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Publication Number:  FHWA-HRT-20-010    Date:  December 2019
Publication Number: FHWA-HRT-20-010
Date: December 2019


Analysis of SHRP2 Data to Understand Normal and Abnormal Driving Behavior in Work Zones

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In 2016, the Federal Highway Administration posted a Broad Agency Announcement (BAA) to conduct research on potential safety improvements using the Naturalistic Driving Study (NDS) and Roadway Information Database (RID) collected during the research phase of the Second Strategic Highway Research Program. Phase 1 served as a "proof of concept" to determine if meaningful conclusions or countermeasures can be developed using NDS and RID databases. Phase 2 enabled researchers to conduct more indepth analyses, leading to specific highway safety improvements.

The following final report describes the methodology and results of one of six BAA projects to characterize normal and abnormal driving behavior in work zones. In this study, the researchers successfully used the NDS and RID databases to quantify the role of traffic management, work zone activities, and traffic conditions on driver behavior such as speed, merging, and so forth. The results suggest that "nudging" drivers to comply with work zone speed limits and safe following distances would be effective at reducing the number of safety-critical events. This report will be of interest to State and local departments of transportation professionals that are responsible for managing work zones, setting guidelines and policies to implement in work zones, and developing applications and communication protocols for autonomous vehicles.

Brian P. Cronin, P.E.
Director, Office of Safety and Operations
Research and Development


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The U.S. Government does not endorse products or manufacturers. Trademarks or manufacturers’ names appear in this report only because they are considered essential to the objective of the document.

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Technical Report Documentation Page
1. Report No.
2. Government Accession No. 3. Recipient’s Catalog No.
4. Title and Subtitle
Analysis of SHRP2 Data to Understand Normal and Abnormal Driving Behavior in Work Zones
5. Report Date
December 2019
6. Performing Organization Code
7. Author(s)
Carol A. Flannagan (ORCID: 0000-0001-8484-4187)*,
Selpi (ORCID: 0000-0003-2800-4479)†,
Pinar Boyraz Baykas (ORCID: 0000-0002-3665-1775)†,
Andrew Leslie (ORCID: 0000-0001-7233-6644)*,
Jordanka Kovaceva (ORCID: 0000-0002-7445-3489)†, and
Robert Thomson (ORCID: 0000-0002-8847-6753)†
8. Performing Organization Report No.
9. Performing Organization Name and Address
University of Michigan Transportation Research Institute*
2901 Baxter Rd Ann Arbor, MI 48109

Chalmers University of Technology†
Chalmersplatsen 4, 412 96 Gothenburg, Sweden

Virginia Tech Transportation Institute
3500 Transportation Research Plaza Blacksburg, VA 24061
10. Work Unit No. (TRAIS)
11. Contract or Grant No.
12. Sponsoring Agency Name and Address
U.S. Department of Transportation
Federal Highway Administration
6300 Georgetown Pike
McLean, VA 22101-2296
13. Type of Report and Period Covered
Final Report; October 2014–October 2019
14. Sponsoring Agency Code
15. Supplementary Notes
The COR/FHWA POC is Yusuf Mohamedshah (HRDS-20, ORCID: 0000-0003-0105-5559).
16. Abstract

This research project used the Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) to improve highway safety by using statistical descriptions of normal driving behavior to identify abnormal driving behaviors in work zones. SHRP2 data used in these analyses included 50 safety-critical events (SCEs) from work zones and 444 baseline events selected on a matched case-control design.

Principal components analysis (PCA) was used to summarize kinematic data into "normal" and "abnormal" driving. Each second of driving is described by one point in three-dimensional principal component (PC) space; an ellipse containing the bulk of baseline points is considered "normal" driving. Driving segments with out-of-ellipse points have a higher probability of being an SCE. Matched case-control analysis indicates that the specific individual and traffic flow made approximately equal contributions to predicting out-of-ellipse driving.

Structural Topics Modeling (STM) was used to analyze complex categorical data obtained from annotated videos. The STM method finds "words" representing categorical data variables that occur together in many events and describes these associations as "topics." STM then associates topics with either baselines or SCEs. The STM produced 10 topics: 3 associated with SCEs, 5 associated with baselines, and 2 that were neutral. Distraction occurs in both baselines and SCEs.

Both approaches identify the role of individual drivers in producing situations where SCEs might arise. A countermeasure could use the PC calculation to indicate impending issues or specific drivers who may have higher crash risk, but not to employ significant interventions such as automatically braking a vehicle with out-of-ellipse driving patterns. STM results suggest communication to drivers or placing compliant vehicles in the traffic stream would be effective. Finally, driver distraction in work zones should be discouraged.

17. Key Words
Work zones, crashes, safety, statistics, data analysis, methodology
18. Distribution Statement
No restrictions. This document is available to the public through the National Technical Information Service, Springfield, VA 22161.
19. Security Classif. (of this report)
20. Security Classif. (of this page)
21. No. of Pages
22. Price
Form DOT F 1700.7 (8-72) Reproduction of completed pages authorized.
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