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わずかなコネクテッドカーで交通信号のタイミングを改善(Improving traffic signal timing with a handful of connected vehicles)

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2024-02-20 ミシガン大学

ミシガン大学の研究者は、道路上の車両のわずか6%のGPSデータを使用して、交差点での渋滞と遅延を大幅に減少させることができる交通信号の再調整システムを開発した。ジェネラルモーターズが提供する接続された車両データを使用して、ミシガン州バーミングハムで行われた18ヶ月のパイロット調査では、シグナル化された交差点での停止回数が20%から30%減少した。U-Mの研究は、Nature Communicationsに掲載された。

<関連情報>

普及率の低い車両軌跡データによる信号機の最適化 Traffic light optimization with low penetration rate vehicle trajectory data

Xingmin Wang,Zachary Jerome,Zihao Wang,Chenhao Zhang,Shengyin Shen,Vivek Vijaya Kumar,Fan Bai,Paul Krajewski,Danielle Deneau,Ahmad Jawad,Rachel Jones,Gary Piotrowicz & Henry X. Liu

Nature Communications  Published:20 February 2024

DOI:https://doi.org/10.1038/s41467-024-45427-4

わずかなコネクテッドカーで交通信号のタイミングを改善(Improving traffic signal timing with a handful of connected vehicles)

Abstract

Traffic light optimization is known to be a cost-effective method for reducing congestion and energy consumption in urban areas without changing physical road infrastructure. However, due to the high installation and maintenance costs of vehicle detectors, most intersections are controlled by fixed-time traffic signals that are not regularly optimized. To alleviate traffic congestion at intersections, we present a large-scale traffic signal re-timing system that uses a small percentage of vehicle trajectories as the only input without reliance on any detectors. We develop the probabilistic time-space diagram, which establishes the connection between a stochastic point-queue model and vehicle trajectories under the proposed Newellian coordinates. This model enables us to reconstruct the recurrent spatial-temporal traffic state by aggregating sufficient historical data. Optimization algorithms are then developed to update traffic signal parameters for intersections with optimality gaps. A real-world citywide test of the system was conducted in Birmingham, Michigan, and demonstrated that it decreased the delay and number of stops at signalized intersections by up to 20% and 30%, respectively. This system provides a scalable, sustainable, and efficient solution to traffic light optimization and can potentially be applied to every fixed-time signalized intersection in the world.

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