Bicycle Collisions Research Articles

Head injuries related to bicycle collisions and helmet use – an observational study

Objectives In Denmark, the use of bicycles is widespread, and head injuries are often seen in cyclists involved in collisions. Despite the well-known effects of using a helmet to reduce head injuries, using helmets is not mandatory in Denmark. The primary objective of this study was to provide data regarding injury outcomes and helmet usage. Methods Participants were bicyclists who sustained head injuries in bicycle collisions and were assessed by the Copenhagen Emergency Medical Services between 1 January 2016; and 15 June 2019. Patients with suspected head injury were identified in an electronic prehospital patient record. Data were linked to the Danish National Patient Registry to retrieve the diagnosis and were categorized into head injury or no head injury based on the diagnosis. Adjusted logistic regression analyses were reported with odds ratios and corresponding confidence intervals to assess the risk of head injury while adjusting for risk factors like age, sex, alcohol consumption, occurrence during weekends and traumatic brain injury. Results A total of 407 patients were included in this study. Within this entity, 247 (61%) had sustained a head injury. The use of a helmet was reported in one-third of the included patients. Among the head-injured patients, 13% sustained moderate to severe head injuries. Patients with suspected alcohol involvement were significantly less likely to report the use of a helmet. Helmet use reduced the risk of head injury with an odds ratio of 0.52, (95% CI 0.31 − 0.86). In high-energy trauma, the use of a helmet showed a significant reduction in the risk of sustaining a head injury with an odds ratio of 0.28, (95% CI 0.12 − 0.80). Conclusions In this study, using a helmet was associated with a significantly decreased risk of head injury; this association was even more significant in high-energy trauma.

Assessing the impact of low-speed limit zones' policy implications on cyclist safety: Evidence from the UK

As part of the shift in policy to promote active travel, the UK's Manual for Streets (MfS) guidance proposed new approaches for priority intersections design in residential areas, promoting lower geometric visibilities for drivers on minor road arms to reduce collision severity, and the low-speed (i.e., 20 mph) limit zone policy. However, the relationship between speed, visibility, and collisions has not been explored comprehensively for these low-speed limit residential areas. This study aimed to evaluate the impact of shorter visibilities on vehicle-bicycle collisions at priority intersections in Portsmouth, UK. Using piecewise Structural Equations Modeling and Artificial Neural Network models, 120 locations in the city were analysed to understand the complex interplay between vehicles' speed, speed limits, visibilities, geometries, traffic flows, and collisions. The research question was whether shorter visibilities supported policy intentions by reducing bicycle collisions in low-speed limit areas, as MfS suggested. The results show that greater visibility 9 m back to left and right positively correlates with higher road traffic collisions. In comparison, greater left-hand visibility at 2.4 m back from minor arms has a counter-effect that improves safety. This supports the notion that shorter visibility in residential areas aid cyclist safety. This study contributes significantly to the literature by shedding light on the impact of shorter visibility on road safety. The findings have important implications for transportation policy and practice, highlighting the importance of the MfS approach for improving cyclist safety in the design of priority intersections.

Chest wall injury fracture patterns are associated with different mechanisms of injury: a retrospective review study in the United States.

Research on rib fracture management has exponentially increased. Predicting fracture patterns based on the mechanism of injury (MOI) and other possible correlations may improve resource allocation and injury prevention strategies. The Chest Injury International Database (CIID) is the largest prospective repository of the operative and nonoperative management of patients with severe chest wall trauma. The purpose of this study was to determine whether the MOI is associated with the resulting rib fracture patterns. We hypothesized that specific MOIs would be associated with distinct rib fracture patterns. The CIID was queried to analyze fracture patterns based on the MOI. Patients were stratified by MOI: falls, motor vehicle collisions (MVCs), motorcycle collisions (MCCs), automobile-pedestrian collisions, and bicycle collisions. Fracture locations, associated injuries, and patient-specific variables were recorded. Heat maps were created to display the fracture incidence by rib location. The study cohort consisted of 1,121 patients with a median RibScore of 2 (range, 0-3) and 9,353 fractures. The average age was 57±20 years, and 64% of patients were male. By MOI, the number of patients and fractures were as follows: falls (474 patients, 3,360 fractures), MVCs (353 patients, 3,268 fractures), MCCs (165 patients, 1,505 fractures), automobile-pedestrian collisions (70 patients, 713 fractures), and bicycle collisions (59 patients, 507 fractures). The most commonly injured rib was the sixth rib, and the most common fracture location was lateral. Statistically significant differences in the location and patterns of fractures were identified comparing each MOI, except for MCCs versus bicycle collisions. Different mechanisms of injury result in distinct rib fracture patterns. These different patterns should be considered in the workup and management of patients with thoracic injuries. Given these significant differences, future studies should account for both fracture location and the MOI to better define what populations benefit from surgical versus nonoperative management.

