Connected Vehicle Pilot Deployment Program Phase 3, Mobile Accessible Pedestrian Signal System (PED-SIG) – New York City Department of Transportation (NYCDOT)

Ozbay, Kaan; Gao, Jingqin; Zuo, Fan; Talas, Mohamad; Khanzada, Hisham; Roth, Gary; Rausch, Robert; Benevelli, David; Sim, Samuel; Opie, Keir · 2021 · ROSA P / United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office

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Summary

This report evaluates the Mobile Accessible Pedestrian Signal System (PED-SIG), a connected vehicle (CV) application designed to assist pedestrians with vision disabilities in safely crossing signalized intersections in New York City. The project, part of the USDOT CV Pilot Deployment Program Phase 3, aims to support the Vision Zero initiative by leveraging CV technology to improve social equity and pedestrian safety. The PED-SIG system utilizes Personal Information Devices (PIDs)—specifically iOS smartphones paired with location augmentation hardware—to receive localized Signal Phase and Timing (SPaT) and Map (MAP) messages. These data streams provide users with audio alerts and haptic prompts regarding intersection geometry, signal status, and remaining crossing time, thereby enhancing navigation independence for visually impaired individuals. The study was conducted through a collaboration between the New York City Department of Transportation (NYCDOT), New York University (NYU), and industry partners including TransCore and Harman. The experimental design involved four instrumented intersections and six predefined routes. Due to development constraints, the application was limited to the iOS platform, resulting in the use of five PID devices. Volunteer participants with vision disabilities were recruited with assistance from various disability advocacy organizations. The evaluation methodology combined quantitative operational data logs collected from the PIDs with qualitative feedback gathered via pre- and post-experiment surveys. The system architecture ensured data privacy through encryption, obfuscation, and secure server transmission, while a Location Augmentation Device was employed to improve smartphone positioning accuracy in urban environments. The findings indicate that the PED-SIG application successfully provided timely and accurate signal information to participants. Operational data logs and field observations demonstrated that the system effectively communicated signal states, such as "Walk" or "Don't Walk," and provided countdown timers for crossing duration. Qualitative survey results revealed that participants perceived the application as useful for improving safety and confidence during street crossings. The audio and haptic feedback mechanisms were generally well-received, helping users orient themselves at intersections and confirm safe crossing windows. However, the report also notes challenges related to location accuracy in dense urban settings, which were partially mitigated by the augmentation devices, and highlights the importance of robust data security protocols to protect user privacy. The significance of this study lies in its demonstration of how connected vehicle technology can be adapted to address accessibility needs, specifically for pedestrians with vision disabilities. By validating the technical feasibility and user acceptance of the PED-SIG system, the report provides critical insights for future deployments of accessible CV applications. The lessons learned regarding system design, participant recruitment, and data management offer a framework for expanding such technologies to broader populations. Ultimately, the project supports the broader goal of integrating CV infrastructure with inclusive design principles to reduce traffic injuries and fatalities, aligning with the Vision Zero initiative’s objectives.

Key finding

The PED-SIG application successfully provided accurate and timely audio and haptic alerts to pedestrians with vision disabilities, leading to positive user feedback regarding safety and usability during field tests at instrumented intersections.

Methodology

field_study

Sample size: 24

Provenance

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

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