Bicycle-motor Vehicle Crashes Research Articles

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.

Integrating machine learning into path analysis for quantifying behavioral pathways in bicycle-motor vehicle crashes

The behavioral pathways in traffic crashes describe the chained linkages among contributing factors, pre-crash road user behaviors, and crash outcomes. Bicyclists are more vulnerable than motorists on road and their pre-crash behaviors play an essential role in the pathways leading to injuries. The objective of this study is to develop a methodological framework that integrates machine learning with path analysis to quantify behavioral pathways in bicycle-motor vehicle crashes. Specifically, two sets of models are developed for predicting: 1) pre-crash behaviors given contributing factors and 2) bicyclist injury severity given contributing factors including pre-crash behaviors. The path analysis chains machine learning models to establish the indirect linkages between contributing factors and injury severities through correlates of pre-crash behaviors. This study explored five machine learning methods, including Random Forest (RF), Categorical Naive Bayes (CNB), Support vector machine (SVM), AdaBoost (Boost), and Neural network (NN). To reduce the bias of any single model, this study proposes a technique to combine model estimates by averaging marginal effects. This study used a dataset containing 9,296 bicycle-motor vehicle crashes to demonstrate the application of the framework. Across five machine learning models, the signs of marginal effects generally agree but their magnitudes vary substantially. The pre-crash behavior of “bicyclist failed to yield” increases bicyclist injury severity by 1.11%. The path analysis results highlighted contributing factors related to risky pre-crash behaviors that lead to severe injuries, such as bicyclist intoxication. The framework is expected to support agencies’ decision-making to improve cycling safety by reducing unsafe behaviors on roads.

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.

Uncertainty matters: Bayesian modeling of bicycle crashes with incomplete exposure data

BackgroundOne major challenge faced by neighborhood-level bicycle safety analysis is the lack of complete and reliable exposure data for the entire area under investigation. Although the conventional travel-diary surveys, together with the emerging smartphone fitness applications and bike-sharing systems, provide straightforward and valuable opportunities to estimate territory-wide bicycle activities, the obtained ridership suffers inherently from underreporting. MethodsWe introduced the Bayesian simultaneous-equation model as a sound methodological alternative here to address the uncertainty arising from incomplete exposure data when modeling bicycle crashes. The proposed method was successfully fitted to a crowdsourced dataset of 792 bicycle–motor vehicle (BMV) crashes aggregated from 209 neighborhoods over a 3-year period in Hong Kong. ResultsOur analysis empirically demonstrated the bias due to omission of activity-based exposure measures or to the direct use of cycling distance extracted from the travel-diary survey without correcting for incompleteness. By modeling bicycle activities and the frequency of BMV crashes simultaneously, we also provided new evidence that an expansion of bicycle infrastructure was likely associated with a significant increase in cycling levels and a substantial reduction in the risk of BMV crashes, despite a slight increase in the absolute number of BMV crashes. ConclusionsOur approach is promising in adjusting for the uncertainty in raw exposure data, extrapolating the missing exposure values, and untangling the linkage among built environment, bicycle activities, and the frequency of BMV crashes within a unified framework. To promote safer cycling, designated facilities should be provided to consecutively separate cyclists from motor vehicles.

Exploring injury severity of bicycle-motor vehicle crashes: A two-stage approach integrating latent class analysis and random parameter logit model

Bicycle–motor vehicle (BMV) crashes have been identified as a major type of traffic accident affecting transportation safety. In order to determine the characteristics of BMV crashes in cold regions, this study presents an analysis using police-reported data from 2015 to 2017 on BMV crashes in Shenyang, China. A two-stage approach integrating latent class analysis (LCA) and the random parameter logit (RP-logit) model is proposed to identify specific crash groups and explore their contributing factors. First, LCA was used to classify data into several homogenous clusters, and then the RP-logit model was established to identify significant factors in the whole data model and the cluster-based model from LCA. The proposed two-stage approach can maximize the heterogeneity effects both among clusters and within clusters. Results show that three significant factors in the cluster-based model are obscured by the whole data model in which male cyclists are associated with a higher risk of fatality, especially in the winter. Additionally, differences exist in the exploration of factors due to the characteristics of clusters; thus, countermeasures for specific crash groups should be implemented. This research can provide references for regulators to develop targeted policies and reduce injury severity in BMV crashes in cold regions.

