Identification of Traffic Accident Clusters using Kulldorff's Space-Time Scan Statistics

Identifying traffic accident clusters is vital in helping road users and policymakers make better decisions in managing accident risks. Traffic accidents contain both spatial and temporal dimensions and their interaction should be analyzed to have a better understanding of the nature of the clusters. Similar studies conducted in this area rely on manually sorting data into time buckets before conducting spatial analysis on each of the buckets. While this better than a purely spatial or temporal analysis, the temporal clusters defined by the researcher may not be statistically significant or reveal meaningful space-time interactions. In this paper, we describe the use of Kulldorff's space-time scan statistics to identify traffic accident spatiotemporal clusters. The method identifies clusters by using a scanning cylinder that is varying in size to search for accident cases which are close together in both space and time. The null hypothesis is that the cases are assumed to have constant risk over space and time and follow the Poisson distribution. The Poisson generalized likelihood ratio was determined for each cylinder as a measure of the evidence that it is a hotspot. The clusters were then statistically evaluated using Monte Carlo hypothesis testing. This study was conducted on the 2016 United Kingdom traffic accident dataset and the results show that this method is able to pin point the exact location, size and period of statistically significant clusters.