Theoretical Trade-Off Between Fairness and Efficiency in the Cooperative Driving Problem for CAVs at On-Ramps

He, Zimin; Pei, Huaxin; Guo, Yuqing; Yao, Danya; Li, Li · 2023 · OpenAlex-citations

DOI: 10.1109/ojits.2023.3344216

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Summary

This paper addresses the critical issue of fairness in cooperative driving strategies for connected and automated vehicles (CAVs) at on-ramps, a common traffic bottleneck. While existing cooperative driving methods significantly improve traffic efficiency and safety by optimizing vehicle passing orders, they often neglect fairness, leading to inequitable distribution of travel delays. The authors identify a fundamental tension: strategies that maximize efficiency (minimizing total travel time) often result in unfairness, whereas fair strategies like First-In-First-Out (FIFO) can exacerbate congestion. The study aims to theoretically prove the existence of a trade-off between these two metrics and provide methods to balance them. To investigate this, the authors formulate the merging problem as a mixed-integer linear programming (MILP) problem to determine optimal passing orders. They analyze two primary strategies: a rule-based strategy that adjusts passing orders based on specific cases, and a dynamic programming (DP)-based strategy that derives global optimal orders with polynomial computational complexity. Fairness is quantified using the standard deviation of vehicle travel times, while efficiency is measured by total travel time. The theoretical analysis utilizes fundamental relations in traffic flow theory to demonstrate the trade-off in simple single-lane on-ramp scenarios. The authors then modify these strategies to incorporate fairness considerations and validate their findings through simulations, comparing the theoretical derivations with empirical results. The results confirm that enhancing traffic efficiency typically leads to increased unfairness, characterized by higher variance in individual travel times. The theoretical analysis successfully illustrates the existence of this trade-off at congested on-ramps. Simulation results show that the modified cooperative driving strategies achieve trade-offs that align with the theoretical predictions. By adjusting the strategies, the authors demonstrate that it is possible to balance efficiency and fairness, ensuring that no individual vehicle is overburdened by excessive delay while still maintaining efficient traffic flow. The study also extends these conclusions to more complex on-ramps with multiple lanes. The significance of this work lies in providing a rigorous theoretical guarantee for the trade-off between fairness and efficiency in cooperative driving, a gap previously filled only by empirical observations. This theoretical foundation supports the integration of fairness metrics into real-world traffic management systems. By offering modified strategies that balance these competing objectives, the paper contributes to the development of more equitable and efficient transportation systems, enhancing the overall travel experience and ensuring fair access to road resources for all users.

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