标题： 基于SUMO的数字孪生用于传统和电动汽车网络的评估 显示英文标题

标题： A SUMO-Based Digital Twin for Evaluation of Conventional and Electric Vehicle Networks

摘要： 数字孪生在交通建模中的应用日益广泛，用于复制现实世界的交通动态并评估交通流动性和能源效率。 本研究提出了一种基于SUMO的数字孪生模型，在主要高速公路路段上模拟混合燃油车和电动车的交通，利用来自感应线圈、GPS探测器和收费记录的多传感器数据融合。 该模型在完整信息和部分信息场景下进行了验证，平均速度估计的准确率达到93.1%，平均行程长度估计达到97.1%。 统计指标，包括KL散度和Wasserstein距离，表明模拟和观测的交通模式之间有很强的一致性。 此外，CO2排放量仅被高估了0.8-2.4%，而电动车能耗被低估了1.0-5.4%，这突显了即使在车辆分类信息不完整的情况下，该模型也具有强大的鲁棒性。 显示英文摘要

摘要： Digital twins are increasingly applied in transportation modelling to replicate real-world traffic dynamics and evaluate mobility and energy efficiency. This study presents a SUMO-based digital twin that simulates mixed ICEV-EV traffic on a major motorway segment, leveraging multi-sensor data fusion from inductive loops, GPS probes, and toll records. The model is validated under both complete and partial information scenarios, achieving 93.1% accuracy in average speed estimation and 97.1% in average trip length estimation. Statistical metrics, including KL Divergence and Wasserstein Distance, demonstrate strong alignment between simulated and observed traffic patterns. Furthermore, CO2 emissions were overestimated by only 0.8-2.4%, and EV power consumption underestimated by 1.0-5.4%, highlighting the model's robustness even with incomplete vehicle classification information.