Evidence accumulation models provide a formal framework for studying decision making as a dynamic process unfolding over time. While these models have been extensively developed and reviewed in laboratory paradigms, their structured application in complex, ecologically valid domains has received comparatively little attention. Road traffic is a particularly relevant context for studying sustained, embodied perception action behavior, where decisions unfold under time pressure and involve continuous control and ongoing perception-action coupling. Examining how EAMs have been applied in this domain may therefore offer insights beyond discrete laboratory tasks toward decision making in real-world behavior. This semi-systematic review synthesizes 28 studies (2014-2026) applying EAMs to traffic-related behavior. We organize the literature along two dimensions: 1) modelling level, distinguishing models at the level of discrete decision-making and models at the level of continuous action control, and 2) model architecture, distinguishing evidence accumulation as either a stand-alone decision model or an embedded component within broader perception-action or interaction frameworks. These distinctions are associated with systematic differences in model architecture, parameterization, data usage, and validation strategies, reflecting task specific demands. By providing a structured overview of these patterns, this review clarifies how EAMs are currently instantiated in traffic contexts and highlights methodological challenges and future directions both in traffic modelling and in modelling of decision-making more broadly. Promising directions include laboratory work on evidence accumulation in sustained and time-varying tasks, interactive multi-individual decision-making, and the use of neurophysiological measures to identify the perceptual evidence underlying complex perception-action behavior.

翻译：证据累积模型为研究随时间展开的动态决策过程提供了正式框架。尽管这些模型在实验室范式中已得到广泛发展和综述，但其在复杂、具有生态效度的领域中的结构化应用却相对较少受到关注。道路交通是研究持续性、具身化感知-行动行为的特别相关场景，其中决策在时间压力下进行，涉及持续控制和感知-行动的实时耦合。因此，考察证据累积模型在该领域的应用，可能为超越离散实验室任务、理解真实世界行为中的决策提供洞见。这项半系统性综述综合了2014年至2026年间应用证据累积模型研究交通相关行为的28项研究。我们沿两个维度组织文献：1）建模层次，区分离散决策层面的模型与连续行动控制层面的模型；2）模型架构，区分证据累积作为独立决策模型或作为嵌入更广泛感知-行动或交互框架的组成部分。这些区分与模型架构、参数化、数据使用以及验证策略的系统性差异相关联，反映了具体任务需求。通过提供这些模式的系统化概述，本综述阐明了证据累积模型目前在交通情境中的具体实现方式，并指出了交通建模及更广泛决策建模中的方法论挑战与未来方向。有前景的方向包括：在持续性和时变任务中研究证据累积的实验室工作、多主体交互决策，以及利用神经生理学指标来识别复杂感知-行动行为背后的知觉证据。