  A speed threshold is a crucial parameter in breakdown and capacity
distribution analysis as it defines the boundary between free-flow and
congested regimes. However, literature on approaches to establishing the
breakpoint value for detecting breakdown events is limited. Most of existing
studies rely on the use of either visual observation or predefined thresholds.
These approaches may not be reliable considering the variations associated with
field data. Thus, this study compared the performance of two data-driven
methods, that is, logistic function (LGF) and two-regime models, used to
establish the breakpoint from traffic flow variables. The two models were
calibrated using urban freeway traffic data. The models'performance results
revealed that with less computation efforts, the LGF has slightly better
prediction accuracy than the two-regime model. Although the two-regime model
had relatively lower performance, it can be useful in identifying the
transitional state.
