Application of the Lomax distribution to estimate the conditional probability of crash

Although crash frequency and severity are convectional and most direct measures of road safety, crash data have limitations that include low quality, lack of reliable insight into the events leading to the crash, and long data collection times. Valid and practical surrogate measures for modeling and estimating safety are needed to allow preventive improvements of safety before human lives and health are lost. To be applicable in transportation safety, surrogate events should be observable and logically connected with crashes. The introduction of new technologies for detecting and tracking road users opened new opportunities for quantifying and measuring road user’s motion leading to more precise measurements of non-crash events. More precise data collection methods might help validate surrogate measures by converting non-crash events into a corresponding crash frequency or even severity. This paper presents the recent development in estimating the probability of crash conditional on traffic conflict. This development increases the traffic method practicality and also provides the theoretical and statistical bridge between conflicts and crashes. A case of study is introduced for road departures studied in a driving simulator to demonstrate the applicability of the method. Application of traffic conflicts at these facilities will provide a proactive approach to safety estimation in driving simulators, naturalistic driving, site observations, and when autonomous vehicles are in the mix of traffic