Network screening techniques are widely used by state agencies to identify locations with high collision concentration, also referred to as hotspots. However, most of the research in this regard has focused on identifying highway segments that are of concern to automobile collisions. A major difference between pedestrian and automobile hotspots is that pedestrian-based conflicts are more likely to arise in localized regions, such as near intersections, mid-blocks, and/or other crossings, as opposed to along long stretches of roadway. Hence, in order to address this issue, a dynamic programming-based hotspot identification approach is proposed which provides efficient hotspot definitions for pedestrian crashes. The proposed approach is compared with the sliding window method and the results reveal that the dynamic programming method generates more hotspots with a higher number of crashes, while covering fewer miles.
