Understanding the determinants of crash injury severity is essential for developing effective safety strategies and reducing traffic-related losses. This study proposes a hybrid analytical framework that integrates interpretable machine learning with statistical modeling to address the limitations of existing approaches. A Random Forest (RF) classifier, combined with Shapley Additive Explanations (SHAP), was first employed to capture nonlinear relationships and identify key predictors of injury outcomes, including safety equipment, age, gender, and the presence of fixed obstacles. Random Forest was chosen for its strong predictive performance in capturing nonlinear relationships, while SHAP provides transparent explanations of model predictions. To ensure statistical rigor and quantify associations, a Partial Proportional Odds (PPO) model was subsequently applied, allowing for the relaxation of the proportional odds assumption (POA) and enabling the estimation of marginal effects. The results consistently highlight the protective role of safety equipment and the increased risks associated with fixed obstacles, adverse weather, and nighttime conditions. For instance, seatbelt use is associated with a 29.61% higher probability of no injury, whereas fixed obstacles are associated with a 29.36% lower probability and a higher risk of severe injury. These findings support safety campaigns that encourage protective equipment use and infrastructure policies aimed at reducing roadside obstacles and improving nighttime visibility. Future research will focus on accounting unobserved heterogeneity and validating the framework across multi-regional datasets to improve its generalizability and policy relevance.

In the event of a road accident, a quick intervention is crucial. The mobile emergency services take care of patients whose condition requires an emergency repatriation to a hospital, by land in an ambulance or by air in a helicopter. The main criteria for choosing the means of transport are the time required for repatriation and the patient’s more or less critical state of health. Do the vehicle dynamic effects endured by the transported patient have an influence on their health condition? Vehicle dynamics data were recorded with a road data recorder for a period of 3 months, under real conditions of patient repatriation to a hospital; 39 trips were recorded by ambulance and 29 trips by helicopter. Significant differences in speed (average 42 versus 202 km/h) and distance travelled (average 23 versus 85 km) were observed. The sustained effects are similar in helicopters and ambulances. The ambulance causes more abrupt variations in longitudinal and transversal directions, whereas the helicopter has more variations in vertical direction. The vibration level in helicopters is higher than in ambulances. These results can be considered as a first reference baseline for establishing a characterization of transported patients’ exposure to vehicle dynamics.

Powered 2-wheeler (PTW) safety remains a critical concern, prompting the investigation into the efficacy of advanced rider assistance systems (ARAS) in reducing crash rates. The objective of this study is to provide a comprehensive quantification of the effects of 2 such systems, Adaptive Cruise Control (ACC) and Autonomous Emergency Braking (AEB), on the avoidance or mitigation of motorcycle crashes. Using real-world motorcycle crash data, this research employed kinematic reconstruction techniques and numerical simulations to evaluate the impact of ACC and AEB on crash reduction. A parametric study was conducted, exploring various parameters including trigger distances, deceleration rates, and field of view, to assess their influence on the effectiveness of this systems in crashes involving PTWs. The analysis revealed significant findings regarding the effectiveness of ACC and AEB in reducing crash rates involving motorcycles. Notably, ACC, especially in dynamic mode with a trigger distance of 40 m, demonstrated the ability to prevent 53% of crashes, resulting in impact speed reductions ranging from 4 to 25 km/h. Similarly, AEB exhibited remarkable effectiveness, reducing impact speeds by 2.5 to 38.9 km/h, with avoidance rates ranging from 7% to 63%. Optimization of key parameters such as triggering strategy, deceleration, sensor range, and field of view is crucial for maximizing the effectiveness of ARAS in motorcycle safety applications. These findings provide valuable insights for manufacturers and policymakers, highlighting the need for tailored approaches to system design and implementation. By refining these systems based on empirical data and analysis, significant improvements can be made in motorcycle safety, ultimately saving lives and reducing the severity of crashes on roadways.

This study investigates everyday cycling behavior among conventional bike and electric bike users in Lyon, France, using a naturalistic methodology. Over a two-month period, participants’ daily trips were recorded using dedicated smartphones, capturing data on trajectories, speeds, accelerations, and rotational velocities, as well as video footage of the forward scene. A dedicated data acquisition app enabled continuous monitoring. Emergency maneuvers such as hard braking, hard cornering, and strong jolts caused by road defects or poor infrastructure were detected using criteria established after controlled tests on track, with specific filters and threshold values applied to the collected data. The analysis focused on usage patterns, speeds, and mapping locations where emergency maneuvers and jolts were frequent, highlighting potentially hazardous spots related to road layout or design. By objectively capturing real-world cycling dynamics, the study provides detailed insights into how cyclists interact with Lyon’s urban environment and infrastructure. The approach allows for the identification of areas where targeted improvements could enhance safety and comfort. Future work will apply this methodology in Paris to compare cycling behaviors across different French urban contexts.