Leveraging digital twin technology for traffic optimization: a pathway to sustainable urban transportation

Urbanization and rapid population growth have intensified
vehicular traffic, leading to congestion, increased travel
times, and environmental challenges, particularly carbon emissions. Addressing these issues requires innovative approaches that can optimize traffic flow while minimizing environmental impacts. Digital twin technology, a virtual replication of physical systems utilizing real-time data, has emerged as a promising solution in urban transportation management. This paper presents DT-STOP (Digital Twin for Sustainable Traffic Optimization and Planning), a novel framework leveraging computer vision and edge computing to optimize traffic management in unstructured traffic environments. DT-STOP is designed specifically for the
complex and often chaotic traffic scenarios seen in countries
like India, where lane discipline is minimal and vehicle diversity is high. Through the integration of digital twins and advanced AI-driven computer vision, DT-STOP enables dynamic traffic management, and improves urban mobility. The effectiveness of our approach is demonstrated through a case study in an urban Indian setting, showcasing the efficiency of integrating computer vision in unstructured traffic environments.