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

ObjectiveThe Finnish national Traffic Safety Strategy 2022–2026 seeks to halve the number of road fatalities and serious injuries from 2020 to 2030. The strategy states that better information on bicycle crashes is needed for safety promotion. The aim of this study was to describe the demographics, injury characteristics, alcohol involvement, and helmet use of severely injured cyclists and to compare single bicycle crashes (falling alone or hitting a fixed object) to collisions. Material and methodsWe identified all bicycle crashes between 2006 and 2021 from the Helsinki Trauma Registry (HTR). Variables analysed were basic patient demographics, Abbreviated Injury Scale (AIS) codes, AIS 3+ injuries, injured body regions, patient Injury Severity Score (ISS) and New Injury Severity Score (NISS), 30-day in-hospital mortality, ICU length of stay, injury mechanism, alcohol use by the injured cyclists, and helmet use. ResultsOf the 325 severe (NISS >15) cycling injury patients in the HTR, 53.5 % were injured in single crashes and 46.5 % in collisions with a moving object. Most (71.4 %) patients were men and mean age of all patients was 54.1 years (SD 16.7). Alcohol was detected in 23.1 % of cases and more often in single crashes (32.8 %) than in collisions (11.9 %). Less than a third (29.2 %) of all cyclists wore a helmet; those who wore a helmet had fewer serious (AIS 3+) head injuries than those who did not. Cyclists injured in collisions had higher ISS and NISS scores than those injured in single crashes. Serious (AIS 3+) injuries in extremities or in pelvic girdle were more common in collisions than in single crashes. ConclusionsAmong severely injured cyclists, single bicycle crashes were more common; alcohol was more often detected in single bicycle crashes than in collisions. Overall injury severity was higher in collisions than in single crashes. Helmet users had less AIS 3+ head injuries than non-users. Attention should be focused on preventing alcohol-related cycling injuries, promoting use of bicycle helmets, and more precise and comprehensive documentation of bicycle crashes in health care units.

BSafe-360: An All-in-One Naturalistic Cycling Data Collection Tool.

The popularity of bicycles as a mode of transportation has been steadily increasing. However, concerns about cyclist safety persist due to a need for comprehensive data. This data scarcity hinders accurate assessment of bicycle safety and identification of factors that contribute to the occurrence and severity of bicycle collisions in urban environments. This paper presents the development of the BSafe-360, a novel multi-sensor device designed as a data acquisition system (DAS) for collecting naturalistic cycling data, which provides a high granularity of cyclist behavior and interactions with other road users. For the hardware component, the BSafe-360 utilizes a Raspberry Pi microcomputer, a Global Positioning System (GPS) antenna and receiver, two ultrasonic sensors, an inertial measurement unit (IMU), and a real-time clock (RTC), which are all housed within a customized bicycle phone case. To handle the software aspect, BSafe-360 has two Python scripts that manage data processing and storage in both local and online databases. To demonstrate the capabilities of the device, we conducted a proof of concept experiment, collecting data for seven hours. In addition to utilizing the BSafe-360, we included data from CCTV and weather information in the data analysis step for verifying the occurrence of critical events, ensuring comprehensive coverage of all relevant information. The combination of sensors within a single device enables the collection of crucial data for bicycle safety studies, including bicycle trajectory, lateral passing distance (LPD), and cyclist behavior. Our findings show that the BSafe-360 is a promising tool for collecting naturalistic cycling data, facilitating a deeper understanding of bicycle safety and improving it. By effectively improving bicycle safety, numerous benefits can be realized, including the potential to reduce bicycle injuries and fatalities to zero in the near future.

