Applications of AI-Driven Practice in Road Freight Transport Decarbonisation: A Quantitative Systematic Literature Review
道路貨物輸送の脱炭素化におけるAI駆動実践の応用：定量的系統的文献レビュー (AI 翻訳)
Minyou Qing, Suhaiza Zailani
🤖 gxceed AI 要約
日本語
本レビューは、道路貨物輸送（RFT）の脱炭素化におけるAI駆動実践の応用を体系的に分析した。227件の論文をSPAR-4-SLRプロトコルに従ってレビューし、ビブリオメトリック分析とサイエンスマッピングを実施。過去10年間で発表が指数関数的に増加し、特に過去3年間で顕著である。8つの知識クラスター（パワートレインエネルギー管理、戦略的フリート電化、インターモーダル回廊計画、物理情報に基づく同時最適化など）を特定し、車両レベルの効率改善からシステム統合型の脱炭素化アーキテクチャへの移行を明らかにした。将来の研究は、AI駆動の需要予測、パワートレイン制御、排出会計を統合したエンドツーエンドの協調最適化フレームワークに焦点を当てるべきと提言している。
English
This review systematically analyzes the application of AI-driven practices in road freight transport (RFT) decarbonization. Following the SPAR-4-SLR protocol, 227 articles were reviewed using bibliometric analysis and science mapping. Publications have grown exponentially over the past decade, especially in the last three years. Eight knowledge clusters were identified, including powertrain energy management, strategic fleet electrification, intermodal corridor planning, and physics-informed co-optimization, evidencing a shift from vehicle-level efficiency gains toward system-integrated decarbonization architectures. The authors argue that future advances will come from coupling AI-driven demand forecasting, powertrain control, and emission accounting into end-to-end collaborative optimization frameworks.
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
日本の物流業界は2024年問題やカーボンニュートラル宣言に対応するため、AIを活用した輸送効率化と排出削減の両立が急務。本レビューは、車両電化や運行最適化など、日本企業が導入可能なAI適用領域を俯瞰でき、SSBJ開示やScope 3排出量算定の実務にも示唆を与える。
In the global GX context
Globally, road freight is a hard-to-abate sector, and AI-driven optimization is emerging as a key lever for decarbonization. This review provides a structured map of AI applications across the freight value chain, which is valuable for companies aligning with TCFD/ISSB disclosure requirements and for policymakers designing transport decarbonization strategies.
👥 読者別の含意
🔬研究者:Provides a structured taxonomy of AI applications in freight decarbonization and identifies research gaps for future work.
🏢実務担当者:Offers a landscape of AI tools for fleet electrification, route optimization, and emission accounting that can inform corporate decarbonization roadmaps.
🏛政策担当者:Highlights the need for system-level integration and data sharing to enable AI-driven freight decarbonization, relevant for transport and climate policy.
📄 Abstract（原文）
This paper systematically reviews the applications of artificial intelligence (AI)-driven practices in road freight transport (RFT) decarbonisation. RFT scenarios are becoming increasingly complex, and the deep decarbonisation challenge is still severe. AI-driven practices are regarded as a transformative frontier and a key path to addressing them. However, related research is mainly confined to a single disciplinary background, which may hinder the field from making substantial progress in designing diverse solutions and exploring collaborative decarbonisation mechanisms in multiple transportation stages. This review conducts bibliometric analysis and science mapping on 227 articles, following the SPAR-4-SLR protocol. The performance analysis reveals exponential growth in publications over the past decade, especially in the past 3 years. Bibliographic coupling identifies eight knowledge clusters including powertrain energy management, strategic fleet electrification, intermodal corridor planning, and physics-informed co-optimisation, collectively evidencing a field-wide transition from isolated vehicle-level efficiency gains toward system-integrated decarbonisation architectures. Cross-cluster analysis exposes structural integration gaps at the boundaries of mature clusters, from which stage-specific future research opportunities are proposed across different RFT phases. The findings argue that consequential advances will arise from coupling AI-driven demand forecasting, powertrain control, and emission accounting into end-to-end collaborative optimisation frameworks, rather than from continued single-technology refinement.
🔗 Provenance — このレコードを発見したソース
- crossref https://doi.org/10.3390/su18158009first seen 2026-08-07 05:52:33
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