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 multi-source data from IoT sensors and simulation to provide a high spatio-temporal 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 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.
Jostmann et al. (Wed,) studied this question.