Utilizing black-box visualization tools to interpret non-parametric real-time risk assessment models

Abstract

This study bridges the gap between Real-Time Risk Assessment (RTRA) and its practical implications by following the post-hoc interpretability approach and utilizing black-box graphical tools for safety data visualization. The real-time traffic-related crash contributing factors were detected using the matched-case control design on 402-miles Interstate 80 in Wyoming. Four black-box visualization tools, including Partial Dependence Plot (PDP), Individual Conditional Expectation (ICE), centered ICE, and Accumulated Local Effect (ALE), were scrutinized to interpret the causal effect of these factors on crash probabilities. The results revealed that these techniques have many advantages, disadvantages, and unanswered questions that must be recognized by Active Traffic Management. PDPs must be accompanied by ICEs that explain the heterogeneity across observations. ALE is the most reliable technique in one-dimensional plots for highly correlated space of variables. However, there is a substantial distinction between PDP and ALE in two-dimensional plots that may make ALE an unreliable method.

KEYWORDS:

• Real-Time Risk Assessment
• Safety Data Visualization
• Partial Dependence Plot
• Individual Conditional Expectation
• Accumulated Local Effect

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research is sponsored by the U.S. Department of Transportation Connected Vehicle Pilot Deployment Program (grant number DTFH6116RA00007), and the Wyoming Department of Transportation (grant number RS04218).