Enriching GPS data for expanding interpretation of emergency vehicles using a pathfinding algorithm and spatial data harvesting methods

Abstract

Special-purpose vehicles, such as fire trucks, have limitations on collecting data due to their small numbers and different driving patterns compared to normal vehicles. Although extensive research has been investigated on emergency vehicles, limited data has made it difficult to support their missions. In this respect, this research capitalizes on the emerging availability of data from technologies in our cities and society with the interconnectedness. The main objective of this research is to enhance route preemption and optimization of emergency vehicle trips through automated data restoring and harvesting approaches with the existing data. This research restores vehicle paths of emergency vehicles with a pathfinding algorithm and harvests spatial data along with the restored vehicle paths. Dijkstra's algorithm and the proposed data harvesting methods are used to bring the enriched data to analyze the emergency vehicle routes. Based on the enriched data, two applications are developed to identify which factors have significant impacts on emergency response time and which intersections and specific driving routes at these intersections have more impact on emergency response time. This research will contribute to the body of knowledge by expanding interpretation of emergency vehicles and providing further information for the emergency response time.