Sample-Based Estimation of Vehicle Speeds from Yaw Marks: Bayesian Implementation Using Markov Chain Monte Carlo Simulation

The critical speed method uses measurements of the radii of yawmarks left by vehicles, together with values for centripetal acceleration, to estimate the speeds of the vehicles when the yawmarks were made. Several field studies have indicated that equating the centripetal force with braking friction produced biased estimates, but that the biases tended to be small (e.g. within 10%-15% on average) and led to underestimates, suggesting that the method can be useful for forensic purposes. Other studies, however, have challenged this conclusion. The critical speed method has also seen use in safety-related research, where it is important to have a reliable assessment of the uncertainty associated with a speed estimate. This paper describes a variant of the critical speed method, where data from field tests lead to an informative prior probability distribution for the centripetal acceleration. Using Bayes theorem, this distribution is combined with the measured radius to produce a posterior probability distribution for the desired speed. The required computations are readily carried out using Markov Chain Monte Carlo simulation. Calibration/Cross-validation tests, conducted using published data sets, in most cases found no significant differences between the actual and the nominal coverages of confidence intervals. For example, the 90% confidence intervals computed from the measured yaw radii tended to catch approximately 90% of the measured vehicle speeds.