智慧城市中基于邻居嵌入图神经网络的人群配送交通管理
Neighbor-embedded Graph Neural Network-based Crowd Delivery Traffic Management in Smart City
- National Sun Yat-sen University（国立中山大学）
- VIZJA University（维兹亚大学）
- University of Aizu（会津大学）
- Indian Institute of Information Technology Bhopal（博帕尔印度信息技术学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对智慧城市交通拥堵问题，提出含TCPu和TOMu的NeCDM模型，通过GNN优化预测流量并智能选车，在损失和计算时间上实现显著提升，助力达成SMT与SCPs目标。
AI中文摘要：
车辆交通的大幅激增给全球智能动员与交通（Smart Mobilization and Transportation, SMT）的实现带来了巨大挑战。当前方法主要通过拥堵预测开展车辆交通管理，但未能满足减少交通量、合理选择车辆以缓解智慧城市（Smart Cities, SmCt）拥堵等核心目标。为解决这些问题，本研究提出一种新型基于邻居嵌入图神经网络的人群配送交通管理模型（Neighbor-Embedded Graph Neural Network-based Crowd Delivery Traffic Management, NeCDM），该模型包含两个关键组件：交通拥堵预测单元（Traffic Congestion Prediction Unit, TCPu）和交通观测与管理单元（Traffic Observation and Management Unit, TOMu）。其中，TCPu利用图神经网络（Graph Neural Network, GNN）优化，精准预测智慧城市生态系统内各配送站点的交通流量水平；TOMu则为人群配送请求（Crowd Delivery Requests, CDR）智能选择最合适的配送车辆。本研究强调，人群配送是实现智能动员与交通（SMT）目标的可行方案，且需符合智慧城市参数（Smart City Parameters, SCPs），包括降低碳排放、缩短出行时间、最小化出行距离。所提模型在计算效率上实现显著提升，L1损失（£）降低最多达4.03%，L2损失（£_rmse）降低16.66%，计算时间缩短7.64%。
英文摘要：
The significant upsurge in vehicle traffic presents a considerable challenge in the pursuit of smart mobilization and transportation (SMT) worldwide. Current approaches primarily focus on vehicular traffic management through congestion prediction but fall short in addressing essential objectives such as traffic reduction and appropriate vehicle selection to alleviate congestion in smart cities ($SmCt$). To address these concerns, this work introduces a novel \textit{Neighbor-Embedded Graph Neural Network-based Crowd Delivery Traffic Management} (NeCDM) Model, comprising two key components: the Traffic Congestion Prediction Unit (TCPu) and the Traffic Observation and Management Unit (TOMu). The TCPu utilizes Graph Neural Network (GNN) optimization to accurately predict traffic flow levels at various delivery stations within $SmCt$ ecosystems. Additionally, the TOMu facilitates the intelligent selection of the most suitable delivery vehicles for fulfilling crowd delivery requests ($CDR$). This work emphasizes the potential of crowd delivery as a feasible solution for achieving SMT goals while adhering to smart city parameters ($\mathcal{SCP}$s), such as reduced carbon emissions, shorter travel times, and minimized travel distances. The proposed model achieves notable improvements in computational efficiency, including reductions of up to 4.03\% in L1 loss ($£$), 16.66\% in L2 loss ($£_{rmse}$), and 7.64\% in computation time.
