Given that reliable prediction of such relatively rare-and random-events as accident occurrence will remain elusive, the most important potentially soluble factor in the development of accident management strategies is to identify and quantify the conditions affecting the nonrecurrent congestion caused by accidents once they have been known to have occurred. The objective of the research reported in this paper is to develop a method of quantifying the delay due to accidents on urban freeways, as well as to identify the causal factors affecting the total delay caused by such accidents. Binary integer programming (BIP) is applied in estimating the temporal and spatial extent of delay caused by freeway accidents, based solely on commonly available inductance loop detector data. The basic idea behind the method is to estimate the most likely temporal and spatial extent of the region of congestion caused by an accident by solving a BIP problem that is consistent with the topology of the spatio-temporal region that defines candidate speed differences between normal flow conditions and accident conditions. The procedures developed in this paper will be useful for the performance evaluation of accident management systems by quantifying accident congestion in terms of the total delay to evaluate the benefit of accident management systems accrued from efficient traffic operations. The procedures are demonstrated by a case study using accident data collected from six major freeways in Orange County, CA.