Spatial instability of crash prediction models: A case of scooter crashes

Abstract

Scooters have gained widespread popularity in recent years due to their accessibility and affordability, but safety concerns persist due to the vulnerability of riders. Researchers are actively investigating the safety implications associated with scooters, given their relatively new status as transportation options. However, analyzing scooter safety presents a unique challenge due to the complexity of determining safe riding environments. This study presents a comprehensive analysis of scooter crash risk within various buffer zones, utilizing the Extreme Gradient Boosting (XGBoost) machine learning algorithm. The core objective was to unravel the multifaceted factors influencing scooter crashes and assess the predictive model’s performance across different buffers or spatial proximity to crash sites. After evaluating the model’s accuracy, sensitivity, and specificity across buffer distances ranging from 5 ft to 250 ft with the scooter crash as a reference point, a discernible trend emerged: as the buffer distance decreases, the model’s sensitivity increases, although at the expense of accuracy and specificity, which exhibit a gradual decline. Notably, at the widest buffer of 250 ft, the model achieved a high accuracy of 97% and specificity of 99%, but with a lower sensitivity of 31%. Contrastingly, at the closest buffer of 5 ft, sensitivity peaked at 95%, albeit with slightly reduced accuracy and specificity. Feature importance analysis highlighted the most significant predictor across all buffer distances, emphasizing the impact of vehicle interactions on scooter crash likelihood. Explainable Artificial Intelligence through SHAP value analysis provided deeper insights into each feature’s contribution to the predictive model, revealing passenger vehicle types of significantly escalated crash risks. Intriguingly, specific vehicular maneuvers, notably stopping in traffic lanes, alongside the absence of Traffic Control Devices (TCDs), were identified as the major contributors to increased crash occurrences. Road conditions, particularly wet and dry, also emerged as substantial risk factors. Furthermore, the study highlights the significance of road design, where elements like junction types and horizontal alignments – specifically 4 and 5-legged intersections and curves – are closely associated with heightened crash risks. These findings articulate a complex and spatially detailed framework of factors impacting scooter crashes, offering vital insights for urban planning and policymaking.

Keywords

• Scooter crashes
• Spatial analysis
• Explainable AI
• SHAP values
• Feature importance
• XGBoost