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

TL;DR

This paper introduces a SUMO-based digital twin for traffic simulation that accurately models mixed vehicle traffic and energy consumption, validated with multi-sensor data and demonstrating high fidelity in traffic and emission estimates.

Contribution

The study develops and validates a SUMO-based digital twin capable of simulating mixed ICEV-EV traffic with high accuracy using multi-sensor data fusion, even under partial information scenarios.

Findings

Achieved 93.1% accuracy in average speed estimation.

Achieved 97.1% accuracy in average trip length estimation.

Overestimated CO2 emissions by only 0.8-2.4%.

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.