Citation

Oh M, Dong-O'Brien J. J. Adv. Transp. 2023; 2023: e3402853.

Copyright

(Copyright © 2023, Institute for Transportation, Publisher John Wiley and Sons)

DOI

10.1155/2023/3402853

PMID

unavailable

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.

Language: en