AI Powered Urban Mobility Optimizer With Carbon Footprint Reduction And Smart Carpooling Integration
AIを活用した都市モビリティ最適化：炭素排出削減とスマートカープール統合 (AI 翻訳)
Jayashri Waman, Pratik Pawar, Shruti Dalvi, Rohan Mane
🤖 gxceed AI 要約
日本語
本論文は、貨物輸送に焦点を当てた既存研究とは異なり、旅客輸送向けにAIを活用した都市モビリティ最適化システム（AUMO）を提案する。炭素認識ルーティングとスマートカープールを統合し、シミュレーションによりCO2排出量と車両数の削減、占有率向上を実証した。
English
This paper proposes AUMO, an AI-powered Urban Mobility Optimizer that integrates carbon-aware routing with smart carpooling for passenger transportation, addressing a gap in freight-focused literature. Simulation results show reductions in CO2 emissions, fleet size, and improvements in vehicle occupancy and travel efficiency.
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
日本の都市部では交通渋滞とCO2削減が課題であり、本システムはカープール促進による排出削減と効率向上に貢献する可能性がある。ただし、実装には法規制や利用者行動の考慮が必要。
In the global GX context
Globally, urban passenger transport is a major emissions source. This paper extends low-carbon routing principles from freight to passenger scenarios, aligning with global decarbonization goals and smart city initiatives.
👥 読者別の含意
🔬研究者:Researchers interested in passenger-oriented carbon optimization and AI for sustainable mobility will find the AUMO framework a novel contribution.
🏢実務担当者:Urban mobility planners and ride-sharing companies can leverage the proposed system for fleet optimization and carbon reduction strategies.
🏛政策担当者:Policymakers can consider supporting smart carpooling infrastructure and integrating carbon-aware routing into urban transport policies.
📄 Abstract（原文）
Urban mobility systems are facing increasing pressure due to traffic congestion, rising fuel consumption, and growing environmental concerns. The transportation sector alone contributes a significant share of global greenhouse gas emissions, making sustainable mobility solutions a critical requirement [1]. Over the past decade, researchers have proposed several optimization-based routing models, including the Pollution Routing Problem (PRP) [5], fuel-consumption-aware vehicle routing approaches [4], and emission-sensitive routing frameworks [3]. However, the majority of these efforts have focused primarily on freight and logistics operations rather than passenger transportation. Advances in time-dependent routing models [10] and environmentally conscious vehicle routing formulations [6], [7] have improved operational efficiency and emission control in logistics networks. Nevertheless, passenger-oriented solutions such as smart carpooling and ridesharing remain relatively underexplored from a carbon-optimization perspective, despite their potential to significantly reduce vehicle usage in urban environments. To address this gap, this paper proposes AUMO, an AI-powered Urban Mobility Optimizer designed to integrate carbon-aware routing with smart carpooling for passenger mobility. The proposed framework adapts established low-carbon routing principles from freight transportation [3], [6] and applies them to urban passenger travel scenarios. A simulation-based evaluation using a microscopic traffic environment demonstrates that the proposed system achieves notable reductions in CO₂ emissions and fleet size while improving vehicle occupancy and overall travel efficiency. These results highlight the potential of intelligent carpooling systems for sustainable urban mobility.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.5281/zenodo.20728799first seen 2026-06-23 05:43:20 · last seen 2026-06-23 05:43:34
🔔 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。