Health impacts of bike sharing system – A case study of Shanghai

BackgroundBike sharing systems have been promoted in many countries. Bike sharing can alleviate urban air pollution and reduce road congestion during peak hours in the morning and evening. In addition, using shared bicycles as a daily commuting tool can help users increase their daily exercise volume. This study evaluates the health effects of shared bicycle use. The evaluation of health is prospective, and we utilize current data to evaluate and analyze the health of future users. The primary health considerations for users include physical activity, PM2.5 levels, and collision rates. Physical exercise might be hindered by high concentrations of PM2.5. Thus, while riding in conditions of very high PM2.5 concentration, the pollutants taken by the traveler will hurt the body and counteract the advantages of physical exercise. This research demonstrates that cycling during periods of low or moderate PM2.5 concentrations should lead to an overall reduction premature mortality. Data and methodsWe perform a health assessment study to quantify the health risks and benefits of car trip substitution by bike trip. We collected the cycling data from Mobike shared bicycles operator in Shanghai established in August 2016. From August 1st to August 31st, 2018, there were 1,023,603 orders and 3,036,936 cycling users. During the computational analysis, we examined three factors: physical activity, PM2.5 pollution, and bicycle collision rate, and then summed the results to determine the cyclist's risk of early death. Three scenarios are created to estimate the annual expected number of deaths (increasing or reduced) due to physical activity, road traffic fatalities, and air pollution. ResultsAir pollution exposure was assessed using variations in the background fine particulate matter (PM2.5) concentration, which was 45 μg/m3 on average in August 2016 in Shanghai. Cycling under these settings, the advantages of physical exercise exceeded the hazards posed by pollution. When PM2.5 concentrations exceed 45 μg/m3, seven to eight people will avert early mortality for every 306,936 users. It means 23–26 per million cyclists would avoid premature death. When PM2.5 concentrations exceed 68 μg/m3, 1 to 2 people will be significantly harmed by air pollution and 4–7 out of every million cyclists are negatively affected by high PM2.5 concentrations. ConclusionsThese results demonstrate that shared cycling can avoid premature mortality. In addition, from the perspective of urban pollution, commuters choosing bicycles instead of cars to travel can reduce urban air pollution, improve air quality, and reduce traffic jams in the morning and evening peaks. Further research on the co-benefits of shared bicycles would be helpful to planners.

Mechanisms of Injury in Adolescent Trauma Patients With a Positive Marijuana Screen.

Marijuana use among adolescents may have increased after its legalization in the United States. An association between violence and marijuana use in adults has been demonstrated in previous reports. We hypothesized that adolescent trauma patients presenting with a positive marijuana screen (pMS) are more likely to have been injured by gunfire or knives and will have more severe injuries overall, compared to patients with a negative marijuana screen (nMS). The 2017 Trauma Quality Improvement Program database was queried for adolescent (13-17years old) pMS patients and compared to adolescents who tested negative for all substance/alcohol. Patients with positive polysubstance/alcohol were excluded. From 8257 adolescent trauma patients, 2060 (24.9%) had a pMS with a higher rate of males in the pMS group (76.3% vs 64.3%, P < .001). The pMS group presented more frequently after gun (20.3% vs 7.9%, P < .001) or knife trauma (5.7% vs 3.0%, P < .001) and less frequently after falls (8.9% vs 15.6%, P < .001) and bicycle collisions (3.3% vs 4.8%, P = .002). The rate of serious thoracic injury (AIS ≥3) was higher for pMS patients (16.7% vs 12.0%, P < .001), and more pMS patients required emergent operation (14.9% vs 10.6%, P < .001). In our adolescent patient population, one quarter tested positive for marijuana. These patients are more likely to be injured by guns and/or knives suffering serious injuries, and often require immediate operative intervention. A marijuana cessation program for adolescents can help improve outcomes in this high-risk patient group.

