TY - JOUR
T1 - Outline for a causal model of traffic conflicts and crashes
AU - Davis, Gary A.
AU - Hourdos, John
AU - Xiong, Hui
AU - Chatterjee, Indrajit
PY - 2011/11
Y1 - 2011/11
N2 - Road crashes tend to be infrequent but with nontrivial consequences, leading to a long-running interest in identifying surrogate events, such as traffic conflicts, that can support a timely recognition and correction of safety deficiencies. Although a variety of possible surrogates have been suggested, questions remain regarding how crashes and surrogates are related. Using recent developments in causal analysis we propose a simple model which represents crashes and conflicts as resulting from interactions between initiating conditions and evasive actions, and then use this model to identify relationships between these types of events. Our first set of results expresses the probability of a crash as a mixture of probabilities over different sets of initiating conditions, where the mixing probabilities are governed by the evasive action. Our second set of results considers situations where sampling is restricted to non-crash events, and gives conditions where these truncated probabilities can serve as proxies for crash probabilities. We then illustrate how trajectory-based reconstruction can be used to classify initiating events with respect to severity, and to estimate a proxy for the crash probability from a set of incompletely observed non-crash events.
AB - Road crashes tend to be infrequent but with nontrivial consequences, leading to a long-running interest in identifying surrogate events, such as traffic conflicts, that can support a timely recognition and correction of safety deficiencies. Although a variety of possible surrogates have been suggested, questions remain regarding how crashes and surrogates are related. Using recent developments in causal analysis we propose a simple model which represents crashes and conflicts as resulting from interactions between initiating conditions and evasive actions, and then use this model to identify relationships between these types of events. Our first set of results expresses the probability of a crash as a mixture of probabilities over different sets of initiating conditions, where the mixing probabilities are governed by the evasive action. Our second set of results considers situations where sampling is restricted to non-crash events, and gives conditions where these truncated probabilities can serve as proxies for crash probabilities. We then illustrate how trajectory-based reconstruction can be used to classify initiating events with respect to severity, and to estimate a proxy for the crash probability from a set of incompletely observed non-crash events.
KW - Causal models
KW - Surrogate measures
KW - Traffic conflicts
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U2 - 10.1016/j.aap.2011.05.001
DO - 10.1016/j.aap.2011.05.001
M3 - Article
C2 - 21819818
AN - SCOPUS:79961167293
SN - 0001-4575
VL - 43
SP - 1907
EP - 1919
JO - Accident Analysis and Prevention
JF - Accident Analysis and Prevention
IS - 6
ER -