Connecting Surrogate Safety Measures to Crash Probablity via Causal Probabilistic Time Series Prediction
This work addresses the validation of surrogate safety measures for traffic safety analysis, which is incremental as it applies a novel method to a known bottleneck in connecting near-miss data to crash outcomes.
The paper tackled the problem of validating surrogate safety measures by connecting them to crash probability, using probabilistic time series prediction with transformer-MAF to estimate probability density functions from speed, acceleration, and time-to-collision data, resulting in accurate predictions and reasonable probability estimates in traffic conflict and normal interaction contexts.
Surrogate safety measures can provide fast and pro-active safety analysis and give insights on the pre-crash process and crash failure mechanism by studying near misses. However, validating surrogate safety measures by connecting them to crashes is still an open question. This paper proposed a method to connect surrogate safety measures to crash probability using probabilistic time series prediction. The method used sequences of speed, acceleration and time-to-collision to estimate the probability density functions of those variables with transformer masked autoregressive flow (transformer-MAF). The autoregressive structure mimicked the causal relationship between condition, action and crash outcome and the probability density functions are used to calculate the conditional action probability, crash probability and conditional crash probability. The predicted sequence is accurate and the estimated probability is reasonable under both traffic conflict context and normal interaction context and the conditional crash probability shows the effectiveness of evasive action to avoid crashes in a counterfactual experiment.