In-Depth Bicycle Collision Reconstruction: From a Crash Helmet to Brain Injury Evaluation

Traumatic brain injury (TBI) is a prevalent injury among cyclists experiencing head collisions. In legal cases, reliable brain injury evaluation can be difficult and controversial as mild injuries cannot be diagnosed with conventional brain imaging methods. In such cases, accident reconstruction may be used to predict the risk of TBI. However, lack of collision details can render accident reconstruction nearly impossible. Here, we introduce a reconstruction method to evaluate the brain injury in a bicycle-vehicle collision using the crash helmet alone. Following a thorough inspection of the cyclist's helmet, we identified a severe impact, a moderate impact and several scrapes, which helped us to determine the impact conditions. We used our helmet test rig and intact helmets identical to the cyclist's helmet to replicate the damage seen on the cyclist's helmet involved in the real-world collision. We performed both linear and oblique impacts, measured the translational and rotational kinematics of the head and predicted the strain and the strain rate across the brain using a computational head model. Our results proved the hypothesis that the cyclist sustained a severe impact followed by a moderate impact on the road surface. The estimated head accelerations and velocity (167 g, 40.7 rad/s and 13.2 krad/s2) and the brain strain and strain rate (0.541 and 415/s) confirmed that the severe impact was large enough to produce mild to moderate TBI. The method introduced in this study can guide future accident reconstructions, allowing for the evaluation of TBI using the crash helmet only.

Collision and fall rates by regular cyclists on bicycle facilities

The effects on safety of bicycle facilities are unclear. The aim of this study is to estimate bicycle collisions and falls’ rates on bicycle facilities in Lyon (France). This study uses data from a survey of regular cyclists that collected information regarding crash history as well as their regular routes. A sample of 780 regular cyclists’ routes was analyzed. Bicycle crash data (n=274) and cyclist exposure (n=764 633 km) were collected for 2 years to calculate relative event rate (collision, fall and injury rates). Relative rates of collision on bicycle facilities are significantly higher than on road. Differences for fall rates are not significant, except for contraflow cycle lane which seems unsafe. Relative rates of injury suggest that travelling on bicycle facilities is not necessarily safer than on the road. Results provide some evidence for cycle facilities design and planning.

자전거 교통사고 다발지역 예측을 위한 딥러닝 모형의 적용

자전거 교통사고 다발지역 예측을 위한 딥러닝 모형의 적용

S1905 A Rare Case of Acute Pancreatitis With Normal Lipase

Introduction: Acute pancreatitis (AP) is an inflammatory disease of the pancreas that usually presents with epigastric pain radiating to the back. AP is a critical condition that can lead to multi-organ failure if not diagnosed early. It is diagnosed with 2 of the following: characteristic abdominal pain, imaging consistent with acute pancreatitis, and lipase levels greater than 3 times the upper limit of normal. The negative predictive value of lipase for diagnosing acute pancreatitis is between 94% and 100%. We present a rare case of acute pancreatitis with normal serum lipase levels. Our findings highlight the vital utility of imaging for diagnosing acute pancreatitis in the setting of normal lipase levels. Case Description/Methods: An 80-year-old female presented with a 1-day history of epigastric pain radiating to her back associated with nausea, vomiting, and loss of appetite. Her past medical history was significant for hypertension, NIDDM II, and atrial fibrillation. She had no recent medication changes and took amlodipine, metformin, metoprolol, and apixaban. Previous surgeries included cholecystectomy and appendectomy. She abstained from alcohol, tobacco, or recreational drug use. She reported abdominal trauma from bicycle collision one week prior to arrival. Vital signs were remarkable for T 101.2 F. She did not appear in acute distress. Her physical exam was remarkable for epigastric tenderness on palpation. Murphy, Cullen, and Grey-Turner signs were absent. Laboratory evaluation revealed WBC 12.8 k/uL, lipase 29 U/L, amylase 52 U/L, AST 13 U/L, ALT 21 U/L, ALP 58 U/L (Table). Lipid profile and EtOH levels were within normal limits. CT of the abdomen and pelvis revealed interstitial edematous pancreatitis (Figure). She was started on aggressive intravenous fluid resuscitation, metoclopramide for nausea, and intravenous acetaminophen for pain. Her abdominal pain progressively resolved, and she tolerated a full diet by day 3. Discussion: AP is a critical condition that requires early diagnosis. The negative predictive value of elevated lipase levels for diagnosing AP ranges from 94% to 100%. Our patient was diagnosed with acute pancreatitis due to trauma in the setting of normal lipase levels. We demonstrate the utility of imaging in diagnosing acute pancreatitis in the setting of normal serum lipase levels.Figure 1.: CT scan of the abdomen revealed peripancreatic fat stranding and interstitial edema consistent with findings of pancreatitis. Table 1. - Laboratory trends are displayed here. Lipase remained within normal limits through the 4 days measured. Admission Day 1 Day 2 Day 3 Day 4 Normal Range Hemoglobin 8.8 7.8 9.3 8.6 9.0 12.0 - 15.0 g/dL WBC 12.8 7.4 5.9 5.7 6.2 3.4 - 11.0 10*3/uL Amylase 52 39 29 - 103 U/L Lipase 29 12 13 - 60 U/L Calcium 8.9 8.1 8.9 8.8 8.4 8.4 - 10.2 mg/dL Alkaline Phosphatase 58 44 46 59 54 40 - 129 U/L ALT 21 16 29 17 11 10 - 50 U/L AST 13 10 14 12 16 13 - 39 U/L