Addressing unobserved heterogeneity in the analysis of bicycle crash injuries in Scotland: A correlated random parameters ordered probit approach with heterogeneity in means

This paper investigates the determinants of injury severities in single-bicycle and bicycle-motor vehicle crashes by estimating correlated random parameter ordered probit models with heterogeneity in the means. This modeling approach extends the frontier of the conventional random parameters by capturing the likely correlations among the random parameters and relaxing the fixed nature of the means for the mixing distributions of the random parameters. The empirical analysis was based on a publicly available database of police crash reports in the UK using information from crashes occurred on urban and rural carriageways of Scotland between 2010 and 2018. The model estimation results show that various crash, road, location, weather, and driver or cyclist characteristics affect the injury severities for both categories of crashes. The heterogeneity-in-the-means structure allowed the incorporation of a distinct layer of heterogeneity in the statistical analysis, as the means of the random parameters were found to vary as a function of crash or driver/cyclist characteristics. The correlation of the random parameters enabled the identification of complex interactive effects of the unobserved characteristics captured by road, location and environmental factors. Overall, the determinants of injury severities are found to vary between single-bicycle and bicycle-motor vehicle crashes, whereas a number of common determinants are associated with different effects in terms of magnitude and sign. The comparison of the proposed methodological framework with less sophisticated ordered probit models demonstrated its relative benefits in terms of statistical fit, explanatory power and forecasting accuracy as well as its potential to capture unobserved heterogeneity to a greater extent.

Auditory Alerts and Safety with Simulated Bicycles and Motor Vehicles

Bicycling has become an increasingly popular and environmentally friendly active transportation modality for many commuters across the nation. Consequently, as ridership increases so does the rate of bicycle–motor vehicle crashes, many of which are caused by reduced bicycle visibility and driver inattention. Therefore, one effective solution to improve bicyclist safety may be through the use of an audible bicycle alarm system to alert both the driver and the rider. A study was conducted to determine whether a unique auditory alert would be effective at reducing crash rates and whether a localized alert (i.e., an alert presented from the driver’s perspective) would improve the driver’s responsiveness in avoiding a potential collision. A driving simulator study tested car horn sounds, an experimental bike alert, and no auditory alert in different potential collision scenarios to measure collision rates and other collision avoidance metrics. Findings indicated that the experimental bike alert contributed to fewer relative crashes than the horn sound and no sound on bicycles, motor vehicles were struck more frequently than bicycles, collisions were more likely to occur from the front than the sides, and collisions were more likely for drivers going straight than when making turns. Taken together, the findings suggest that an alarm designed to be specifically compatible with bicycles is more effective than auditory alerts from other sources.

Comparison of motor vehicle-involved e-scooter and bicycle crashes using standardized crash typology

Introduction: The market share of e-scooters in the United States has proliferated in cities: 86 million trips were made on shared e-scooters in 2019, a more than 100% increase compared to 2018. However, the interaction of e-scooters with other road users and infrastructure remains uncertain. Method: This study scrutinized 52 e-scooter and 79 bicycle police-reported crashes in Nashville, Tennessee, from April 2018 to April 2020 from the Tennessee Integrated Traffic Analysis Network (TITAN) database. We used descriptive analysis and a recent prototype version of the Pedestrian and Bicycle Crash Analysis Tool (PBCAT) to classify crashes based on the locations of the crashes relative to roadway segments or intersections, as well as the maneuver of the motor vehicle and e-scooter/bicycle relative to the motor vehicle. Results: Two crash typologies can explain the majority of e-scooter crashes, while bicycle crashes are distributed over several crash typologies. Additionally, 1 in 10 e-scooter- and bicycle-motor vehicle crashes leads to the injury or fatality of the e-scooter rider or bicyclist. Furthermore, we noted statistically significant differences in spatial and temporal distribution, demographics, lighting conditions, and crash distance from home for e-scooter and bicycle crashes. Conclusions: The police crash report provides a comprehensive picture of e-scooter safety complementing existing literature. We found that e-scooter crash characteristics do not fully overlap with features of bicycle crashes. Practical Implications: A generalized engineering, education, and enforcement treatment to reduce and prevent e-scooter and bicycle crashes, injuries, and fatalities might not result in equal outcomes for each mode. More rigorous enforcement could be implemented to deter e-scooters riders under the age of 18 years and e-scooter safety campaigns could target female riders.

