Estimate collective cooperativeness of driving agents in mixed traffic flow

• URL: http://arxiv.org/abs/2408.07297v1
• Date: Wed, 31 Jul 2024 00:15:54 GMT
• Title: Estimate collective cooperativeness of driving agents in mixed traffic flow
• Authors: Di Chen, Jia Li, H. Michael Zhang,
• Abstract summary: Cooperation is a ubiquitous phenomenon in many natural, social, and engineered systems that contain multiple agents. We propose a unified conceptual framework to estimate collective cooperativeness of driving agents. Our case study indicates the existence of collective cooperativeness between human-driven passenger cars and trucks in real-world traffic.
• Score: 21.67640928933297
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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