Risk prediction and factor analysis of rider’s injury severity in passenger car and E-bike accidents based on interpretable machine learning

Casualties and property losses caused by the passenger car and electric bicycle crash accidents increased year by year. Assessment of the relevant risk factors of injury severity in passenger car and electric bicycle crashes could help to mitigate crash severity. This study uses an emerging machine learning method to predict the relationship between the risk factors and bicyclist accident injury severity in passenger car-electric bicycle collision accidents. The model’s performance is compared and evaluated based on accuracy, precision, recall, F1-Score, area under curve (AUC), and receiver operating characteristic curve (ROC). An interpretable machine learning framework Shapley additive explanations (SHAP) is used to further analyze the relationship between risk factors and bicyclist injury severity. It is found that we can adopt the light gradient boosting machine (LightGBM) algorithm after hyper-parameter optimization to get the highest accuracy (94.85%), precision (95.2%), recall (94.9%), F1-Score (95%), and AUC (0.993) based on the accident data of electric bicycles and passenger cars in the China in-depth accident study dataset from 2014 to 2018. The model can be used to assess new accident cases based on the model learning rate. There are some new findings in the aspects of bicyclists’ physical factors and electric vehicle characteristics. The throwing distance of the bicyclist has a positive impact on the injury severity. The bicyclist is more likely to suffer more serious injuries in a crash accident when the bicyclist is male, or shorter. Electric bicycles have a smaller handlebar width. In general, the lower handlebar height is, or lower saddle height is, the more serious the bicyclist’s injury is. Safety training for drivers can help reduce the injury severity in crash accidents and improve traffic safety.

Spatial variation in bicycling risk based on crowdsourced safety data

Bicycling‐related injury data are difficult to obtain from official reports, which capture only about 20% of crashes and often lack coordinates, injury outcomes, and narratives needed for understanding where and why incidents occurred. Crowdsourced data on bicycling safety provides new opportunities for the study of bicycling injury and risk. Our goal was to quantify factors that influence the spatial variation in unsafe bicycling across a city, based on self‐reports of bicycling incidents. To meet this goal, we leveraged BikeMaps.org, a global tool for reporting bicycling safety incidents, drawing on data from Metro Vancouver. We summarized incident conditions that led to injury, developed a model to identify predictors of injury using random forest regression, and mapped bicycling incident hot spots. Our results demonstrate that injuries from bicycling incidents are associated with older and younger bicyclists, downhill slopes, parked cars, recreation and weekend rides, falls, and single bicycle incidents with infrastructure, roads, and railroads. The broad range of incidents reported to BikeMaps.org allows us to add evidence that falls and single bicycle collisions are major causes of injury. Also, we demonstrate the value of attributing safety hot spots with contextual details to identify infrastructure interventions that can reduce injury for bicyclists.

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.

