Motorcycle Accident Reconstruction

Accident reconstruction utilizes principles of physics and empirical data to analyze the physical, electronic, video, audio, and testimonial evidence from a crash, to determine how and why the crash occurred, how the crash could have been avoided, or to determine whose description of the crash is most accurate. This process draws together aspects of mathematics, physics, engineering, materials science, human factors, and psychology, and combines analytical models with empirical test data. Different types of crashes produce different types of evidence and call for different analysis methods. Still, the basic philosophical approach of the reconstructionist is the same from crash type to crash type, as are the physical principles that are brought to bear on the analysis. This book covers a basic approach to accident reconstruction, including the underlying physical principles that are used, then details how this approach and the principles are applied when reconstructing motorcycle crashes. This second edition of Motorcycle Accident Reconstruction presents a thorough, systematic, and scientific overview of the available methods for reconstructing motorcycle crashes. This new edition contains: Additional theoretical models, examples, case studies, and test data. An updated bibliography incorporating the newest studies in the field. Expanded coverage of the braking capabilities of motorcyclists. Updated, refined, and expanded discussion of the decelerations of motorcycles sliding on the ground. A thoroughly rewritten and expanded discussion of motorcycle impacts with passenger vehicles. Updated coefficients of restitution for collisions between motorcycles and cars. A new and expanded discussion of using passenger car EDR data in motorcycle accident reconstruction. A new section covering recently published research on post-collision frozen speedometer readings on motorcycles. A new section on motorcycle interactions with potholes, roadway deterioration, and debris and expanded coverage of motorcycle falls. This second edition of Motorcycle Accident Reconstruction is a must-have title for accident reconstructionists, forensic engineers, and all interested in understanding why and how motorcycle crashes occur.

Development of a finite element neck model for head-first compressive impacts: Toward the assessment of motorcycle neck protective equipment

Head-first compressive impacts occur in motorcycle crashes and may result in serious to fatal neck injuries to riders. Equipment to protect the riders’ necks from these injuries are available in the market; however, their effectiveness in reducing injury risk is not clear, either due to the lack of scientific evidences or assessment with any prevalently accepted standard. This paper presents a finite element ligamentous neck model, developed as a computationally efficient tool, for future use in the computational phase of assessment process of neck protective equipment. The 3D cervical spine was generated using the mean statistical dimensions of vertebrae and proposed constitutive models, provided in the scientific literature. Ligaments, for the vertebra-vertebra and Hybrid III head–vertebra ligamentous joints, were introduced with the aid of published anatomical descriptions. For validation, the response of the head-neck system under compressive loadings and the flexion-extension bending stiffness of the neck at the segment level were compared against experimental data. The advanced CORrelation and Analysis (CORA) algorithm was applied on the validation responses to assess biofidelity of the model. The results indicate that the model is functional and meets ISO/TR9790 standard as a “good” biofidelic model.

Topic Models from Crash Narrative Reports of Motorcycle Crash Causation Study

The Motorcycle Crash Causation Study (MCCS) is a matched case-control study that contains a very wide list of crash contributing factors associated with motorcycle crash occurrences. It contains information such as motorcycle information, rider information, and associated trip information. This study also provides crash narrative information that presents an in-depth narrative discussion of the crash causation. Because of the plethora of information, it is critical to investigate MCCS-related data. Some studies examined the structured information in MCCS datasets. There is no in-depth study that has examined the unstructured textual contents in the MCCS data. This study aims to mitigate this research gap by applying different natural language processing tools (e.g., text mining, topic modeling). Fatal and non-fatal crash narratives are clustered separately to gain insights pertaining to the injury level. The findings of this study will contribute to the ongoing studies on MCCS to better understand the crash causation mechanism associated with motorcycle crashes.