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Evidence and quantification of cooperation of driving agents in mixed traffic flow

Di Chen, Jia Li, H. Michael Zhang

TL;DR

This paper addresses how collective cooperativeness emerges in mixed-autonomy traffic and how it can be identified from real trajectory data. It develops a unified, multi-space framework that links microscopic vehicle interactions to macroscopic traffic states, defines empirical regimes, and imparts a data-driven method to infer a surplus-split factor $\lambda$ that allocates cooperation gains. Applying the framework to NGSIM I-80 data, the authors demonstrate a positive cooperation surplus $s>0$ and quantify the likelihood of 2-pipe (Pareto-efficient) equilibria, finding $P_{coop}\approx 0.138$ and a balanced but inequitable split with $\hat{\lambda}\approx 0.807$, favoring trucks. The work provides practical means to measure and potentially steer cooperation in mixed traffic, with implications for the design of mixed-autonomy systems and policies.

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.

Evidence and quantification of cooperation of driving agents in mixed traffic flow

TL;DR

This paper addresses how collective cooperativeness emerges in mixed-autonomy traffic and how it can be identified from real trajectory data. It develops a unified, multi-space framework that links microscopic vehicle interactions to macroscopic traffic states, defines empirical regimes, and imparts a data-driven method to infer a surplus-split factor that allocates cooperation gains. Applying the framework to NGSIM I-80 data, the authors demonstrate a positive cooperation surplus and quantify the likelihood of 2-pipe (Pareto-efficient) equilibria, finding and a balanced but inequitable split with , favoring trucks. The work provides practical means to measure and potentially steer cooperation in mixed traffic, with implications for the design of mixed-autonomy systems and policies.

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.
Paper Structure (23 sections, 2 theorems, 22 equations, 17 figures, 6 tables, 1 algorithm)

This paper contains 23 sections, 2 theorems, 22 equations, 17 figures, 6 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Equilibrium traffic models and collective cooperativeness
  3. Prior definition and properties of collective cooperativeness
  4. Methodology
  5. Representation of collective cooperativeness in multi-space
  6. An intuitive example
  7. Physical, fluid, and strategic space representation
  8. Proposed framework
  9. Adaptation and refinement of collective cooperativeness concept
  10. Macroscopic state computation
  11. Practical definition of traffic regimes
  12. Empirical existence condition of collective cooperativeness
  13. Identification of surplus split factor
  14. Summary of identification process
  15. Case study
  16. ...and 8 more sections

Key Result

Proposition 1

At Pareto-efficient NE, the following equivalence holds: I. The condition of reaching 2-pipe equilibrium $u_{1} \geq u^*$ and $u_{2} \geq u^*$ (the equal sign cannot hold simultaneously) is equivalent to the condition $p_1^*+p_2^* < 1$. II. The condition of reaching 1-pipe equilibrium $u_{1} = u^*$

Figures (17)

  • Figure 1: A simple illustration of collective cooperativeness between two classes of traffic agents
  • Figure 2: Physical, fluid, and strategic spaces
  • Figure 3: Framework to identify and characterize collective cooperativeness in real-world mixed traffic
  • Figure 4: Illustration of microscopic and macroscopic density in ring road with mixed traffic
  • Figure 5: Snapshots of the mixed traffic at different times
  • ...and 12 more figures

Theorems & Definitions (4)

  • Definition 1: Cooperation surplus
  • Definition 2: Surplus split factor
  • Proposition 1: Equivalent condition of Pareto-efficient NE
  • Proposition 2: Equity of benefit allocation