliable and insightful information to engineers and policy makers regarding the contributing factors to head-on crashes.

Personality and Behavioral Predictors of Cyclist Involvement in Crash-Related Conditions

In recent years, the increasing rate of road crashes involving cyclists with a disproportionate overrepresentation in injury statistics has become a major concern in road safety and public health. However, much remains unknown about factors contributing to cyclists’ high crash rates, especially those related to personal characteristics. This study aims to explore the influence of cyclist personality traits and cycling behaviors on their road safety outcomes using a mediated model combining these constructs. A total of 628 cyclists completed an online questionnaire consisting of questions related to cycling anger, impulsiveness, normlessness, sensation seeking, risky cycling behaviors, and involvement in crash-related conditions in the past year. After the psychometric properties of the employed scales were examined, the relationships among the tested constructs were investigated using structural equation modeling. The results showed that cyclists’ crash risks were directly predicted by risky cycling behaviors and cycling anger, and the effects of cycling anger, impulsiveness, as well as normlessness on crash risks, were mediated by cycling behaviors. The current findings provide insight into the importance of personality traits in impacting cycling safety and could facilitate the development of evidence-based prevention and promotion strategies targeting cyclists in China.

Investigating injury severities of motorcycle riders: A two-step method integrating latent class cluster analysis and random parameters logit model

Due to the wide existence of heterogeneous nature in traffic safety data, traditional methods used to investigate motorcyclist rider injury severity always lead to masking of some underlying relationships which may be critical for the formulation of efficient safety countermeasures. Instead of applying one single model to the whole dataset or focusing on pre-defined crash types as done in previous studies, the present study proposes a two-step method integrating latent class cluster analysis and random parameters logit model to explore contributing factors influencing the injury levels of motorcyclists. A latent class cluster approach is first used to segment the motorcycle crashes into relatively homogeneous clusters. A mixed logit model is then elaborately developed for each cluster to identify its unique influential factors. The analysis was based on the police-reported crash dataset (2015-2017) of Hunan province, China. The goodness-of-fit indicators and the Receiver Operating Characteristic curves show that the proposed method is more accurate when modeling the riders' injury severities. The heterogeneity found in each homogeneous subgroup supports the application of the random parameters logit model in the study. More importantly, the results demonstrate that segmenting motorcycle crashes into relatively homogeneous clusters as a preliminary step helps to uncover some important influencing factors hidden in the whole-data model. The proposed method is proved to have great potential for accounting for the source of heterogeneity. The injury risk factors identified in specific cases provide more reliable information for traffic engineers and policymakers to improve motorcycle traffic safety.

Intersections and Non-Intersections: A Protocol for Identifying Pedestrian Crash Risk Locations in GIS

Intersection and non-intersection locations are commonly used as spatial units of analysis for modeling pedestrian crashes. While both location types have been previously studied, comparing results is difficult given the different data and methods used to identify crash-risk locations. In this study, a systematic and replicable protocol was developed in GIS (Geographic Information System) to create a consistent spatial unit of analysis for use in pedestrian crash modelling. Four publicly accessible datasets were used to identify unique intersection and non-intersection locations: Roadway intersection points, roadway lanes, legal speed limits, and pedestrian crash records. Two algorithms were developed and tested using five search radii (ranging from 20 to 100 m) to assess the protocol reliability. The algorithms, which were designed to identify crash-risk locations at intersection and non-intersection areas detected 87.2% of the pedestrian crash locations (r: 20 m). Agreement rates between algorithm results and the crash data were 94.1% for intersection and 98.0% for non-intersection locations, respectively. The buffer size of 20 m generally showed the highest performance in the analyses. The present protocol offered an efficient and reliable method to create spatial analysis units for pedestrian crash modeling. It provided researchers a cost-effective method to identify unique intersection and non-intersection locations. Additional search radii should be tested in future studies to refine the capture of crash-risk locations.

Intersection characteristics that influence collision severity and cost

INTRODUCTION

Traffic engineers require robust tools to assist with their day-to-day decision making, and there is no better example of this than traffic signal warrants. North American traffic signal warrant systems are lacking in how they incorporate motor-vehicle collisions from both a severity and prediction perspective. The objective of this study was to produce reliable collision costs for the development of improved traffic signal warrants that accounted for the variations in severity that practitioners should expect based on the characteristics of the intersection being studied.

METHOD

The primary data used for this analysis were from the National Automotive Sampling System (NASS) Crashworthiness Data System, with adjustments from the NASS General Estimates System and Fatality Accident Reporting System. Generalized ordered logit models were used to identify the most significant intersection characteristics, which were then used to segregate the data to determine expected the collision severity profiles and average costs of both casualty and total collisions at intersections.

RESULTS

The average collision at a signalized intersection was found have a lower severity than the average collision at a stop-controlled intersection. A combination of posted speed limit, urban/rural, and divided/undivided were identified as the most significant intersection characteristics in most cases and were used to delineate the data for developing collision cost estimates.

