Analysis of Factors Affecting Emergency Vehicle Dispatches

From August 2016 to January 2017, in collaboration with other agencies, Daejeon Metropolitan City employed diverse algorithms to thoroughly analyze the various factors that could affect the dispatches of fire engines and ambulances

It analyzed 30 million cases of GPS data of ambulances to generate location coordinates at 10-second intervals, and analyzed the pager numbers of 210 vehicles dispatched to various disasters, the administrative jurisdictions of fire stations, the 119 Safety Centers under the fire stations, and the vehicle type data

It also analyzed the intelligent transportation system (ITS, nodelink.its.go.kr), in order to arrive at more accurate and reliable results

The analysis was carried out based on 110,000 cases of road location data as well as 9,000 cases of road characteristics data, categorized in terms of roadway names, road IDs, road numbers, lane information, etc.

Analysis of Vehicle Routing for Optimization of Emergency Dispatches

Machine learning-based analysis of routing data identified seven vulnerable areas and 800 chronically congested spots which emergency vehicles could not reach within 5 minutes

The analysis identified the optimal routes for avoiding congested sections and dispatching emergency vehicles to promptly arrive at the vulnerable areas

The results of analysis showed that using the most expeditious routes between the centers and the destinations is more efficient than focusing on the shortest direct distances between them (Even if its total distance between the two points is longer than the shortest direct distance, the more efficient route, which avoids obstacles such as traffic lights or left-turn signals, should be used)

Drawing on the data, an emergency service team can be more promptly dispatched from a center from which it can arrive at the site most rapidly

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[Source] Ministry of the Interior and Safety