Analysis of factors influencing the degree of accidental injury of bicycle riders considering data heterogeneity and imbalance

• Published in: PloS one 2024-05, Vol.19 (5), p.e0301293-e0301293
• Main Authors: Dong, Xinchi, Zhang, Daowen, Wang, Chaojian, Zhang, Tianshu
• Format: Article
• Language: English
• Subjects: Accident data; Accidental Injuries - epidemiology; Accidental Injuries - etiology; Accidents; Accidents, Traffic - statistics & numerical data; Adolescent; Adult; Algorithms; Bayes Theorem; Bayesian analysis; Bicycles; Bicycling; Bicycling - injuries; Cluster Analysis; Clusters; Computer and Information Sciences; Data mining; Datasets; Driving ability; Earth Sciences; Engineering and Technology; Fatalities; Female; Heterogeneity; Humans; Injuries; Injury analysis; Machine learning; Male; Mathematical models; Medicine and Health Sciences; Middle Aged; Peak periods; Physical Sciences; Research and Analysis Methods; Risk Factors; Sampling; Sampling methods; Sampling techniques; Speed limits; Traffic accidents & safety; Weather; Weather conditions; Young Adult
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0301293

Bicycle safety has emerged as a pressing concern within the vulnerable transportation community. Numerous studies have been conducted to identify the significant factors that contribute to the severity of cyclist injuries, yet the findings have been subject to uncertainty due to unobserved heterogeneity and class imbalance. This research aims to address these issues by developing a model to examine the impact of key factors on cyclist injury severity, accounting for data heterogeneity and imbalance. To incorporate unobserved heterogeneity, a total of 3,895 bicycle accidents were categorized into three homogeneous sub-accident clusters using Latent Class Cluster Analysis (LCA). Additionally, five over-sampling techniques were employed to mitigate the effects of data imbalance in each accident cluster category. Subsequently, Bayesian Network (BN) structure learning algorithms were utilized to construct 32 BN models after pairing the accident data from the four accident cluster types before and after sampling. The optimal BN models for each accident cluster type provided insights into the key factors associated with cyclist injury severity. The results indicate that the key factors influencing serious cyclist injuries vary heterogeneously across different accident clusters. Female cyclists, adverse weather conditions such as rain and snow, and off-peak periods were identified as key factors in several subclasses of accident clusters. Conversely, factors such as the week of the accident, characteristics of the trafficway, the season, drivers failing to yield to the right-of-way, distracted cyclists, and years of driving experience were found to be key factors in only one subcluster of accident clusters. Additionally, factors such as the time of the crash, gender of the cyclist, and weather conditions exhibit varying levels of heterogeneity across different accident clusters, and in some cases, exhibit opposing effects.