Identifying motorist characteristics associated with youth bicycle-motor vehicle collisions

Blame attribution analysis of police motor vehicle collision reports involving child bicyclists

BACKGROUND

Injuries resulting from collisions between a bicyclist and driver are preventable and have high economic, personal and societal costs. Studying the language choices used by police officers to describe factors responsible for child bicyclist-motor vehicle collisions may help shift prevention efforts away from vulnerable road users to motorists and the environment. The overall aim was to investigate how police officers attribute blame in child (≤18 years) bicycle-motor vehicle collision scenarios.

METHODS

A document analysis approach was used to analyse Alberta Transportation police collision reports from Calgary and Edmonton (2016-2017). Collision reports were categorised by the research team according to perceived blame (child, driver, both, neither, unsure). Content analysis was then used to examine police officer language choices. A narrative thematic analysis of the individual, behavioural, structural and environmental factors leading to collision blame was then conducted.

RESULTS

Of 171 police collision reports included, child bicyclists were perceived to be at fault in 78 reports (45.6%) and adult drivers were perceived at fault in 85 reports (49.7%). Child bicyclists were portrayed through language choices as being irresponsible and irrational, leading to interactions with drivers and collisions. Risk perception issues were also mentioned frequently in relation to poor decisions made by child bicyclists. Most police officer reports discussed road user behaviours, and children were frequently blamed for collisions.

CONCLUSIONS

This work provides an opportunity to re-examine perceptions of factors related to motor vehicle and child bicyclist collisions with a view to prevention.

Police narrative reports: Do they provide end-users with the data they need to help prevent bicycle crashes?

Bicycles gained significant popularity among Americans in 2020. Greater investment in adequate bicycle safety facilities will be needed. Crash data from police will undoubtedly play a role in decision-making. This research evaluated the data quality of text narratives in police reports on bicycle crashes. The aims were to identify situations in which police officers wrote more detail in the narrative text, investigate if longer reports translate to more in-depth crash descriptions, examine the extent to which narrative texts cover details useful for those charged with bicycle safety. This is a 4-year retrospective cohort study of vehicle-vs-bicycle crashes that occurred between January 1, 2009, and December 31, 2012, in Boston, Massachusetts (USA). Police reports were matched with the Pedestrian and Bicycle Crash Analysis Tool (PBCAT) to measure how much information was captured and when reports were more likely to capture more information. Police reports only captured most information in one area of the standardized form (Crash Typing), with average total missingness of over 75%. Longer reports did reduce the amount of missingness, and officers were more likely to write longer reports when they were on the crash site, when there was an injury, when the crash involved an extended car door, and during the day. A 100% increase in the report's words was associated with a four-percentage point reduction in PBCAT missingness. While longer reports result in less missingness when measured against the standardized crash form, the average report still misses most of the information that the form would capture. We recommend that police departments adopt a standardized form to facilitate information capture at the scene of bicycle-vehicle crashes.

Enhancing trauma registries by integrating traffic records and geospatial analysis to improve bicyclist safety

BACKGROUND

Trauma registries are used to identify modifiable injury risk factors for trauma prevention efforts. However, these may miss factors useful for prevention of bicycle-automobile collisions, such as vehicle speeds, driver intoxication, street conditions, and neighborhood characteristics. We hypothesize that (GIS) analysis of trauma registry data matched with a traffic accident database could identify risk factors for bicycle-automobile injuries and better inform injury prevention efforts.

METHODS

The trauma registry of a US Level I trauma center was used retrospectively to identify bicycle-motor vehicle collision admissions from January 1, 2010, to December 31, 2018. Data collected included demographics, vitals, injury severity scores, toxicology, helmet use, and mortality.Matching with the Statewide Integrated Traffic Records System was done to provide collision, victim and GIS information. The GIS mapping of collisions was done with census tract data including poverty level scoring. Incident hot spot analysis to identify statistically significant incident clusters was done using the Getis Ord Gi* statistic.

RESULTS

Of 25,535 registry admissions, 531 (2.1%) were bicyclists struck by automobiles, 425 (80.0%) were matched to Statewide Integrated Traffic Records System. Younger age (odds ratio [OR], 1.026; 95% confidence interval [CI], 1.013-1.040, p < 0.001), higher census tract poverty level percentage (OR, 0.976; 95% CI, 0.959-0.993, p = 0.007), and high school or less education (OR, 0.60; 95 CI, 0.381-0.968; p = 0.036) were predictive of not wearing a helmet. Higher census tract poverty level percentage (OR, 1.019; 95% CI, 1.004-1.034; p = 0.012) but not educational level was predictive of toxicology positive-bicyclists in automobile collisions. Geographic information systems analysis identified hot spots in the catchment area for toxicology-positive bicyclists and lack of helmet use.

CONCLUSION

Combining trauma registry data and matched traffic accident records data with GIS analysis identifies additional risk factors for bicyclist injury. Trauma centers should champion efforts to prospectively link public traffic accident data to their trauma registries.

LEVEL OF EVIDENCE

Prognostic and Epidemiological, level III.

Analysis of fatal factors influencing accidents involving two-wheel electric vehicle drivers at intersections

With the development of society, the convenience offered by two-wheel electric vehicles has made them an ideal road-transportation option for daily short-distance travel. However, this also poses various road-safety concerns. This study aims to analyse the main factors causing fatal injuries at intersections for two-wheel electric vehicle drivers. This study uses 902 sets of data on two-wheel electric vehicle accidents that occurred in Shandong Province between 2014 and 2018 (obtained from the Shandong Tianhong Judicial Expertise Center database) to comprehensively analyse the nine factors that result in driver fatalities at road intersections. A dimensionality reduction of the factors via principal component analysis returned four main factors, namely the collision object, collision-object speed, running direction, and sight obstacles. A subsequent analysis of the main factors revealed that the most common collision object for two-wheel electric vehicle drivers is the automobile, with the most common speed during the collision being 50-60 km/h. Sight obstacles primarily include other vehicles as well as glaring lights. Two-wheel electric vehicles are most prone to accidents when turning left. This study comprehensively analyses the main factors that cause fatal injuries to two-wheel electric vehicle drivers at road intersections. Further investigations can be conducted to analyse the impact of the specific conditions of the two-wheel electric vehicle collisions on the mortality of drivers.