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

TL;DR

This paper presents a digital twin-based data-driven approach to optimize traffic flow and SDVN performance, reducing waiting times and flow-table overflow in connected vehicle networks.

Contribution

It introduces a digital twin framework and two novel solutions for traffic efficiency and SDVN optimization, addressing multi-domain challenges with real-time data and simulation.

Findings

Reduced average waiting times by 22% at 40% CAV penetration

Achieved 50% reduction in flow-table space requirements at 100% CAV penetration

Demonstrated effectiveness of digital twin in real-time traffic and network management

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.