Gender disparities in rural motorcycle accidents: A neural network analysis of travel behavior impact.

Abstract

Rural road accidents involving motorcycle riders present a formidable challenge to road safety globally. This study offers a comprehensive gender-based comparative analysis of rural road accidents among motorcycle riders, aimed at illuminating factors contributing to accidents and discerning potential gender disparities in accident rates and severity. Employing a sophisticated Neural Network approach, the research delves into the intricate relationship between various variables and accident outcomes, with a specific emphasis on identifying gender-specific patterns. For female riders, the ANN model demonstrates impressive overall accuracy (CA) of 92 %, indicating its capability to correctly classify accident outcomes. Precision, which measures the model's ability to avoid false positives, stands at a commendable 90.8 %. Moreover, the model exhibits high recall (92 %) and F1 score (88.4 %), indicating its effectiveness in identifying both fatal and non-fatal accidents among female riders. Additionally, the Matthews Correlation Coefficient (MCC) of 0.132 suggests a moderate level of agreement between the predicted and actual outcomes. Upon further examination, it is evident that the model performs exceptionally well in predicting non-fatal accidents for female riders, achieving a precision, recall, and F1 score of 92 %, 99.9 %, and 95.8 %, respectively. However, its performance in predicting fatalities is relatively lower, with a precision of 75.6 % and recall of 2.6 %, resulting in a lower F1 score of 5.0 %. Despite this disparity, the MCC remains consistent at 0.132, indicating a balanced performance across both classes. The findings reveal valuable insights for policymakers and road safety practitioners, providing avenues for the development of targeted interventions and the enhancement of safety measures for motorcycle riders on rural roads. By addressing the gap in understanding gender-related differences in travel habits and accident risks, this research contributes to ongoing efforts to mitigate the impact of road accidents and promote safer travel environments for all road users.