Digital twin for urban car traffic emission: A case study in Kista, Stockholm

Abstract The commitment to decarbonization is motivating urban planners to adopt emerging techniques that advance sustainability. Road traffic emissions remain a major source of greenhouse gases and pollutants, requiring precise, near-real-time monitoring for effective mitigation policies. This study introduces the design and demonstration of a digital twin (DT) platform for road traffic emission nowcasting and forecasting. The focus is on establishing a streamlined technical architecture and showcasing how the system can utilize multisource data from the Internet of Things (IoT) sensors and simulation to provide a high spatiotemporal resolution view of emissions. As a proof of concept, the platform leverages traffic camera data as IoT input, highlighting its potential for simultaneous emission and origin destination matrix estimation (ODME). A case study in Kista, Stockholm, illustrates the platform’s capabilities through a 3-dimensional (3D) interactive visualization in Unity. This demonstration serves as a first step toward a fully validated emission monitoring system, providing a scalable and modular framework that can be adapted for related applications, such as congestion analysis and noise monitoring.