Spatiotemporal Analysis and Predictive Modeling of Traffic Accidents in Boston: Insights for Advancing Vision Zero Initiatives

This study examined the spatial, temporal, and predictive dimensions of traffic crashes in Somerville, Massachusetts, to contribute to the evidence base for Vision Zero initiatives. Using crash data, spatial autocorrelation analysis, temporal distribution assessments, and machine learning models, the research identified patterns and predictors of crash severity. Spatial analysis revealed significant clustering of crashes, particularly around intersections and arterial corridors, underscoring the structural vulnerabilities of the urban road network. Temporal analysis demonstrated that crashes peaked during midday and afternoon hours, with seasonal spikes in January, September, and December, reflecting the combined influence of commuting cycles, weather conditions, and increased travel activity. Predictive modeling using logistic regression, random forest, and gradient boosting highlighted the challenges of forecasting severe crashes in imbalanced datasets. While gradient boosting achieved high accuracy (0.885) and precision (0.959), its ROC‑AUC score of 0.50 indicated poor discriminatory power, revealing a bias toward the majority class. The confusion matrix further confirmed that severe crashes were frequently misclassified as non‑severe, limiting the model’s utility for proactive safety interventions. Nonetheless, feature importance analysis identified time of day, intersection type, and pedestrian involvement as key predictors of crash severity, reinforcing the systemic interplay of temporal, infrastructural, and behavioural factors. The implications extend to Vision Zero frameworks, suggesting that traffic safety outcomes are embedded within discernible patterns that can be studied and anticipated. This study underscores the value of spatiotemporal and predictive analyses in advancing the understanding of urban traffic safety and informing long‑term strategies to eliminate severe crashes.