Empirically-based performance assessment & simulation of pedestrian behavior at unsignalized crossings.

Schroeder, Bastian; Rouphail, Nagui; Salamati, Katy; Hunter, Elizabeth; Phillips, Briana; Elefteriadou, Lily; Chase, Thomas; Zheng, Yinan; Sisiopiku, Virginia P.; Mamidipalli, Shrikanth · 2014 · ROSA P / Southeastern Transportation Research, Innovation, Development and Education Center (STRIDE)

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Summary

This research addresses the need for improved algorithms to model pedestrian-vehicle interactions at unsignalized mid-block crossings, aiming to enhance traffic operational analysis and microsimulation packages. The study was motivated by the limitations of existing models in accurately capturing the complex behavioral dynamics between drivers and pedestrians. The primary objective was to develop empirically based models for driver yielding and pedestrian gap acceptance that are compatible with microsimulation tools. The methodology involved a comprehensive data collection effort across 27 unsignalized mid-block crosswalks in Alabama, Florida, and North Carolina, covering both university campus and downtown land uses. An observational study utilized video recording to capture interaction events, measuring variables such as vehicle speed, gap times, and pedestrian attributes. Additionally, an in-vehicle study with 15 drivers in Florida provided data on driver decision distances and dynamics. The researchers developed binary logit models for driver yielding and gap acceptance, alongside nine supplemental simulation components describing vehicle dynamics and conflict identification. These algorithms were implemented in a prototype microsimulator for testing and validation against the observational data. The findings yielded specific behavioral models. The recommended driver yielding model identified that increased vehicle speed and required deceleration rates reduced the likelihood of yielding. Conversely, yielding probability increased with the presence of adjacent yields, low-speed platoons, multiple pedestrians, female pedestrians, and when located on university campuses. The gap acceptance model relied on gap length and whether the gap was a "lag" event (first arriving vehicle) or a standard gap; pedestrians were less likely to accept lag events due to the time required for evaluation. The simulation implementation successfully replicated observed behaviors, with the eleven predictive models—including driver yield decisions, pedestrian gap acceptance, and various yield dynamics—demonstrating accuracy in simulating vehicle and pedestrian delays. The significance of this work lies in providing a robust, empirically validated set of algorithms for traffic simulation software. By detailing the specific factors influencing driver and pedestrian decisions, the study offers a more nuanced understanding of unsignalized crossing interactions than previous methods. The deliverables include the prototype algorithms, a final report, and educational modules, facilitating the integration of these behavioral models into broader transportation planning and safety analyses. This research supports the development of more accurate microsimulation tools that can better assess the performance and safety of pedestrian facilities.

Key finding

Increased vehicle speed and required deceleration rate reduced the likelihood of driver yielding, while adjacent yields, low-speed platoons, multiple pedestrians, and female pedestrians increased it.

Methodology

mixed_methods

Sample size: 27

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StageOutcomeToolModelPromptAttemptsCompleted
discover success rosap 2 2026-05-23
archive success 1 2026-05-23
extract success cached 2 2026-06-10
clean success 1 2026-06-01
chunk success 1 2026-06-01
embed success 1 2026-06-02
enrich success 1 2026-05-23
promote success 1 2026-05-23
summarize success llm qwen3.6-27b-prismaquant summ-v5 3 2026-06-10
tag success vector_similarity 19 2026-06-11
verify partial 2 2026-06-10

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

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