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

Introduction: Single Bicycle Crashes (SBCs) are common, and underreported in official statistics. In urban environments, light rail tram tracks are a frequent factor, however, they have not yet been the subject of engineering analysis. Method: This study employs video-based analysis at nine Dublin city centre locations and introduces a predictive model for crossing success on tram tracks, utilising cyclist crossing angles within a Surrogate Measure of Safety (SMoS) framework. Additionally, Convolutional Neural Networks (CNNs) were explored for automatic estimation of crossing angles. Results: Modeling results indicate that cyclist crossing angle is a strong predictor of crossing success, and that cyclist velocity is not. Findings also highlight the prevalence of external factors which limit crossing angles for cyclists. In particular, kerbs are a common factor, along with passing/approaching vehicles or other cyclists. Furthermore, results indicate that further training on a relatively small sample of 100 domain-specific examples can achieve substantial accuracy improvements for cyclist detection (from 0.31AP0.5 to 0.98AP0.5) and crossing angle inference from traffic camera footage. Conclusions: Ensuring safe crossing angles is important for cyclist safety around tram tracks. Infrastructural planners should aim for intuitive, selfexplainable road layouts that allow for and encourage crossing angles of 60 degrees or more - ideally 90 degrees. Practical Applications: The SMoS framework and the open-source SafeCross1 application offer actionable insights and tools for enhancing cyclist safety around tram tracks.

This study analyzed New York Subway incident cases from 2019 to expand on the current understanding of subway train to human collisions. From the 263 incident cases available, 185 (70%) involved train to pedestrian contact. The fatality data were compared with published literature covering 1990 to 2007, showing reasonable agreement in age-, gender-, and borough distributions. The location of incidents was typically the station platform (84%). Four primary behaviors were exhibited by pedestrians before impact with the train. Jumping from the platform was the most common, followed by falling from the platform, walking along the tracks, and standing too close to the edge of the platform. A higher fatality rate was found for collisions that occurred at elevated stations (40%) compared with below-ground stations (27%). The two primary collision types were frontal- and side impact (on the train). The most common impact velocity was 40 to 48 km/h (25 to 30 mph). The most likely outcome of these interactions was a fatality (31%) with only 9% of subway–human interactions resulting in mild injuries. The data suggested that policies based on proactive countermeasures could reduce a significant portion of subway train–human collisions as the majority of preimpact activity occurred on the station platform. Further investigation into the difference in elevated and below-ground collisions may yield useful information, especially relating to the potential protection offered by the drainage trough. When simulating subway–human collisions for countermeasure design, equal consideration should be given to the three impact position types: standing, lying, and jumping.

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