Joint analysis of two nationwide crash databases to model transit bus stop-related pedestrian crash severity in the United States.

The Fatality Analysis Reporting System (FARS) and Crash Report Sampling System (CRSS) report a unique "transit bus stop-related" pedestrian crash type. This study merged fatal and non-fatal transit bus stop-related pedestrian crashes from FARS and CRSS (2016-2023) to examine injury severity and identify risk factors for pedestrians at bus stops. To address imbalance in severity (KA = 183, BCO = 86), synthetic minority oversampling for nominal and continuous features (SMOTE-NC) was applied. Then, a severity analysis using elastic net penalized logistic regression was conducted on the original and SMOTE-NC sample with 17 predictors, as regularization handles high-dimensional data and multicollinearity better than traditional parametric approaches. The SMOTE-NC model showed severe transit bus stop-related pedestrian crashes were associated with poor lighting, midblock locations, and higher-speed, wider roadways. Results showed amplified effects for predictors in the SMOTE-NC models, including several that were zero in the original model but were selected after oversampling, suggesting oversampling potentially mitigated bias due to class imbalance. The results provided a foundation for identifying tangible solutions to address dangerous bus stops. Improving street lighting or installing bus stop lighting may be effective, as dark, unlighted conditions were associated with higher odds ratios for KA injuries (OR = 3.18) than dark, lighted conditions (OR = 1.69), relative to daylight. Evaluating pedestrian behavior near midblock bus stops, like unmarked crossings, can further mitigate risks exacerbated by darkness and high-speed multilane roads. These findings support data-driven policy and design decisions by transit agencies and transportation planners to address higher-risk bus stops.