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InTrans

Institute for Transportation

InTrans|Research|In-Progress

Crash Prediction Methods for Long-Duration Work Zones

Project Details
STATUS

In-Progress

PROJECT NUMBER

NCHRP 17-137

START DATE

05/01/26

END DATE

04/30/29

FOCUS AREAS

Safety

RESEARCH CENTERS InTrans, CTRE
SPONSORS

National Cooperative Highway Research Program (NCHRP)

Researchers
Principal Investigator
Shauna Hallmark

Director, InTrans

Co-Principal Investigator
Jonathan Wood

Faculty Affiliate, CTRE

Co-Principal Investigator
Skylar Knickerbocker

Research Engineer, CTRE

Co-Principal Investigator
Guillermo Basulto-Elias

Research Scientist, CTRE

About the research

Mitigating work zone crashes relies in part on the ability to predict crashes based on roadway and work zone characteristics and then identify strategies to address them. The American Association of State Highway and Transportation Officials (AASHTO) Highway Safety Manual (HSM) provides quantitative methods to predict crash frequencies on various types of roadways. While the HSM is the recognized source of information and methodologies to
quantitively evaluate safety performance, minimal guidance is provided for the prediction of crashes in work zones. And what information is available is limited to crash modification factors (CMFs) based solely on work zone duration and length. Few other tools exist to predict crashes in work zones. Consequently, state departments of transportation (DOTs) and other agencies rely on judgment and experience rather than quantitative safety analyses to develop temporary traffic control (TTC) plans and address safety for long-duration work zones.

In order to better predict the safety performance for different work zone configurations, the goal for research under NCHRP 17-137 is the development of crash prediction methods to assess the likely crash impacts for long-duration work zones on high-speed (45 mph or higher) multilane roadway facilities. To accomplish this, the following objectives are expected:

  • Identify feasible data sources, including crash, work zone plans, exposure, or other public data that can be used to develop robust crash prediction models
  • Develop a detailed methodological framework for developing crash prediction models based on work zone TTC plans, countermeasures, and expected traffic characteristics
  • Develop a user-friendly, updatable, and easy-to-maintain spreadsheet tool and manual
  • Prepare draft language to be considered by AASHTO for inclusion in next HSM update
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