Fault tree analysis (FTA) is a logically structured process that can help identify potential causes of system
failure before the failures actually occur. However, FTA often suffers from a lack of enough probabilistic
basic events to check the consistency of the logic relationship among all events through linkage with
gates. Sometimes, even logic relationship among all events is difficult to determine, and failures in system
operation may have been experienced rarely or not at all. In order to address the limitations, this paper
proposes a novel incident tree methodology that characterizes the information flow in a system instead
of logical relationship, and the amount of information of a fuzzy incident instead of probability of an
event. From probability statistics to fuzzy information quantities of basic incidents and accident, we propose
an incident tree model and incident tree analysis (ITA) method for identification of uncertain, random,
complex, possible and variable characteristic of accident occurrence in quantified risk assessment.
In our research, a much detailed example for demonstrating how to create an incident tree model has
been conducted by an in-depth analysis of traffic accident causation. The case study of vehicle-leaving-
roadway accident with ITA illustrates that the proposed methodology may not only capture the
essential information transformations of accident that occur in system operation, but also determine
the various combinations of hardware faults, software failures and human errors that could result in
the occurrence of specified undesired incident at the system level even accident.