Total-delay-based Max Pressure: A Max Pressure Algorithm Considering Delay Equity

This paper proposes a novel decentralized signal control algorithm that seeks to improve traffic delay equity, measured as the variation of delay experienced by individual vehicles. The proposed method extends the recently developed delay-based max pressure (MP) algorithm by using the sum of cumulative delay experienced by all vehicles that joined a given link as the metric for weight calculation. Doing so ensures the movements with lower traffic loads have a higher chance of being served as their delay increases. Three existing MP models are used as baseline models with which to compare the proposed algorithm in microscopic simulations of both a single intersection and a grid network. The results indicate that the proposed algorithm can improve the delay equity for various traffic conditions, especially for highly unbalanced traffic flows. Moreover, this improvement in delay equity does not come with a significant increase to average delay experienced by all vehicles. In fact, the average delay from the proposed algorithm is close to-and sometimes even lower than-the baseline models. Therefore, the proposed algorithm can maintain both objectives at the same time. In addition, the performance of the proposed control strategy was tested in a connected vehicle environment. The results show that the proposed algorithm outperforms the other baseline models in both reducing traffic delay and increasing delay equity when the penetration rate is less or equal to 60%, which would not be exceeded in reality in the near future.

Automated summary

This paper proposes a novel decentralized signal control algorithm that improves traffic delay equity without significantly increasing average delay. The algorithm uses the sum of cumulative delay as the weight calculation metric, ensuring that less congested movements have a higher chance of being served. Microscopic simulations comparing the proposed algorithm with three baseline models demonstrate its effectiveness, especially for highly unbalanced traffic flows. Additionally, the algorithm outperforms other models in reducing traffic delay and increasing delay equity in a connected vehicle environment with a penetration rate less than or equal to 60%.