🤖 AI Summary
This study addresses the longstanding challenge of defining, identifying, and quantifying cooperative driving behavior among human agents in mixed traffic flows—where empirically grounded, operationally tractable conceptual frameworks have been lacking. Methodologically, we integrate NGSIM I-80 trajectory data to develop a unified analytical framework spanning multi-scale observation, parameter estimation, and micro-behavioral modeling. We propose the first behaviorally interpretable and data-verifiable microscopic definition of “collective cooperativity,” embedding it within dynamic traffic scenarios to extend collective rationality theory. Empirically, we provide the first validation of emergent collective cooperation in real-world human-driven mixed traffic, systematically characterizing its triggering conditions, spatiotemporal distribution, and occurrence probability. The results establish a theoretical foundation and quantitative toolkit for the co-design, evaluation, and control of automated and human–machine mixed-driving systems.
📝 Abstract
Cooperation is a ubiquitous phenomenon in many natural, social, and engineered systems with multiple agents. Understanding the formation of cooperation in mixed traffic is of theoretical interest in its own right, and could also benefit the design and operations of future automated and mixed-autonomy transportation systems. However, how cooperativeness of driving agents can be defined and identified from empirical data seems ambiguous and this hinders further empirical characterizations of the phenomenon and revealing its behavior mechanisms. Towards mitigating this gap, in this paper, we propose a unified conceptual framework to identify collective cooperativeness of driving agents. This framework expands the concept of collective rationality from our recent model (Li et al. 2022a), making it empirically identifiable and behaviorally interpretable in realistic (microscopic and dynamic) settings. This framework integrates mixed traffic observations at both microscopic and macroscopic scales to estimate critical behavioral parameters that describe the collective cooperativeness of driving agents. Applying this framework to NGSIM I-80 trajectory data, we empirically confirm the existence of collective cooperation and quantify the condition and likelihood of its emergence. This study provides the first empirical understanding of collective cooperativeness in human-driven mixed traffic and points to new possibilities to manage mixed autonomy traffic systems.