Phase 2 – High Visibility Crosswalk Pedestrian Study: Concept to Countermeasure – Research to Deployment Using the SHRP2 Safety Data

Majka, Kevin; Pierowicz, John; Blatt, Alan; Anastasopoulos, Panagiotis Ch.; Pantangi, Sarvani Sonduru; Eker, Ugur; Fountas, Grigorios; Ahmed, Sheikh Shahriar · 2020 · ROSA P / New York State Department of Transportation

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Summary

This study evaluates the effectiveness of high-visibility crosswalk (HVC) markings in improving pedestrian safety at uncontrolled locations, addressing a debate within the traffic safety community regarding their impact on driver behavior. Motivated by high pedestrian fatality rates in the United States and New York State, the research investigates how different HVC designs (ladder, continental, bar-pair), locations (mid-block vs. end-of-block), and associated signage influence vehicle kinematics and driver scanning patterns. The study aims to determine if HVCs reduce speed and acceleration, alter eye glance behavior, and if these effects vary by driver demographics. The researchers utilized naturalistic driving data from the Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study, covering five U.S. test sites between 2011 and 2013. The analysis focused on 18 uncontrolled HVC locations where sufficient data existed for both pre- and post-installation comparisons. A total of 3,480 traversals by 183 drivers were analyzed using forward-facing video and time-series data. The methodology involved identifying upstream benchmark points to measure vehicle speed, acceleration, throttle pedal actuation (TPA), and brake application. Additionally, interior video data were processed to analyze driver eye glance directions and durations. Statistical modeling employed correlated grouped random parameters linear regression and binary logit models to account for panel effects and unobserved heterogeneity, using safety surrogates in the absence of observed crashes. The results indicate that HVC presence generally reduces vehicle speed, acceleration, and TPA at both benchmark and crosswalk locations. Ladder-type configurations were found to be the most effective design, significantly improving safety surrogates and increasing drivers’ external scanning patterns. The simultaneous presence of HVC markings and advance pedestrian crossing signs yielded mixed effects on acceleration but decreased the acceleration difference between the benchmark and the crosswalk. Location and design mattered: end-of-block HVCs increased the likelihood of acceleration and TPA decreases, while bar-pair types sometimes increased TPA at the crosswalk. Driver demographics also influenced outcomes; younger drivers were more likely to increase acceleration at the benchmark, whereas older drivers showed mixed effects on speed. The presence of lead vehicles and the absence of parked cars near the crosswalk further reduced speed differences. The study concludes that HVCs are effective countermeasures, particularly when ladder markings are used in conjunction with advance signage. It recommends targeting education and awareness programs toward young drivers (under 25) and older drivers (over 65) to enhance HVC effectiveness. The findings support the New York State Department of Transportation’s efforts to implement systemic pedestrian safety treatments. By leveraging detailed naturalistic driving data, this research provides a more comprehensive evaluation of HVC effectiveness than traditional crash-based studies, offering specific guidance on design and placement to optimize pedestrian safety.

Key finding

Ladder-type high-visibility crosswalk configurations combined with advance pedestrian signage significantly improved safety surrogates by reducing vehicle speed and acceleration while increasing driver external scanning patterns.

Methodology

naturalistic

Sample size: 183

Provenance

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

Topics

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• pedestrian behavior perception
• rail grade crossings
• driver vru interaction
• vru facing ehmi

Information type

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