Digital Twin Enabled Data-Driven Approach for Traffic Efficiency and Software-Defined Vehicular Network Optimization

TL;DR

This work addresses the challenge of improving traffic efficiency and SDVN reliability in IoV/ITS as connected and autonomous vehicles proliferate. It proposes a digital twin–enabled, data-driven platform that combines a physical roundabout model, DT simulations, edge computing, and V2I communications to optimize both traffic flow and SDN flow-table usage. The main contributions are a DT-based decision-support framework that reduces roundabout waiting times (achieving up to 22% reduction at 40% AV penetration) and a flow-entry lifespan optimization method that reduces flow-table occupancy by about 50% even with full CV penetration, along with mechanisms to prevent flow-table overflow and re-installations. The results demonstrate the practical impact of integrating DT with SDVN for coordinated traffic management and robust network operation in dynamic vehicular environments.

Abstract

In the realms of the internet of vehicles (IoV) and intelligent transportation systems (ITS), software defined vehicular networks (SDVN) and edge computing (EC) have emerged as promising technologies for enhancing road traffic efficiency. However, the increasing number of connected autonomous vehicles (CAVs) and EC-based applications presents multi-domain challenges such as inefficient traffic flow due to poor CAV coordination and flow-table overflow in SDVN from increased connectivity and limited ternary content addressable memory (TCAM) capacity. To address these, we focus on a data-driven approach using virtualization technologies like digital twin (DT) to leverage real-time data and simulations. We introduce a DT design and propose two data-driven solutions: a centralized decision support framework to improve traffic efficiency by reducing waiting times at roundabouts and an approach to minimize flow-table overflow and flow re-installation by optimizing flow-entry lifespan in SDVN. Simulation results show the decision support framework reduces average waiting times by 22% compared to human-driven vehicles, even with a CAV penetration rate of 40%. Additionally, the proposed optimization of flow-table space usage demonstrates a 50% reduction in flow-table space requirements, even with 100% penetration of connected vehicles.

Figures (9)

• Figure 1: System model for implementing data-driven edge solutions utilizing digital twin and software defined network.
• Figure 2: Proposed digital twin to utilize real-time data and simulation for data driven approaches.
• Figure 3: Flow entry management algorithm for reducing overflow and re-installation in SDVN.
• Figure 4: Creating vehicle-objects in the DT and comparing DT simulation traffic flow with drone-collected data.
• Figure 5: The discrepancy in average speed observed in the DT simulation underscores the importance of incorporating actual AVs into the PT for precise calibration of AV models.