@article{ref1,
title="A data-driven approach to estimate incident-induced delays using incomplete probe vehicle data: application to safety service patrol program evaluation",
journal="Journal of advanced transportation",
year="2023",
author="Oh, Minsoo and Dong-O'Brien, Jing",
volume="2023",
number="",
pages="e3402853-e3402853",
abstract="This paper presents a data-driven approach to estimate incident-induced delays (IIDs) using probe vehicle data while accounting for missing data. The proposed approach is applied to evaluate the effectiveness of a safety service patrol (SSP) program. Existing data-driven methods for IID estimation usually rely on complete data sets. The proposed approach employs a random forest-based classification model and an interpolation method to estimate IIDs when real-time data are completely or partially missing during the incident-impacted time period. It also identifies reference profiles from the closest spatial-temporal road segments to improve data availability. The case study shows that the SSP program in the Quad Cities area of Iowa reduces IIDs associated with various incidents by 15%-91%. This data-driven evaluation framework can be applied to other traffic incident management programs, allowing more accurate and objective evaluations of their effectiveness.<p /> <p>Language: en</p>",
language="en",
issn="0197-6729",
doi="10.1155/2023/3402853",
url="http://dx.doi.org/10.1155/2023/3402853"
}