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

Abstract

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.