Identifying Road Speeds and Analyzing Trajectories: GPS-Based Response Time Insights for Electrical Grid Metrics

Calculating travel time to outages from different starting locations is essential for evaluating impacts of staffing levels on a utility’s grid outage metrics. Many roads traveled by utility responders are private and/or do not have speed limits, reducing accuracy when determining routes and travel time to outages. This paper demonstrates methods for utilizing OpenStreetMaps (OSM) [1] road data with historical GPS information to analyze changes to the Customer Average Interruption Duration Index (CAIDI) when initial responses to outages come from a different location. By analyzing increases and decreases in first responder response time, we can understand the impacts of handling outages from different shops on CAIDI. The following analysis establishes practices for utilities to develop a model for estimating travel time from different locations and determining impacts on CAIDI through a case study in Northeast Wyoming.

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Publication Details

Published
2025-04-29
DOI
https://doi.org/10.1109/repc60353.2025.00015
Primary Topic
Traffic Prediction and Management Techniques
Type
article
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article

Identifying Road Speeds and Analyzing Trajectories: GPS-Based Response Time Insights for Electrical Grid Metrics

W. M. Mueller
Traffic Prediction and Management Techniques
article

Identifying Road Speeds and Analyzing Trajectories: GPS-Based Response Time Insights for Electrical Grid Metrics

W. M. Mueller
article en

Abstract

Calculating travel time to outages from different starting locations is essential for evaluating impacts of staffing levels on a utility’s grid outage metrics. Many roads traveled by utility responders are private and/or do not have speed limits, reducing accuracy when determining routes and travel time to outages. This paper demonstrates methods for utilizing OpenStreetMaps (OSM) [1] road data with historical GPS information to analyze changes to the Customer Average Interruption Duration Index (CAIDI) when initial responses to outages come from a different location. By analyzing increases and decreases in first responder response time, we can understand the impacts of handling outages from different shops on CAIDI. The following analysis establishes practices for utilities to develop a model for estimating travel time from different locations and determining impacts on CAIDI through a case study in Northeast Wyoming.

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Traffic Prediction and Management Techniques
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