Behavioral pathways in bicycle-motor vehicle crashes: From contributing factors, pre-crash actions, to injury severities

Introduction: This study performed a path analysis to uncover the behavioral pathways (from contributing factors, pre-crash actions to injury severities) in bicycle-motor vehicle crashes. Method: The analysis investigated more than 7,000 bicycle-motor vehicle crashes in North Carolina between 2007 and 2014. Pre-crash actions discussed in this study are actions of cyclists and motorists prior to the event of a crash, including “bicyclist failed to yield,” “motorist failed to yield,” “bicyclist overtaking motorist,” and “motorist overtaking bicyclist.” Results: Model results show significant correlates of pre-crash actions and bicyclist injury severity. For example, young bicyclists (18 years old or younger) are 23.5% more likely to fail to yield to motor traffic prior to the event of a crash than elder bicyclists. The “bicyclist failed to yield” action is associated with increased bicyclist injury severity than other actions, as this behavior is associated with an increase of 5.88 percentage points in probability of a bicyclist being at least evidently injured. The path analysis can highlight contributing factors related to risky pre-crash actions that lead to severe injuries. For example, bicyclists traveling on regular vehicle travel lanes are found to be more likely to involve the “bicyclist failed to yield” action, which resulted in a total 44.38% (7.04% direct effect + 37.34% indirect effect) higher likelihood of evident or severe injuries. The path analysis can also identify factors (e.g., intersection) that are not directly but indirectly correlated with injury severity through pre-crash actions. Practical Applications: This study offers a methodological framework to quantify the behavioral pathways in bicycle-motor vehicle crashes. The findings are useful for cycling safety improvements from the perspective of bicyclist behavior, such as the educational program for cyclists.

Factors influencing the injury severity of single-bicycle crashes

The majority of research on bicyclist injury severity relates to bicycle-motor vehicle crashes, even though single-bicycle crashes make up more than half of bicycle crashes. This study explores the factors related to the injury severity outcome of single-bicycle crashes. We use single-bicycle crash data obtained from medical records collected in the period 2010–2015 combined with road maintenance data. The data includes three injury severity categories: ‘severe injury’, ‘slight injury’, ‘no injury’.The relation between the factors surrounding single-bicycle crashes and the resulting injury severity is estimated using a latent class ordered probit model. The model estimation identifies three latent classes where the likelihood of cyclist membership depends on the bicyclist's age and gender. Furthermore, several factors appear to affect the likelihood of injuries in single-bicycle crashes. These are the road geometry (i.e. if the crash occurred on a bicycle lane or a road section), maintenance level, and the interaction between road geometry and maintenance level. The findings suggest that single-bicycle crashes on road sections result in more severe injuries than single-bicycle crashes on bicycle lanes. The largest effect is seen when a single-bicycle crash occurs on a road section with a poorly maintained bicycle lane being available. Crashes on low volume roads with few bicyclists are also related to an increased probability of severe injury as well as crashes occurring after dark.

Driver knowledge of cycling-related road rules in Queensland, Australia

Objectives Violations of road rules are common in bicycle-motor vehicle crashes, but little is known about how much this results from lack of knowledge of the rules. This article addresses the research questions of how well do drivers know the road rules related to interacting with cyclists, and what factors influence their level of knowledge. Methods An online survey compared drivers who reported riding bicycles on Queensland roads in the previous 12 months (“cyclists”: n = 2,839) and those who did not (“drivers”: n = 4,070). A list of 10 plausible road rules (four were not actual rules) relating to interactions between motor vehicles and bicycles was presented, and participants were asked “Which of the following road rules applies in Queensland?” The response options for each item were “Yes,” “No,” and “I don’t know”. Results The percentages correct varied markedly across the rules, from 86.5% to 38.7% for cyclists and from 72.2% to 20.9% for drivers. Incorrect responses were generally more common than “Don’t know” responses (cyclists: 22.3% incorrect, 9.8% “Don’t know”; drivers: 29.9% incorrect, 19.6% “Don’t know”). The hierarchical regression analysis confirmed that cyclists had better road rule knowledge than drivers. In the cyclist-only model, more frequent riding was significantly associated with better road rule knowledge but explained only 3% of the total variance. Older cyclists and those living in more urbanized areas had better knowledge but age and location of residence together added only 1% to the variance explained. In the driver-only model, knowledge was better for male, older, more educated participants and those living in more urbanized areas but these variables together accounted for only 5% of the variance. Conclusions The results of this study suggest that improving knowledge of some current road rules could be beneficial for cycling safety but for some other rules, improved knowledge could be detrimental for cycling safety and changing the rules to reflect shared perceptions of safe behavior may be more beneficial. For each of the rules, further research is needed to examine the links between knowledge and compliance, and the influence of compliance on the risks of bicycle–motor vehicle crashes and near misses.

The perception of bicycle crashes with and without motor vehicles: Which crash types do older and middle-aged cyclists fear most?