CONCLUSIONS

Posted speed limit, rural/urban land use, and the presence of divided approaches are intersection characteristics that traffic engineers can readily determine and/or control for that have significant effects on intersection collision severity. Practical applications: The collision costs produced through this process give traffic engineers a reliable estimate that can provide a more substantial foundation for justifying a proposed change in intersection traffic control.

Integrating Spatial and Temporal Approaches for Explaining Bicycle Crashes in High-Risk Areas in Antwerp (Belgium)

The majority of bicycle crash studies aim at determining risk factors and estimating crash risks by employing statistics. Accordingly, the goal of this paper is to evaluate bicycle–motor vehicle crashes by using spatial and temporal approaches to statistical data. The spatial approach (a weighted kernel density estimation approach) preliminarily estimates crash risks at the macro level, thereby avoiding the expensive work of collecting traffic counts; meanwhile, the temporal approach (negative binomial regression approach) focuses on crash data that occurred on urban arterials and includes traffic exposure at the micro level. The crash risk and risk factors of arterial roads associated with bicycle facilities and road environments were assessed using a database built from field surveys and five government agencies. This study analysed 4120 geocoded bicycle crashes in the city of Antwerp (CA, Belgium). The data sets covered five years (2014 to 2018), including all bicycle–motorized vehicle (BMV) crashes from police reports. Urban arterials were highlighted as high-risk areas through the spatial approach. This was as expected given that, due to heavy traffic and limited road space, bicycle facilities on arterial roads face many design problems. Through spatial and temporal approaches, the environmental characteristics of bicycle crashes on arterial roads were analysed at the micro level. Finally, this paper provides an insight that can be used by both the geography and transport fields to improve cycling safety on urban arterial roads.

Factors impacting bicyclist lateral position and velocity in proximity to commercial vehicle loading zones: Application of a bicycling simulator

There is little research on the behavioral interaction between bicycle lanes and commercial vehicle loading zones (CVLZ) in the United States. These interactions are important to understand, to preempt increasing conflicts between truckers and bicyclists. In this study, a bicycling simulator experiment examined bicycle and truck interactions. The experiment was successfully completed by 48 participants. The bicycling simulator collected data regarding a participant's velocity and lateral position. Three independent variables reflecting common engineering approaches were included in this experiment: pavement marking (L1: white lane markings with no supplemental pavement color, termed white lane markings, L2: white lane markings with solid green pavement applied on the conflict area, termed solid green, and L3: white lane markings with dashed green pavement applied on the conflict area, termed dashed green), signage (L1: No sign and L2: a truck warning sign), and truck maneuver (L1: no truck in CVLZ, L2: truck parked in CVLZ, and L3: truck pulling out of CVLZ). The results showed that truck presence does have an effect on bicyclist's performance, and this effect varies based on the engineering and design treatments employed. Of the three independent variables, truck maneuvering had the greatest impact by decreasing mean bicyclist velocity and increasing mean lateral position. It was also observed that when a truck was present in a CVLZ, bicyclists had a lower velocity and lower divergence from right-edge of bike lane on solid green pavement, and a higher divergence from the right-edge of bike lane was observed when a warning sign was present.

Investigation of driver injury severities in rural single-vehicle crashes under rain conditions using mixed logit and latent class models

Due to limited visibility and low skid resistance on road surface, single-vehicle crashes under rain conditions, especially those occurred in rural areas, are more likely to result in driver incapacitating injuries and fatalities. A three-year crash dataset including all rural single-vehicle crashes under rain conditions from 2012 to 2014 in four South Central states, i.e., Texas, Arkansas, Oklahoma, and Louisiana, are selected in this paper to analyze the impact factors on driver injury severity. The mixed logit model (MLM) and the latent class model (LCM) are developed on the same dataset. Several parsimony indices, e.g., AIC and BIC, and as well as McFadden pseudo r-squared, are calculated for all the models to evaluate their respective performance. Results show that choosing the uniform distribution as the prior for random parameters could better improve the goodness-of-fit of the MLM than using normal and lognormal distributions. In addition, the two-class LCM also shows superiority when compared to three- and four-class LCMs. Finally, a careful comparison between these two models is conducted, and the results indicate that the LCM has a slightly better performance in analyzing the aforementioned dataset in this study. Model estimation results show that curve, on grade, signal control, multiple lanes, pickup, straight, drug/alcohol impaired, and seat belt not used can significantly increase the probability of incapacitating injuries and fatalities for drivers in the two models. On the other hand, wet, male, semi-trailer, and young can significantly decrease the probability of incapacitating injuries and fatalities for drivers. This study provides an insightful understanding of the effects of these attributes on rural single-vehicle crashes under rain conditions and beneficial references for developing effective countermeasures for severe injury prevention.

