Research on visualization and interactive model of highway design based on virtual reality technology

This study combines a Convolutional Backpropagation Neural Network (Conv-BPNN) model with Virtual Reality (VR) technology to propose a novel method to highway design and traffic system optimization. The study intends to quantify vehicle energy consumption and emissions under various design scenarios, assess the effectiveness of route planning, and replicate real-world traffic flow. With the use of traffic flow data and the China Vehicle Emission Dataset (2020–2024), the suggested model can reliably forecast patterns of fuel consumption and CO₂ emissions in a range of traffic densities and route configurations. By integrating virtual reality (VR), stakeholders may evaluate and see infrastructure projects interactively, simplifying complicated traffic dynamics. When compared to conventional modeling techniques, experimental findings show a notable improvement in route choice accuracy, design efficiency, and pollution reduction. This study highlights the benefits of integrating immersive simulation technology with data-driven neural models to facilitate environmental impact assessment and sustainable transportation planning.