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 reducti...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Open Journal of Vehicular Technology |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10726797/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832582296759173120 |
|---|---|
| author | Kubra Duran Lal Verda Cakir Achille Fonzone Trung Q. Duong Berk Canberk |
| author_facet | Kubra Duran Lal Verda Cakir Achille Fonzone Trung Q. Duong Berk Canberk |
| author_sort | Kubra Duran |
| collection | DOAJ |
| description | 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 <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 20% decrease in emissions by presenting an effective carbon tracing framework. |
| format | Article |
| id | doaj-art-4186367a0a3249b9923ac8f8a3fcffdc |
| institution | Kabale University |
| issn | 2644-1330 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Vehicular Technology |
| spelling | doaj-art-4186367a0a3249b9923ac8f8a3fcffdc2025-01-30T00:04:15ZengIEEEIEEE Open Journal of Vehicular Technology2644-13302024-01-0151650166210.1109/OJVT.2024.348495610726797Digital Twin-Empowered Green Mobility Management in Next-Gen Transportation NetworksKubra Duran0https://orcid.org/0000-0002-5502-9690Lal Verda Cakir1https://orcid.org/0000-0002-2577-9562Achille Fonzone2https://orcid.org/0000-0001-8159-7731Trung Q. Duong3https://orcid.org/0000-0002-4703-4836Berk Canberk4https://orcid.org/0000-0001-6472-1737School of Computing, Engineering and Built Environment, Edinburgh Napier University, Edinburgh, U.K.School of Computing, Engineering and Built Environment, Edinburgh Napier University, Edinburgh, U.K.Transport Research Institute, Edinburgh Napier University, Edinburgh, U.K.Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's, CanadaSchool of Computing, Engineering and Built Environment, Edinburgh Napier University, Edinburgh, U.K.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 <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 20% decrease in emissions by presenting an effective carbon tracing framework.https://ieeexplore.ieee.org/document/10726797/Autonomous traffic managementdigital twinreinforcement learningtwin sampling rate |
| spellingShingle | Kubra Duran Lal Verda Cakir Achille Fonzone Trung Q. Duong Berk Canberk Digital Twin-Empowered Green Mobility Management in Next-Gen Transportation Networks IEEE Open Journal of Vehicular Technology Autonomous traffic management digital twin reinforcement learning twin sampling rate |
| title | Digital Twin-Empowered Green Mobility Management in Next-Gen Transportation Networks |
| title_full | Digital Twin-Empowered Green Mobility Management in Next-Gen Transportation Networks |
| title_fullStr | Digital Twin-Empowered Green Mobility Management in Next-Gen Transportation Networks |
| title_full_unstemmed | Digital Twin-Empowered Green Mobility Management in Next-Gen Transportation Networks |
| title_short | Digital Twin-Empowered Green Mobility Management in Next-Gen Transportation Networks |
| title_sort | digital twin empowered green mobility management in next gen transportation networks |
| topic | Autonomous traffic management digital twin reinforcement learning twin sampling rate |
| url | https://ieeexplore.ieee.org/document/10726797/ |
| work_keys_str_mv | AT kubraduran digitaltwinempoweredgreenmobilitymanagementinnextgentransportationnetworks AT lalverdacakir digitaltwinempoweredgreenmobilitymanagementinnextgentransportationnetworks AT achillefonzone digitaltwinempoweredgreenmobilitymanagementinnextgentransportationnetworks AT trungqduong digitaltwinempoweredgreenmobilitymanagementinnextgentransportationnetworks AT berkcanberk digitaltwinempoweredgreenmobilitymanagementinnextgentransportationnetworks |