Exploring Factors Associated with Cyclist Injury Severity in Vehicle-Electric Bicycle Crashes Based on a Random Parameter Logit Model

Electric bicyclists are vulnerable road users and play an important role in traffic safety. The focus of this research is on analyzing cyclists’ injury severity in vehicle-electric bicycle collisions. It is an exploratory analysis that was conducted based on samples obtained from video data provided by the police of Xi’an China. Three types of severity include fatal, injury, and property-damage-only (PDO). A random parameter logit (RPL) model was specified to gain more insights into factors related to the injury severity level, including human behaviors, vehicle characteristics, roadway attributes, and environmental conditions. Some factors not included in previous research were introduced into this study, especially precrash behaviors of drivers and cyclists. The direct pseudo-elasticity effects of variables were compared to investigate the stability of individual parameter estimates on the severity categories. The results indicated that variables that significantly increment the probability of fatal accidents were as follows: driver violation behaviors (speeding, red-light violation, driving in the opposite direction), cyclist violation behaviors (speeding, red-light violation), day of time (nighttime), visibility restrictions (fixed obstacles), and vehicle type (larger bus, small truck, and larger truck). Based on these findings, we suggested measures such as strengthening law enforcement by installing cameras, implementing zero tolerance for cyclist violations, promoting education by completing training courses for cyclists, and enhancing traffic safety awareness through educational activities. The research results can provide a theoretical basis for formulating strategies to improve cyclist safety.

Potential Effectiveness of Bicycle-Automatic Emergency Braking using the Washtenaw Area Transportation Study Data Set

In the United States, fatalities from vehicle–bicycle crashes have been increasing since 2010. A total of 857 cyclists were struck and killed in 2018 which is an increase from 623 fatalities in 2010. One promising countermeasure is Automatic Emergency Braking (AEB), which can help prevent and/or mitigate many vehicle–bicycle crashes. AEB is a vehicle-based system that can detect and mitigate an impending crash. The goal of this study was to elucidate U.S. vehicle–bicycle crashes and examine related factors to estimate AEB effectiveness. This study used a unique in-depth vehicle–bicycle crash study dataset collected under the collaboration of the Washtenaw Area Transportation Study (WATS) and the Toyota Collaborative Research Center conducted in southeast Michigan from 2011 to 2013. The WATS database provides in-depth investigations of vehicle–bicycle crashes in the United States. The characteristics of the WATS vehicle–bicycle crashes were validated against the Fatality Analysis Reporting System and the General Estimate System. The WATS database cases were examined to estimate the potential effectiveness of AEB to prevent or mitigate vehicle–bicycle collisions. In 60% of the WATS cases, cyclists were in the road for more than 1 s before impact. Assuming that a hypothetical AEB system requires a minimum of 1 s for detection and brake activation, these collisions would potentially be avoided or mitigated. However, for the remaining cases with less than 1 s of time to react (40% of cases), that AEB system would be challenged to avoid or mitigate the collision.

Routing for safer rides: a space syntax approach to predict bicycle collisions in a Brazilian city

Abstract In order to broaden the discussion on the safety of bicycle transport, this paper uses the analytical capacity of the spatial syntax applied to the recording of accidents involving cyclists in the city of Rolândia-PR, located in the Southern Region of Brazil. With the availability of 535 reports of trips made by bicycle mode, collected in the survey origin and destination of the city, a database was elaborated where each segment of the road received its numerical value from the loading of trips, and its corresponding values of choice and integration, generated in the Depthmap software. As a result, the relationship between the point of accident and the trip record by bicycle was refuted. In contrast, the angular values of choice and integration were sufficient to explain the occurrence of accidents involving cyclists in each segment of the municipal urban network, statistically proven through the generation of a generalized linear model. The contribution of this study focuses on the validity of using spatial syntax to predict safer routes, which is considered a theoretical-methodological approach that identifies priority routes for the implementation of specific infrastructure for bicycle transport.

Factors influencing the long-term hospitalization of bicyclists and motorcyclists with oral and maxillofacial injuries.

