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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.10280 |
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| _version_ | 1866908448927514624 |
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| author | Wang, Haomiaomiao Fennell, Conor Poojary, Swati Liu, Mingming |
| author_facet | Wang, Haomiaomiao Fennell, Conor Poojary, Swati Liu, Mingming |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_10280 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A SUMO-Based Digital Twin for Evaluation of Conventional and Electric Vehicle Networks Wang, Haomiaomiao Fennell, Conor Poojary, Swati Liu, Mingming Systems and Control 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. |
| title | A SUMO-Based Digital Twin for Evaluation of Conventional and Electric Vehicle Networks |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2507.10280 |