Green Transportation Planning for Smart Cities: Digital Twins and Real-Time Traffic Optimization in Urban Mobility Networks

Abstract

Featured Application: The proposed Digital Twin framework with Adaptive Inflow Metering (AIM) logic is designed for direct application by municipal Traffic Management Centers (TMC) and urban planners. It serves as a decision-support tool for diagnosing congestion hotspots in real-time and testing “soft” optimization strategies (e.g., smart traffic signals) before committing to capital-intensive infrastructure investments. The system is particularly applicable to mid-sized cities aiming to reduce emission levels at critical network nodes without extensive geometric reconstruction. This paper proposes a comprehensive framework for integrating Digital Twins (DT) with real-time traffic optimization systems to enhance urban mobility management in Smart Cities. Using the Pobitno Roundabout in Rzeszów as a case study, we established a calibrated microsimulation model (validated via the GEH statistic) that serves as the core of the proposed Digital Twin. The study goes beyond static scenario analysis by introducing an Adaptive Inflow Metering (AIM) logic designed to interact with IoT sensor data. While traditional geometrical upgrades (e.g., turbo-roundabouts) were analyzed, simulation results revealed that geometrical changes alone—without dynamic control—may fail under peak load conditions (resulting in LOS F). Consequently, the research demonstrates how the DT framework allows for the testing of “Software-in-the-Loop” (SiL) solutions where Python-based algorithms dynamically adjust inflow parameters to prevent gridlock. The findings confirm that combining physical infrastructure changes with digital, real-time optimization algorithms is essential for achieving sustainable “green transport” goals and reducing emissions in congested urban nodes.