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Congestion Mitigation in Vehicular Traffic Networks with Multiple Operational Modalities

Doris E. M. Brown, Sajal K. Das

TL;DR

This work tackles congestion in networks where vehicles can dynamically switch among multiple operational modalities. It formulates the interaction as a repeated Bayesian Stackelberg game between a central traffic arbiter and self-interested modalities, and introduces the Trust-Aware Control Trading Strategy (TACTS), a regret-matching algorithm that updates a modality-level trust score to adaptively allocate control and steer routing decisions toward network-optimal outcomes. Theoretical analysis provides bounds on the realized travel time relative to the system optimum, and simulations on real networks show TACTS achieving near-optimal performance, especially under high congestion, with modest computational overhead. The results highlight the potential of trust-aware arbitration to mitigate selfish routing in heterogeneous, modality-flexible traffic systems, offering a practical approach for next-generation intelligent transportation networks.

Abstract

Modern commercial ground vehicles are increasingly equipped with multiple operational modalities (e.g., human driving, advanced driver assistance, remote tele-operation, full autonomy). These often rely on heterogeneous sensing infrastructures and distinct routing algorithms, which can yield misaligned perceptions of the traffic environment and route preferences. While such technologies accelerate the transition toward increasingly intelligent transportation networks, their current deployment fails to avoid challenges associated with selfish routing behavior, in which drivers or automated agents prioritize individually optimal routes instead of network-wide congestion mitigation. Existing traffic flow management strategies can address leader-follower dynamics in traffic routing problems but are not designed to account for vehicles capable of dynamically switching between multiple operational modes. This paper models the interaction between a vehicle control arbitration system and a multi-modal vehicle as a repeated single-leader, multiple follower Stackelberg game with asymmetric information. To address the intractability of computing an exact solution in this setting, we propose a Trust-Aware Control Trading Strategy (TACTS) utilizing a regret matching-based algorithm to adaptively update the arbitration system's mixed strategy over sequential, dynamic routing decisions. Theoretical results provide bounds on the realized total network travel time under TACTS algorithm relative to the system-optimal total network travel time. Experimental results of simulations between the system and a vehicle in several real-world traffic networks under various different congestion levels demonstrate that TACTS consistently reduces network-wide congestion and generally outperforms alternative routing and control-allocation strategies, particularly under high congestion and heavy induced vehicle flows.

Congestion Mitigation in Vehicular Traffic Networks with Multiple Operational Modalities

TL;DR

This work tackles congestion in networks where vehicles can dynamically switch among multiple operational modalities. It formulates the interaction as a repeated Bayesian Stackelberg game between a central traffic arbiter and self-interested modalities, and introduces the Trust-Aware Control Trading Strategy (TACTS), a regret-matching algorithm that updates a modality-level trust score to adaptively allocate control and steer routing decisions toward network-optimal outcomes. Theoretical analysis provides bounds on the realized travel time relative to the system optimum, and simulations on real networks show TACTS achieving near-optimal performance, especially under high congestion, with modest computational overhead. The results highlight the potential of trust-aware arbitration to mitigate selfish routing in heterogeneous, modality-flexible traffic systems, offering a practical approach for next-generation intelligent transportation networks.

Abstract

Modern commercial ground vehicles are increasingly equipped with multiple operational modalities (e.g., human driving, advanced driver assistance, remote tele-operation, full autonomy). These often rely on heterogeneous sensing infrastructures and distinct routing algorithms, which can yield misaligned perceptions of the traffic environment and route preferences. While such technologies accelerate the transition toward increasingly intelligent transportation networks, their current deployment fails to avoid challenges associated with selfish routing behavior, in which drivers or automated agents prioritize individually optimal routes instead of network-wide congestion mitigation. Existing traffic flow management strategies can address leader-follower dynamics in traffic routing problems but are not designed to account for vehicles capable of dynamically switching between multiple operational modes. This paper models the interaction between a vehicle control arbitration system and a multi-modal vehicle as a repeated single-leader, multiple follower Stackelberg game with asymmetric information. To address the intractability of computing an exact solution in this setting, we propose a Trust-Aware Control Trading Strategy (TACTS) utilizing a regret matching-based algorithm to adaptively update the arbitration system's mixed strategy over sequential, dynamic routing decisions. Theoretical results provide bounds on the realized total network travel time under TACTS algorithm relative to the system-optimal total network travel time. Experimental results of simulations between the system and a vehicle in several real-world traffic networks under various different congestion levels demonstrate that TACTS consistently reduces network-wide congestion and generally outperforms alternative routing and control-allocation strategies, particularly under high congestion and heavy induced vehicle flows.
Paper Structure (15 sections, 3 theorems, 23 equations, 4 figures, 1 table, 1 algorithm)

This paper contains 15 sections, 3 theorems, 23 equations, 4 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Novel Contributions of This Paper
  3. Related Work
  4. Problem Formulation
  5. Stackelberg Equilibrium
  6. Proposed Methodology
  7. Trustworthiness Model
  8. Traded Control Methodology
  9. An Illustrative Example
  10. Theoretical Analysis
  11. Performance Evaluation
  12. Performance Metrics
  13. Experiments, Datasets, Preprocessing
  14. Experimental Results
  15. Conclusion

Key Result

Lemma 1

The total network travel time achieved by the strategy $\boldsymbol{\sigma}^s$ found using the TACTS algorithm is given by

Figures (4)

  • Figure 1: An example of system-vehicle interaction in which a vehicle seeks sequence of operational modalities yielding selfish path from origin $v_o$ to destination $v_d$.
  • Figure 2: Overview of key steps of the TACTS algorithm.
  • Figure 3: Comparison of performance ratio of TACTS under low, medium, and high congestion levels for induced vehicle flows $f_c \in \{1.0, 10.0, 30.0\}$ across three networks: Anaheim, Sioux Falls, and Chicago Sketch.
  • Figure 4: Average ratio of execution time compared to TACTS in Sioux Falls network across low, medium, and high congestion levels for induced vehicle flows $f_c \in \{1.0, 10.0, 30.0\}$.

Theorems & Definitions (7)

  • Lemma 1
  • proof
  • Definition 1
  • Lemma 2
  • proof
  • Theorem 1
  • proof