大規模都市ネットワークにおける低炭素交通信号制御のための高次元シミュレーション基盤最適化
High-Dimensional Simulation-Based Optimization for Low-Carbon Traffic Signal Control in Large-Scale Urban Networks (原題)
Dazhi Zhao, Zherui Liu, Li Chen
🤖 gxceed AI 要約
日本語
大規模交通信号のタイミングを、平均待ち時間とCO2排出量の二目的ブラックボックス最適化として定式化。Pareto-ハイパーボリューム誘導型信頼領域ベイズ最適化（PHV-TuRBO）を提案し、限られたシミュレーション予算で探索と活用を両立させる。北京の456変数ネットワークで待ち時間・旅行時間・CO2排出の大幅改善を実証した。
English
This study frames large-scale traffic signal timing as a high-dimensional bi-objective black-box optimization minimizing waiting time and CO2 emissions. It proposes PHV-TuRBO, combining Pareto-guided center selection with hypervolume-driven trust-region adaptation for sample efficiency. On a 456-variable Beijing network, Pareto-optimal plans substantially cut waiting time, queues, and CO2 versus the original plan.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
都市交通の脱炭素は自治体・都市計画のGX課題だが、本論文は最適化アルゴリズムが主眼で、SSBJや有報開示との直接接続は薄い。交通・都市インフラの排出削減に関心を持つ実務者には参考になる。
In the global GX context
Urban mobility decarbonization is a recognized lever in city-level climate action, but this paper centers on optimization methodology rather than disclosure. It offers a sample-efficient tool for municipalities and transport authorities pursuing emissions cuts in traffic management.
👥 読者別の含意
🔬研究者:高次元多目的ベイズ最適化のサンプル効率手法として、排出と効率のトレードオフ探索に応用可能。
🏢実務担当者:交通・都市インフラ担当者が信号制御の省エネ・排出削減の定量効果を検討する材料になる。
🏛政策担当者:自治体の交通政策において、信号最適化がCO2削減と渋滞緩和を両立しうる点を政策設計に反映できる。
📄 Abstract（原文）
Large-scale urban traffic signal optimization presents substantial challenges arising from high-dimensional decision spaces, expensive microscopic simulations, and competing mobility and environmental objectives. This study formulates large-scale traffic signal timing as a high-dimensional bi-objective black-box optimization problem, in which average waiting time and total CO2 emissions are minimized simultaneously, thereby ensuring a symmetric and preference-neutral treatment of the two conflicting objectives without imposing predefined preference weights. To address the computational challenges, a Pareto–Hypervolume Guided Trust-Region Bayesian Optimization (PHV-TuRBO) method is proposed. The framework integrates Pareto-guided center selection, exploiting front sparsity and surrogate uncertainty, with hypervolume-driven trust-region adaptation to balance exploration and exploitation under limited simulation budgets. Experiments on 100-dimensional DTLZ1 and DTLZ2 benchmarks demonstrate superior convergence and Pareto-front approximation compared with NSGA-II, ParEGO, qEHVI-MOBO, and MORBO. Further validation on a large-scale Beijing traffic network involving 456 continuous decision variables and a budget of 5000 simulator evaluations demonstrates the effectiveness of PHV-TuRBO in identifying diverse efficiency–emission trade-offs. Representative Pareto-optimal solutions achieve substantial improvements in waiting time, travel time, queue length, stop frequency, mean speed, and CO2 emissions relative to the original signal plan. These results establish PHV-TuRBO as a sample-efficient approach to high-dimensional, multi-objective traffic signal optimization for sustainable urban mobility.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/sym18111895first seen 2026-11-14 05:02:40
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。