Several studies have focused on the perceived risk of bicycle crashes (irrespective of crash types) and concluded that cycling near high volumes of motor vehicles deters people from cycling. The perceived risk of bicycle crash types (with or without motor vehicles) has not yet been studied. Cyclists, both in countries with low and high levels of cycling participation, are substantially more likely to sustain severe injuries in single-bicycle crashes than in bicycle-motor vehicle crashes. This questionnaire study sets out to compare which bicycle crash types are perceived to cause most hospitalizations among cyclists. The study comprised cyclists over 55 years in the Netherlands, and over 40 years in the Belgian regions of Flanders (a region with high cycling participation), Brussels and Wallonia (regions with low cycling participation). The majority of cyclists (60%) perceive bicycle-motor vehicle crashes cause most hospitalizations among cyclists. This percentage is greatest in the areas of Brussels and Wallonia and lowest in the Netherlands. Cyclists who were involved in a bicycle-motor vehicle crash themselves are more likely to regard this crash type as the most common cause of hospitalizations among cyclists, while cyclists over 60 years who were involved in a crash without a motor vehicle are more likely to perceive that crash type as the most common cause. The smaller perception bias in the study areas with higher cycling participation – particularly the Netherlands and to a somewhat lesser degree Flanders – is probably due to bicycle infrastructure being more separated from high-speed motor traffic, leaving cyclists less exposed. The outcomes show that cyclists underestimate the likelihood of severe injuries due to single-bicycle crashes. New interventions should raise the awareness of the risk of single-bicycle crashes and provide solutions to avoid such crashes.

Modeling bicyclist injury severity in bicycle–motor vehicle crashes that occurred in urban and rural areas: a mixed logit analysis

The purpose of this paper is to identify and compare the contributing factors to bicyclist injury severity in urban and rural areas. Two mixed logit models are developed for both the urban and rural areas separately to identify factors that significantly contribute to the injury severity outcome of bicyclists resulting from bicycle–motor vehicle crashes. Data collected from 2007 to 2014 in North Carolina are utilized for the model development. The model estimation results show that factors including bicyclist age from 25 to 54, driver age under 25, vehicle speed, and divided road are found to significantly affect the injury severity outcome of bicyclists in bicycle–motor vehicle crashes in rural areas only. In contrast, factors including drivers age over 60, van, single unit truck, head-on crash, motorist overtaking bicyclist, two-way roadway, road condition, and crash time are found to have a significant impact on the injury severity of bicyclists in urban areas only.

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.

Young Driver Crashes with Cyclists: Identifying Training Opportunities

To understand where driver training should focus to contribute to improving the safety of cyclists, this study compared bicycle-motor-vehicle (BMV) crashes involving novice drivers (under 25 years) with those involving experienced drivers in the Australian states of Victoria, Queensland, and South Australia. Novice drivers were involved in only a small proportion of BMV crashes and were not over-represented on a per-license basis. For both driver groups, most crashes happened on lower speed roads, at intersections, and during the day. In contrast to expectations, the distribution of types of BMV crashes differed little between experienced and novice drivers. The absence of major differences between experienced and novice drivers may result from learning opportunities being too infrequent in low-volume cycling countries, but this hypothesis needs further testing. A comparison between Queensland and Victoria showed three situations with a higher proportion of young driver crashes: in the evening in both states, Right through-opposing directions (Victoria only), and From footway-maneuvering (Queensland only). These patterns are likely to be indicative of young driver experiences. When their time on the road increases, so does their exposure to risk and to challenging driving conditions (e.g., driving in darkness). On the other hand, these patterns may also point to effects of legislation on young driver crashes, for instance cycling on the sidewalk in Queensland. The results suggest that training for novice drivers needs to supplement a wider strategy to improve cyclist safety (including infrastructure and traffic management improvements) and that training needs to be tailored to state-specific conditions.

Spatial characteristics of bicycle–motor vehicle crashes in Christchurch, New Zealand: A case-control approach

This paper aims to examine the risk of bicycle-motor vehicle crashes occurring on a network-wide level in Christchurch, New Zealand, based on the spatial characteristics present in the road environment. To achieve this, logistic regression was undertaken with a binary dependent variable (crash/non-crash) using a case-control strategy, with case sites being locations of reported crashes, while control sites were sampled from the road network in proportion to where people cycle. Due to the uncertainty of cycling flows in Christchurch, four logistic regression models were undertaken based on different route selection preferences.
 
