交通モデルをより効率的にする方法を研究者が発見(Researchers Find Way to Make Traffic Models More Efficient)

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2022-05-05 ノースカロライナ州立大学(NCState)

Photo credit: Ryoji Iwata.

ノースカロライナ州立大学の研究者たちは、特定の時間や場所における交通量を予測するモデルを用いて、信号のパターンから携帯電話のアプリに至るまで、A地点からB地点への移動方法を知らせています。このたび、これらのモデルの計算量を減らし、より効率的に動作させる方法を実証しました。

<関連情報>

システム最適化動的トラフィックアサインメントのための分散勾配アプローチ A Distributed Gradient Approach for System Optimal Dynamic Traffic Assignment

Mehrzad Mehrabipour,Ali Hajbabaie
IEEE Transactions on Intelligent Transportation Systems  Published: 20 April 2022
DOI: 10.1109/TITS.2022.3163369

Abstract

This study presents a distributed gradient-based approach to solve system optimal dynamic traffic assignment (SODTA) formulated based on the cell transmission model. The algorithm distributes SODTA into local sub-problems, who find optimal values for their decision variables within an intersection. Each sub-problem communicates with its immediate neighbors to reach a consensus on the values of common decision variables. A sub-problem receives proposed values for common decision variables from all adjacent sub-problems and incorporates them into its own offered values by weighted averaging and enforcing a gradient step to minimize its objective function. Then, the updated values are projected onto the feasible region of the sub-problems. The algorithm finds high quality solutions in all tested scenarios with a finite number of iterations. The algorithm is tested on a case study network under different demand levels and finds solutions with at most a 5% optimality gap.

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