新交通信号は歩行者と車の移動時間を改善する(New Traffic Signal Would Improve Travel Time for Both Pedestrians and Vehicles)

2024-03-12 ノースカロライナ州立大学(NCState)

＜関連情報＞

• https://news.ncsu.edu/2024/03/traffic-signal-pedestrians-vehicles/
• https://onlinelibrary.wiley.com/doi/10.1111/mice.13178
• https://ieeexplore.ieee.org/document/10038635

歩行者を配慮したホワイトフェイズ移動交通制御パラダイムの進展 Advancing the white phase mobile traffic control paradigm to consider pedestrians

Ramin Niroumand, Leila Hajibabai, Ali Hajbabaie

Computer-Aided Civil and Infrastructure Engineering  Published: 10 March 2024

DOI:https://doi.org/10.1111/mice.13178

Abstract

Current literature on joint optimization of intersection signal timing and connected automated vehicle (CAV) trajectory mostly focuses on vehicular movements paying no or little attention to pedestrians. This paper presents a methodology to safely incorporate pedestrians into signalized intersections with CAVs and connected human-driven vehicles (CHVs). The movements of vehicles are controlled using both traffic lights and mobile CAV controllers during our newly introduced “white phase.” CAVs navigate platoons of CHVs through the intersection when the white phases are active. In addition to optimizing CAV trajectories, the model optimally selects the status of the traffic light signal among white and green indications for vehicular and walk and do-not-walk intervals for pedestrian movements. A receding horizon-based methodology is used to capture the stochastic nature of the problem and to reduce computational complexity. The case study results show the successful operation of fleets consisting of pedestrians, CAVs, and CHVs with various demand levels through isolated intersections. The results also show that increasing the CAV market penetration rate (MPR) can decrease average intersection delay by up to 27%. Moreover, the average pedestrian, CHV, and CAV delays decrease as the CAV MPR increases and reach their minimum values with a fully CAV fleet. In addition, the presence of the white phase can decrease the intersection average delay by up to 14.7%.

分散協調による白相交差点制御： 混合交通流における移動制御パラダイム White Phase Intersection Control Through Distributed Coordination: A Mobile Controller Paradigm in a Mixed Traffic Stream

Ramin Niroumand; Leila Hajibabai; Ali Hajbabaie

IEEE Transactions on Intelligent Transportation Systems  Published:06 February 2023

DOI:https://doi.org/10.1109/TITS.2022.3226557

Abstract

This study presents a vehicle-level distributed coordination strategy to control a mixed traffic stream of connected automated vehicles (CAVs) and connected human-driven vehicles (CHVs) through signalized intersections. We use CAVs as mobile traffic controllers during a newly introduced “white phase”, during which CAVs will negotiate the right-of-way to lead a group of CHVs while CHVs must follow their immediate front vehicle. The white phase will not be activated under low CAV penetration rates, where vehicles must wait for green signals. We have formulated this problem as a distributed mixed-integer non-linear program and developed a methodology to form an agreement among all vehicles on their trajectories and signal timing parameters. The agreement on trajectories is reached through an iterative process, where CAVs update their trajectory based on shared trajectory of other vehicles to avoid collisions and share their trajectory with other vehicles. Additionally, the agreement on signal timing parameters is formed through a voting process where the most voted feasible signal timing parameters are selected. The numerical experiments indicate that the proposed methodology can efficiently control vehicle movements at signalized intersections under various CAV market shares. The introduced white phase reduces the total delay by 3.2% to 94.06% compared to cooperative trajectory and signal optimization under different CAV market shares in our tests. In addition, our numerical results show that the proposed technique yields reductions in total delay, ranging from 40.2% – 98.9%, compared to those of a fully-actuated signal control obtained from a state-of-practice traffic signal optimization software.