Estimation of bicycle crash modification factors (CMFs) on urban facilities using zero inflated negative binomial models

The objective of this study was to develop crash modification factors (CMFs) for bicycle crashes for different roadway segment and intersection facility types in urban areas. The study used four years (2011-2014) of crash data from Florida to quantify the safety impacts of roadway and traffic characteristics, bicycle infrastructure, and bicycle activity data on bicycle crashes. A cross-sectional analysis using Generalized Linear Model (GLM) approach with Zero Inflated Negative Binomial (ZINB) distribution was adopted to develop the relevant regression models in this study. Lane width, speed limit, and grass in the median were observed to have positive impacts on reducing bicycle crashes. On the contrary, presence of sidewalk and sidewalk barrier were found to increase the bicycle crash probabilities. Increased bicycle activity was found to reduce the bicycle crash probabilities on segments, while increased bicycle activity resulted in higher bicycle crash probabilities at intersections. Bus stops were found to increase the bicycle crash probabilities at intersections, whereas, protected signal control had a positive impact on bicycle safety. This research provides a greater insight into how various characteristics affect bicycle safety, a topic that is seldom considered by researchers and practitioners.

Identifying high-risk built environments for severe bicycling injuries

INTRODUCTION

This study is aimed at filling part of the knowledge gap on bicycling safety in the built environment by addressing two questions. First, are built environment features and bicyclist injury severity correlated; and if so, what built environment factors most significantly relate to severe bicyclist injuries? Second, are the identified associations varied substantially among cities with different levels of bicycling and different built environments?

METHODS

The generalized ordered logit model is employed to examine the relationship between built environment features and bicyclist injury severity.

RESULTS

Bicyclist injury severity is coded into four types, including no injury (NI), possible injury (PI), evident injury (EI), and severe injury and fatality (SIF). The findings include: (a) higher percentages of residential land and green space, and office or mixed use land are correlated with lower probabilities of EI and SIF; (b) land use mixture is negatively correlated with EI and SIF; (c) steep slopes are positively associated with bicyclist injury severity; (d) in areas with more transit routes, bicyclist injury is less likely to be severe; (e) a higher speed limit is more likely to correlate with SIF; and (f) wearing a helmet is negatively associated with SIF, but positively related to PI and EI. Practical applications: To improve bicycle safety, urban planners and policymakers should encourage mixed land use, promote dense street networks, place new bike lanes in residential neighborhoods and green spaces, and office districts, while avoiding steep slopes. To promote bicycling, a process of evaluating the risk of bicyclists involving severe injuries in the local environment should be implemented before encouraging bicycle activities.

Crash histories, safety perceptions, and attitudes among Virginia bicyclists

INTRODUCTION

Cycling injury and fatality rates are on the rise, yet there exists no comprehensive database for bicycle crash injury data.

METHOD

Widely used for safety analysis, police crash report datasets are automobile-oriented and widely known to under-report bicycle crashes. This research is one attempt to address gaps in bicycle data in sources like police crash reports. A survey was developed and deployed to enhance the quality and quantity of available bicycle safety data in Virginia. The survey captures bicyclist attitudes and perceptions of safety as well as bicycle crash histories of respondents.

RESULTS

The results of this survey most notably show very high levels of under-reporting of bicycle crashes, with only 12% of the crashes recorded in this survey reported to police. Additionally, the results of this work show that lack of knowledge concerning bicycle laws is associated with lower levels of cycling confidence. Count model results predict that bicyclists who stop completely at traffic signals are 40% less likely to be involved in crashes compared to counterparts who sometimes stop at signals. In this dataset, suburban and urban roads with designated bike lanes had more favorable injury severity profiles, with lower percentages of severe and minor injury crashes compared to similar roads with a shared bike/automobile lane or no designated bike infrastructure.

Bicycle helmets - To wear or not to wear? A meta-analyses of the effects of bicycle helmets on injuries

A meta-analysis has been conducted of the effects of bicycle helmets on serious head injury and other injuries among crash involved cyclists. 179 effect estimates from 55 studies from 1989-2017 are included in the meta-analysis. The use of bicycle helmets was found to reduce head injury by 48%, serious head injury by 60%, traumatic brain injury by 53%, face injury by 23%, and the total number of killed or seriously injured cyclists by 34%. Bicycle helmets were not found to have any statistically significant effect on cervical spine injury. There is no indication that the results from bicycle helmet studies are affected by a lack of control for confounding variables, time trend bias or publication bias. The results do not indicate that bicycle helmet effects are different between adult cyclists and children. Bicycle helmet effects may be somewhat larger when bicycle helmet wearing is mandatory than otherwise; however, helmet wearing rates were not found to be related to bicycle helmet effectiveness. It is also likely that bicycle helmets have larger effects among drunk cyclists than among sober cyclists, and larger effects in single bicycle crashes than in collisions with motor vehicles. In summary, the results suggest that wearing a helmet while cycling is highly recommendable, especially in situations with an increased risk of single bicycle crashes, such as on slippery or icy roads.