Pedestrian Behavior Study to Advance Pedestrian Safety in Smart Transportation Systems Using Innovative LiDAR Sensors

Li, Taylor; Kothuri, Sirisha; Yang, Xianfeng (Terry) · 2023 · ROSA P / National Institute for Transportation and Communities (NITC)

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Summary

This study addresses the critical need for improved pedestrian safety at signalized intersections, where pedestrian fatalities have reached their highest levels in decades. The research is motivated by the limitations of current traffic control systems, which are heavily biased toward vehicular traffic and rely on outdated assumptions regarding pedestrian behavior, such as fixed walking speeds and perception-reaction times. Existing detection technologies, primarily push buttons, fail to provide dynamic accommodations for vulnerable users, such as those with mobility impairments, and often lack feedback mechanisms. The authors argue that smart transportation systems must leverage advanced sensing technologies to create equitable safety measures that adapt to real-time pedestrian behavior rather than relying on static design guidelines. To investigate these issues, the researchers developed a pedestrian behavior data collection system using innovative 2D LiDAR sensors, chosen for their reliability in adverse weather and lighting conditions compared to optical cameras or radar. The system was deployed at two signalized intersections to gather thousands of behavioral samples over several months. The study comprised two main components: first, an analysis of pedestrian behaviors, including waiting times, generalized perception-reaction times to the WALK signal, and crossing speeds; and second, the development and evaluation of a novel Dynamic Flashing Yellow Arrow (D-FYA) strategy. This strategy uses LiDAR-based pedestrian tracking to dynamically adjust the timing of permissive left-turn signals, either starting, postponing, or canceling the flashing arrow based on the presence and trajectories of crossing pedestrians. The D-FYA solution was evaluated using both "emulation-in-the-field" and "cabinet-in-the-loop" traffic signal simulation platforms. The findings reveal that contemporary pedestrian behaviors have evolved and differ from the recommendations in standard facility design guidelines, such as the AASHTO “Green Book.” Specifically, the data demonstrated that ADA-compliant audible pedestrian push buttons significantly reduce the "Effective Perception-Reaction" time, defined as the sum of the reaction time to the WALK signal onset and the time required to walk from the waiting area into the intersection. Furthermore, the evaluation of the D-FYA strategy showed promising results, indicating that the system could improve pedestrian safety by resolving conflicts between permissive left-turn vehicles and concurrent crossing pedestrians while maximizing left-turn capacity. The significance of this research lies in its contribution to the body of knowledge on equitable traffic safety and smart transportation systems. By providing up-to-date empirical data on pedestrian behavior, the study supports the shift from fixed signal timing to dynamic, sensor-based operations that accommodate the full spectrum of pedestrian capabilities. The successful proof-of-concept for the D-FYA mechanism offers a practical solution for reducing conflicts at intersections, thereby enhancing safety for vulnerable road users without imposing excessive delays on vehicular traffic. These findings provide a foundation for future planning and operational guidelines that prioritize active transportation modes in increasingly complex urban environments.

Key finding

The study found that ADA-compliant audible pedestrian push buttons significantly reduce pedestrians' effective perception-reaction time, and a novel dynamic flashing yellow arrow strategy based on LiDAR tracking showed promising improvements in pedestrian safety and left-turn capacity.

Methodology

field_study

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).

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

Topics

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• pedestrian behavior perception
• vru facing ehmi