Agent-Based Modeling and Simulation in the Dilemma Zone

Kim, Sojung; Son, Young-Jun; Jeffers, Mary Anne; Williams, Jason; Chiu, Yi-Chang · 2015 · ROSA P / United States. Federal Highway Administration

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Summary

This study addresses the safety risks associated with the "dilemma zone" (DZ) at signalized intersections, defined as the area where a driver cannot safely stop before the stop line nor clear the intersection before the light turns red. Traditional DZ models rely primarily on vehicle speed and distance, often failing to account for environmental factors that influence driver decision-making. The research aims to develop a more realistic DZ behavior model by incorporating external variables, specifically the presence of pedestrian countdown signals, red-light photo enforcement cameras, and the behavior of adjacent vehicles. To achieve this, the researchers utilized the Federal Highway Administration’s Highway Driving Simulator to collect behavioral data from 99 licensed drivers. The experimental design manipulated five key factors: facility speed limit (40 or 55 mph), whether the driver was in a hurry, the presence of a red-light camera, the presence of a pedestrian countdown signal, and the actions of an adjacent vehicle (stopping or proceeding). Participants were divided into four groups based on speed and urgency conditions. Statistical analysis using Generalized Estimating Equations (GEE) assessed how these factors influenced the probability of proceeding through the DZ. Subsequently, an Agent-Based Modeling and Simulation (ABMS) model was developed using the Extended Belief-Desire-Intention (E-BDI) framework. This framework uses probabilistic inference, calibrated via Structural Equation Modeling (SEM), to represent the uncertain perception and decision-making processes of drivers by integrating both internal information (speed, distance) and external environmental cues. The results indicated that all five experimental factors significantly influenced driver decisions. Drivers in the 40 mph condition and those not in a hurry were more likely to proceed through the DZ than their counterparts. Crucially, the presence of external information altered behavior: the absence of a red-light camera or pedestrian countdown signal increased the likelihood of proceeding, while the presence of these features mitigated red-light violations. Drivers tended to follow the actions of adjacent vehicles when uncertain but were more likely to stop if a red-light camera was present. The ABMS model successfully replicated these behaviors, demonstrating that sufficient external information allows for accurate prediction of driver decisions. The significance of this work lies in its validation of a comprehensive DZ model that accounts for complex environmental interactions, moving beyond simple kinematic calculations. By integrating the E-BDI framework, the model provides realistic predictions of driver behavior under various traffic conditions. The findings suggest that deploying countermeasures such as red-light cameras and pedestrian countdown signals can effectively reduce red-light running and associated crash risks. This approach offers transportation agencies a robust tool for designing safer intersections and evaluating the efficacy of safety interventions.

Key finding

The presence of a red-light photo enforcement camera or a pedestrian countdown signal significantly reduced the probability of drivers proceeding through a dilemma zone intersection.

Methodology

simulator

Sample size: 99

Provenance

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

Topics

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• traffic density
• mental model of traffic
• rail grade crossings
• gap acceptance

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: behavioral performance data
• Methodological Resource: tool software
• Theoretical Contribution: computational model