Getis–Ord Spatial Statistics to Identify Hot Spots by Using Incident Management Data

Traditionally, data have been collected to measure and improve the performance of incident management (IM). While these data are less detailed than crash records, they are timelier and contain useful attributes typically not reported in the crash database. This paper proposes the use of Getis–Ord (G_i^*) spatial statistics to identify hot spots on freeways from an IM database while selected impact attributes are incorporated into the analysis. The G_i^* spatial statistics jointly evaluate the spatial dependency effect of the frequency and attribute values within the framework of the conceptualized spatial relationship. The application of the method was demonstrated through a case study by using the incident database from the Houston, Texas, Transportation Management Center (TranStar). The method successfully identified the clusters of high-impact accidents from more than 30,000 accident records from 2006 to 2008. The accident duration was used as a proxy measure of its impact. The proposed method could be modified, however, to identify the locations with high-valued impacts by using any other attributes, provided that they were either continuous or categorical in nature and could provide meaningful implications. With improved intelligent transportation system infrastructure and communication technology, hot spot analyses performed with IM data of freeway network and arterials in the vicinity have become a much more promising alternative. Freeway management agencies can use the results of hot spot analysis to provide visualized information to aid the decision-making process in the design, evaluation, and management of IM strategies and resources. The limitations of the method and possible future research are discussed in the closing section of the paper.