Cooperation is a ubiquitous phenomenon in many natural, social, and engineered systems that contain multiple agents. Characterizing and quantifying cooperativeness of driving agents is of interest and significance for two reasons. Theoretically, it will enhance the understanding of micro-macro connections and emergence of cooperation in mixed traffic. Pragmatically, this understanding will benefit the design and operations of automated and mixed-autonomy transportation systems. However, it remains unclear how the cooperativeness can be accurately defined and quantified from empirical data, and it remains open when and to what extent collective cooperativeness exists. This paper is intended to fill the gap. We propose a unified conceptual framework to estimate collective cooperativeness of driving agents leveraging a recent behavioral equilibrium model of mixed autonomy traffic (Li et al. 2022a). This framework is interpretable, theoretically consistent, and enables quantifying collective cooperativeness of traffic agents from trajectory data. We apply the framework to multilane freeway traffic employing NGSIM I-80 trajectory data set and careful data selection. Our case study indicates the existence of collective cooperativeness between human-driven passenger cars and trucks in real-world traffic and reveals its other properties that are otherwise unknown.

翻译：协作是存在于众多包含多主体的自然、社会及工程系统中的普遍现象。对驾驶主体的协作性进行表征与量化，其意义与重要性源于两方面。理论上，这将深化对混合交通中微观-宏观关联及协作涌现的理解。实践上，这一理解将有益于自动化及混合自主交通系统的设计与运营。然而，如何基于实证数据准确定义与量化协作性，以及集体协作性在何时、以何种程度存在，目前仍不明确。本文旨在填补这一空白。我们提出一个统一的概念框架，利用近期提出的混合自主交通行为均衡模型（Li et al. 2022a），来估计驾驶主体的集体协作性。该框架具有可解释性、理论一致性，并能基于轨迹数据量化交通主体的集体协作性。我们将该框架应用于多车道高速公路交通，采用了NGSIM I-80轨迹数据集及精细的数据筛选。我们的案例研究表明，现实交通中人类驾驶的乘用车与卡车之间存在集体协作性，并揭示了其原本未知的其他特性。