Priority decision of road repair zone using the drive data of ambulances

Abstract

A rough road surface produces strong or continuous vibration in an ambulance, which causes feeling of discomfort, severe pain or sometimes deterioration of physical condition to injured or ill persons. With the aim of reducing such harmful vibration for safe and reliable medical emergency transportation services, this paper proposes a method to find out rough road surfaces from drive data of ambulances and to give them the priority for future pavement repair. There are four steps in the prioritization process: The first step is to collect drive data including information on location, speed and vibration with a smartphone. According to ISO2631-1, the vibration is quantified as a running r.m.s of the frequency-weighted acceleration. In the second step, meaningless data such as small vibration and extremely slow speed data are removed. Then, depending on the location and the traveling direction of the ambulance when the data were recorded, the data form clusters, which are candidates of road repair zones. Finally, each cluster is prioritized by the evaluation function. The larger amount of strong-vibration and slow-speed data are included in the cluster, the higher priority is given to it. An example of application to three ambulances is presented. The high priority clusters were well matched to the road surfaces that the ambulance crews were aware of as strong vibration zones. It is also shown that at least one and a half months are required in collection of the drive data to decide on the top ten clusters.

Keywords

• big data analysis
• drive data
• ambulance
• road repair
• vibration ride comfort