下一代交通网络中由数字孪生驱动的绿色交通管理

IF 5.3 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Open Journal of Vehicular Technology Pub Date : 2024-10-22 DOI:10.1109/OJVT.2024.3484956
Kubra Duran;Lal Verda Cakir;Achille Fonzone;Trung Q. Duong;Berk Canberk
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引用次数: 0

摘要

不断发展的交通网络需要无缝集成和有效利用基础设施,以形成下一代交通网络。此外,交通网络还应能够适时捕捉交通流数据,并及时采取可持续减排措施。然而,传统的交通网络无法处理实时更新,也无法根据动态条件下的要求采取行动。在此,数字孪生(DT)可通过强大的建模和智能功能来加强交通管理。因此,我们采用一种新颖的结对方法,提出了一个由 DT 驱动的生态监管(DTER)框架。我们定义了特定运输工具的孪生采样率,以捕捉运输网络中的适时数据。此外,我们还使用多层感知器(MLP)、双向长短期记忆(Bi-LSTM)和 BANE 嵌入进行排放预测。我们通过拉普拉斯矩阵分析对排放风险区域进行聚类。之后,我们会根据 Q-learning 的输出结果,通过设置高排放区域路口的车辆限行数量来提出行动建议。总之,DTER 利用其针对特定交通的双采样率控制排放,并通过考虑排放预测自动管理交通行动。我们注意到,DTER 的适时数据捕获成功率提高了 19%,查询时间缩短了 30%。此外,与基线相比,我们对排放预测智能算法的混合实施提高了准确性。最后,通过提供有效的碳追踪框架,DTER 的自主建议实现了 20% 的减排。
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Digital Twin-Empowered Green Mobility Management in Next-Gen Transportation Networks
Evolving transportation networks need seamless integration and effective infrastructure utilisation to form the next-generation transportation networks. Also, they should be capable of capturing the traffic flow data at the right time and promptly applying sustainable actions toward emission reduction. However, traditional transportation networks cannot handle right-time updates and act upon the requirements in dynamic conditions. Here, Digital Twin (DT) enables the development of enhanced transportation management via robust modelling and intelligence capabilities. Therefore, we propose a DT-empowered Eco-Regulation (DTER) framework with a novel twinning approach. We define a transport-specific twin sampling rate to catch right-time data in a transportation network. Besides, we perform emission prediction using Multi-Layer Perceptron (MLP), Bidirectional Long Short-Term Memory (Bi-LSTM), and BANE embeddings. We perform Laplacian matrix analysis to cluster the risk zones regarding the emissions. Thereafter, we recommend actions by setting the number of vehicle limits of junctions for high-emission areas according to the outputs of Q-learning. In summary, DTER takes control of the emission with its transport-specific twin sampling rate and automated management of transportation actions by considering the emission predictions. We note DTER achieves 19% more successful right-time data capturing, with 30% reduced query time. Moreover, our hybrid implementation of intelligent algorithms for emission prediction resulted in higher accuracy when compared to baselines. Lastly, the autonomous recommendations of DTER achieved $\sim$ 20% decrease in emissions by presenting an effective carbon tracing framework.
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CiteScore
9.60
自引率
0.00%
发文量
25
审稿时长
10 weeks
期刊最新文献
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