 The results identified that the odds of a crash increased across all four models due to the presence of driveways or intersections, identifying that these characteristics are associated with an increase in crash risk. All of the models identified that the risk of a crash decreases with the presence of on-road cycle lanes, while crash risk due to the presence of specific planning zones or road classification varied across all of the models.

The role of driver’s situational awareness on right-hook bicycle-motor vehicle crashes

ObjectiveThe objective was to explore the effect of driver Situational Awareness (SA) on “right-hook” bicycle-motor vehicle crashes involving right turns into adjacent bicyclists. BackgroundPrevious literature suggests that improper allocation of motorists’ visual attention, inadequate surveillance, and poor SA are contributing factors to bicycle-motor vehicle crashes in other types of encounters. MethodFifty-one participants completed this driving simulator study. Right-turning motorists’ SA was measured using the SAGAT technique in the presence of a through-moving bicyclist in an adjacent bicycle lane during the latter portion of the green phase at a signalized intersection using a three (bicyclist’s relative position) by two (presence of oncoming left-turning vehicle) within-subject factorial design. Each participant made 21 right turns, nine of which were immediately followed by SA queries, and crash avoidance behavior was measured at the last intersection, which involved a crash-likely scenario. ResultsThe bicyclist’s position significantly influenced motorists’ overall SA (p < 0.05) and Level 2 SA (comprehension) (p < 0.05). Level 1 SA (perception) degraded when oncoming vehicles were present and the bicyclist was approaching from behind (p < 0.05). Level 3 SA (projection) degraded when the bicyclist was ahead of the motorist and oncoming vehicles were present (p < 0.05). Level 1 SA and crash occurrence were negatively correlated (rpbi = −0.3, p < 0.05). ConclusionMotorists focused more attention on cars in front of them and less attention on bicycles in the peripheral vision. A common cause of observed crashes in the simulator was detection error. The bicyclist approaching from behind the motorist is the most vulnerable to a right-turning motorist.

Influence of Type of Traffic Control on Injury Severity in Bicycle–Motor Vehicle Crashes at Intersections

Many studies have identified factors that contribute to bicycle–motor vehicle (BMV) crashes, but little is known about determinants of cyclist injury severity under different traffic control measures at intersections. Preliminary analyses of 5,388 police-reported BMV crashes from 2002 to 2014 from Queensland, Australia revealed that cyclist injury severity differed according to whether the intersection had a Stop/Give-way sign, traffic signals or no traffic control. Therefore, separate mixed logit models of cyclist injury severity (fatal/hospitalized, medically treated, and minor injury) were estimated. Despite similar distributions of injury severity across the three types of traffic control, more factors were identified as influencing cyclist injury severity at Stop/Give-way controlled intersections than at signalized intersections or intersections with no traffic control. Increased injury severity for riders aged 40–49 and 60+ and those not wearing helmets were the only consistent findings across all traffic control types, although the effect of not wearing helmets was smaller at uncontrolled intersections. Cyclists who were judged to be at fault were more severely injured at Stop/Give-way and signalized intersections. Speed zone influenced injury severity only at Stop/Give-way signs and appears to reflect differences in intersection design, rather than speed limits per se. While most BMV crashes occurred on dry road surfaces, wet road surfaces were associated with an increased cyclist injury severity at Stop/Give-way intersections. The results of this study will assist transport and enforcement agencies in developing appropriate mitigation strategies to improve the safety of cyclists at intersections.

The impact of weather, road surface, time-of-day, and light conditions on severity of bicycle-motor vehicle crash injuries.

This study examined whether environmental variables including weather, road surface, time-of-day, and light conditions were associated with the severity of injuries resulting from bicycle-motor vehicle crashes. Using log-binomial regressions, we analyzed 113 470 police reports collected between 2000 and 2014 in four U.S. states with environmental and injury severity information. "Severe" injuries included fatal and incapacitating injuries, and "non-severe" included non-incapacitating, possible or no-injuries. Light condition was significantly associated with the injury severity to the bicyclist with more severe injuries at dawn (RR = 1.62 [95%CI 1.35-1.94]) and during darkness (both lighted and unlighted roads: 1.32 [1.24-1.40], respectively, 1.57 [1.41-1.76]) as compared to daylight. In these conditions of low visibility, risk was further increased during early morning hours before 7 am (1.61 [1.22-2.13]). Crashes in low light conditions and during early morning hours are more likely to result in higher injury severity.

The role of intersection and street design on severity of bicycle-motor vehicle crashes

BackgroundSafety concerns are a major barrier to cycling. Intersection and street design variables such as intersection angles and street width might contribute to the severity of crashes and the safety...