Evaluation of Work Zone Mobility by Utilizing Naturalistic Driving Study Data

Zhou, Huaguo Hugo; Turochy, Rod E; Xu, Dan · 2019 · ROSA P / Southeastern Transportation Research, Innovation, Development and Education Center (STRIDE)

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Summary

This study addresses the gap in transportation research regarding the application of Naturalistic Driving Study (NDS) data to evaluate work zone mobility. While existing methods for estimating work zone capacity rely on simulation-based, nonparametric, or parametric approaches—such as those in the Highway Capacity Manual (HCM)—they often fail to account for individual driver characteristics and specific work zone configurations. The authors aimed to determine if existing NDS databases could be reused to develop or update traffic flow and capacity models, specifically focusing on speed-flow-density relationships and car-following behaviors. The researchers utilized a complimentary NDS dataset originally collected for safety studies, comprising time-series data, forward-view videos, radar data, and driver risk perception questionnaires. From an initial pool of 420 baseline and 256 safety-critical events, the team identified 38 safety-critical events and 64 baseline trips for analysis. The study focused on three freeway work zone configurations: two-to-one lane closures, two-to-two shoulder closures, and three-to-three shoulder closures. Methodologically, the authors applied fundamental traffic flow theory, Greenshield’s model, and HCM capacity methods to analyze speed, flow, and density. They also examined time and space headway distributions in relation to driver demographics, including gender, age, and risk perception scores. The results demonstrated a linear relationship between speed and density in work zones. Notably, capacities predicted by NDS-derived speed-flow regression models were consistently higher than those estimated by the HCM, suggesting the HCM may underestimate work zone capacity. Estimated free-flow speeds were 62 mph for two-to-one lane closures, 73 mph for two-to-two shoulder closures, and 72 mph for three-to-three shoulder closures. Analysis of headway distributions revealed that 85% of drivers maintained time headways between 0.8 and 2.8 seconds. Driver characteristics significantly influenced behavior: female drivers exhibited more consistent headway selections than males, though males maintained longer average time headways (2.6 s vs. 2.0 s). Senior drivers (≥60 years) chose the longest time headways (2.6 s), while drivers under 24 maintained the shortest (2.1 s) and exhibited the lowest risk perception scores, indicating higher risk-taking behavior. The study concludes that NDS data is a viable resource for developing car-following models that incorporate driver population factors, which can improve the accuracy of work zone capacity methods and simulation tools. The findings imply that current macroscopic models like the HCM lack the granularity to account for diverse driver behaviors and specific configurations. The authors recommend further research to collect more comprehensive NDS trip data across various work zone types to refine these models for better planning and operational management.

Key finding

Capacities predicted from naturalistic driving speed-flow regression models are typically greater than those from Highway Capacity Manual estimations, with free flow speeds estimated at 62, 73, and 72 mph for two-to-one lane closure, two-to-two shoulder closure, and three-to-three shoulder closure configurations respectively.

Methodology

naturalistic

Sample size: 102

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• work zones
• traffic density
• naturalistic crash near crash
• speed choice

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: crash risk outcomes, observational prevalence
• Methodological Resource: dataset resource