Electric personal mobility device driver behaviors, their antecedents and consequences: A narrative review

A hybrid Machine learning and statistical modeling approach for analyzing the crash severity of mobility scooter users considering temporal instability

One of the main objectives in improving the quality of life for individuals with disabilities, especially those experiencing mobility issues such as the elderly, is to enhance their day-to-day mobility. Enabling easy mobility contributes to their independence and access to better healthcare, leading to improvements in both physical and mental well-being. Mobility Scooters have become increasingly popular in recent years as a means of facilitating mobility, yet traffic safety issues such as crash severity have not been adequately investigated in the literature. This study addresses this knowledge gap by employing a hybrid method that combines a machine learning approach using the eXtreme Gradient Boosting (XGBoost) algorithm with Shapley Additive exPlanations (SHAP) and an advanced statistical model called Random Parameters Binary Logit accounting for heterogeneity in means and variances. Analyzing the United Kingdom mobility scooter crash data from 2018 to 2022, the study examined temporal instability using a likelihood ratio test. The results revealed that there was instability over the three distinct periods of time based on the coronavirus (COVID) pandemic, namely, pre-COVID, during COVID, and post-COVID. Moreover, the results revealed that mobility scooter crashes occurring at a give-way or uncontrolled junctions has a random effect on the severity, while factors such as mobility scooter riders aged over 80, rear-end and sideswipe crashes, and crashes during winter months increase the risk of severe injuries. Conversely, mobility scooter riders involved in crashes while riding on the footway are less likely to experience severe injuries. These findings offer valuable insights for enhancing road safety measures that can be utilized to effectively reduce the crash severity of mobility scooter riders.

Critical risk factors associated with fatal/severe crash outcomes in personal mobility device rider at-fault crashes: A two-step inter-cluster rule mining technique

Personal Mobility Devices (PMDs) have witnessed an extraordinary surge in popularity, emerging as a favored mode of urban transportation. This has sparked significant safety concerns, paralleled by a stark increase in PMD-involved crashes. Research indicates that PMD user behavior, especially in urban areas, is crucial in these crashes, underscoring the need for an extensive investigation into key factors, particularly those causing fatal/severe outcomes. Remarkably, there exists a noticeable gap in the research concerning the analysis of determinants behind fatal/severe PMD crashes, specifically in PMD rider-at-fault collisions. This study addresses this gap by identifying uniform groups of PMD rider-at-fault crashes and investigating cluster-specific key factor associations and determinants of fatal/severe crash outcomes using Seoul's PMD rider-at-fault crash data from 2017 to 2021. A comprehensive two-step framework, integrating Cluster Correspondence Analysis (CCA) and Association Rules Mining (ARM) techniques is employed to segment PMD rider-at-fault crash data into homogeneous groups, revealing unique risk factor patterns within each cluster and further exploring the combination of factors associated with fatal/severe PMD rider-at-fault crash outcomes. CCA revealed three distinct groups: PMD-vehicle, PMD-pedestrian, and single-PMD crashes. From the ARM, it was found that fatal/severe crashes were linked to dry road conditions, male PMD users, and weekdays, irrespective of the cluster. Whereas speeding violations and side collisions were associated with fatal/severe PMD-vehicle rider-at-fault crashes, traffic control violations were related to fatal/severe PMD-pedestrian rider-at-fault crashes at pedestrian crossings. Unsafe riding practices predominantly caused single-PMD crashes during daytime hours. From the findings, engineering improvements, awareness campaigns, education, and law enforcement actions are recommended. The new insights gleaned from this research provide a foundation for informed decision-making and the implementation of policies designed to enhance PMD safety.

'I mean, I wouldn't say I was sober': Exploring the psychosocial impact of e-scooter injuries and aligning a collaborative public health response

ISSUE ADDRESSED

E-scooters are sought after for their cost-effectiveness, sustainability, and efficiency in urban transportation. However, this popularity has been accompanied by a surge in injuries, prompting a deeper investigation into the factors influencing risk perceptions among e-scooter users.

METHODS

Using a qualitative approach, we conducted 19 interviews with those who attended an emergency department as a result of e-scooter injury. We aimed to understand the psychosocial effects of these incidents and employed thematic analysis to discern recurrent patterns in participants' experiences, focusing on alterations in daily life, community response, perception shifts, and avenues to enhance safety awareness.

RESULTS

The findings underscored significant disruptions to daily life due to injuries, demonstrating enduring impacts on lifestyle and wellbeing. Participants exhibited a perceptual shift, transitioning from perceiving e-scooters as enjoyable to viewing them as dangerous. Recommendations for enhancing safety awareness included accessible and clear safety education, pre-ride briefings, real-time safety guidance, temporal rental restrictions, and mandatory breathalysers before e-scooter use.

CONCLUSIONS

The study underscores the importance of considering not only the individual experiences and perceptions of e-scooter injuries but also the broader social context, including the night-time economy. Leveraging peer narratives and community engagement is vital to reshape risk perceptions and promote harm reduction messages. SO WHAT?: A comprehensive approach through proactive interventions and robust educational strategies is essential to foster responsible e-scooter usage and prioritise public safety.