Because bicyclists and motorcyclists with oral and maxillofacial injuries often suffer from disabilities requiring long-term treatment, reducing the severity of such injuries is a valuable objective for improving these people's quality of life (QOL). The aims of this study were, first, to present the prevalence and patterns of oral and maxillofacial injuries of bicyclists and motorcyclists and to compare the features of these injuries and, second, to determine the factors contributing to long-term hospitalization for these patients and to propose effective preventive measures. This was a single-center retrospective analysis. Hospital records from 2011 through 2018 were reviewed for all patients who had sustained oral and maxillofacial injuries in bicycle or motorcycle collisions and had presented at a university hospital which was the only hospital in the region attended 24hours per day by oral and maxillofacial surgeons. Characteristics of the oral and maxillofacial injuries, injury severity, and factors influencing the length of hospitalization were examined. Records of 130 patients (82 bicyclists and 48 motorcyclists) with a mean age of 28.0years were analyzed. Thirty-three patients (25.4%) had maxillofacial fractures, with 41 fracture lines while 103 patients (79.2%) had dental injuries and 57 patients (43.8%) had soft-tissue injuries. The distribution and prevalence of oral and maxillofacial injuries were similar for bicyclists and motorcyclists. However, motorcyclists had significantly higher Abbreviated Injury Scale (AIS) scores for facial injuries and the maximum AIS score than did bicyclists. According to a multiple regression analysis, the number of fracture lines and the requirement for intermaxillary fixation were independent factors influencing long-term hospitalization (standard regression coefficients: 6.795 and 6.715, respectively; P<.001). The number of fracture lines and the use of intermaxillary fixation were independent factors influencing long-term hospitalization of both bicyclists and motorcyclists with oral and maxillofacial injuries.

Adult Bicycle Collisions: Impact of Helmet Use on Head and Cervical Spine Injury

BackgroundNo states currently require adult bicycle riders to wear helmets. Opponents of a universal helmet law argue that helmets may cause a greater torque on the neck during collisions, potentially increasing the risk of cervical spine fracture (CSF). This assumption has not been supported by data for motorcyclists. Therefore, we sought to evaluate the risk of CSF and cervical spinal cord injury (CSCI) in helmeted bicyclists (HBs) versus nonhelmeted bicyclists (NHBs) involved in collisions. We hypothesize that in adult HBs, there is an increased incidence of CSF and injury but lower rates of severe head injury and mortality than in NHBs. Materials and methodsThe Trauma Quality Improvement Program (2010-2016) was queried for adult bicyclists involved in collisions, comparing HBs with NHBs. A multivariable logistic regression model was used for analysis. ResultsOf 25,047 bicyclists, 14,234 (56.8%) were NHBs. NHBs were more often black (13.3% versus 2.3%, P < 0.001) and screened positive for alcohol on admission (25.7% versus 4.6%, P < 0.001). NHBs had lower rates of CSF (17.7% versus 23.7%, P < 0.001) and CSCI (1.1% versus 1.9%, P < 0.001) but higher rates of mortality (4.9% versus 2.2%, P < 0.001) and a higher risk for severe head injury (odds ratio [OR]: 2.26, 2.13-2.40, P < 0.001). After adjusting for covariates, NHBs had a higher risk of mortality (OR: 2.38, 2.00-2.84, P < 0.001) but lower risk of CSF (OR: 0.66 0.62-0.71, P < 0.001) and CSCI (OR: 0.53, 0.42-0.68, P < 0.001). ConclusionsHBs involved in collisions have a higher risk of CSF and CSCI; however, NHBs have a higher risk of severe head injury and mortality. Consideration for a universal helmet law among bicyclists and ongoing research regarding helmet development is needed.

Head kinematics study of E-bicycle rider in car to E-bicycle side collisions

In recent years, car to bicycle collisions take place more and more frequently, which have attracted the attention of some organizations and engineers. They are trying to make some rules for cars to realize better bicyclist protection, including defining new head impact location area and head impact velocity in pedestrian protection tests. However, car to E-bicycle collisions occur more than car to bicycle collisions in China. Therefore, vehicle to E-bicycle collisions should be researched to define appropriate head impact location area and head impact velocity in pedestrian protection tests for cars in Chinese market. In this article, the head kinematics of E-bicycle riders in car to E-bicycle side collisions are studied. First, through analyzing data from China In-Depth Accident Study Database (which conducts field investigation, analysis, and research on traffic accidents in China), some representative conditions and key parameters in car to E-bicycle collisions are extracted. Second, a condition of car to E-bicycle side collision from above analysis is simulated. Third, an experiment according to the above condition is performed and the finite element models in the above simulation are validated. Then, a series of conditions are simulated by using the validated models, and some factors affecting head impact locations and head impact velocities in car to E-bicycle side collisions are studied. Finally, some factors that affect the head kinematics of E-bicycle riders in car to E-bicycle collisions are identified and some results of head movement in these collisions are concluded, such as head impact locations and head impact velocities.