Estimating Baseline Numbers for Safety Measure Target Setting in Virginia. Issue 8 (August 2020)

• Record Type: Journal Article
• Authors: Himes, Scott; Gayah, Vikash; Gooch, Jeff; Read, Stephen
• Is Part Of: Transportation research record. Volume 2674:Issue 8(2020)
• Page Start: 523
• Page End: 535
• Publication Date: 2020-08
• Subjects: Transportation -- Periodicals; Roads; Transport -- Périodiques; Routes -- Périodiques; Routes -- Conception et construction -- Périodiques; Roads; Transportation; 388.05
• DOI: 10.1177/0361198120920632
• Languages: English
• ISSNs: 0361-1981

Abstract

The Federal Highway Administration (FHWA) established the Safety Performance Management program (Safety PM) to support the Highway Safety Improvement Program. The Safety PM Final Rule requires state departments of transportation (DOTs) to establish and report safety targets annually. FHWA does not identify a specific methodology to use when establishing safety targets. Many state DOTs apply annual growth/decline factors to previous-year safety measures. However, state DOTs also have flexibility to use a data-driven process. The Virginia Department of Transportation (VDOT) recently pursued the development of a more robust data-driven safety target setting methodology. This paper presents a methodology for establishing safety target baselines for several measures, including (1) fatalities, (2) serious injuries, and (3) nonmotorized fatalities and serious injuries. Predictive models were developed for establishing a baseline for 2019 targets and were further refined for 2020 targets. The predictive models include macro-level inputs and were developed for monthly, VDOT district-level outcomes. Performance measure data from 2018 were withheld from models for validation purposes and 2018–2020 model inputs were forecasted based on recent data. As 2019 data become available, the models should incorporate newer data and new models should be developed for revised 2020 and beyond predictions, as necessary. Refined models should include additional data elements as predictors, include more years of data to increase sample size, and capture moments when unobserved annual factors (i.e., unobserved underlying macro-level trends) begin to change.