A Data-Driven Decision-Making Support Method for Priority Determination for an Intelligent Road Problem Reporting System

This paper presents a new decision support method aimed at prioritizing processing for an intelligent road problem reporting service. The proposed method uses advanced georeferencing technology to extract the longitude and latitude coordinates in the metadata of photos taken with the smartphone application to capture the complaint scene. This method not only maps out the processing times, but also applies a spatiotemporal clustering technique to link the complaint types and locations with the actual complaint processing times. A validation study of the frequency of reported locations per priority reveals that the complaint-processing prioritization method developed in this study aligns realistically with actual field complaint processing. Furthermore, recognizing the significance of location in processing complaints, the georeferencing technique appears suitable for identifying complaint locations for each report and incorporating this into the decision-making framework.

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Publication Details

Journal
Applied Sciences
Published
2024-11-23
DOI
https://doi.org/10.3390/app142310861
Citations
1
Primary Topic
Data-Driven Disease Surveillance
Type
article
Field-Weighted Citation Impact
0.39
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article

A Data-Driven Decision-Making Support Method for Priority Determination for an Intelligent Road Problem Reporting System

Jin Guk Kim, Joyoung Lee, Woohoon Jeon
1 citations
Applied Sciences
Data-Driven Disease Surveillance
0.39
article

A Data-Driven Decision-Making Support Method for Priority Determination for an Intelligent Road Problem Reporting System

Jin Guk Kim, Joyoung Lee, Woohoon Jeon
article en
1 citations

Abstract

This paper presents a new decision support method aimed at prioritizing processing for an intelligent road problem reporting service. The proposed method uses advanced georeferencing technology to extract the longitude and latitude coordinates in the metadata of photos taken with the smartphone application to capture the complaint scene. This method not only maps out the processing times, but also applies a spatiotemporal clustering technique to link the complaint types and locations with the actual complaint processing times. A validation study of the frequency of reported locations per priority reveals that the complaint-processing prioritization method developed in this study aligns realistically with actual field complaint processing. Furthermore, recognizing the significance of location in processing complaints, the georeferencing technique appears suitable for identifying complaint locations for each report and incorporating this into the decision-making framework.

Applied SciencesVol. 14(23)
New Jersey Institute of Technology (US), Korea Institute of Civil Engineering and Building Technology (KR)
Peace, Justice and strong institutions
Openalex Percentile: Top 32%
Data-Driven Disease Surveillance
0.39
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A Data-Driven Decision-Making Support Method for Priority Determination for an Intelligent Road Problem Reporting System — Jin Guk Kim, Joyoung Lee, et al. · Applied Sciences (2024) | TGRS Research Map | TGRS