This study presents a simulation‐driven digital twin (DT) prototype framework, designed around a wireless sensor network (WSN) style pipeline, to study congestion and vehicular emissions at an urban signalized junction. The architecture follows a sensing‐to‐edge workflow: detector‐like traffic measurements (counts and speeds) are published through an MQTT middleware, synchronized into time‐aligned snapshots, and used to update a SUMO‐based microscopic model that acts as the virtual twin. In the present study, sensing is emulated within the simulator, and no roadside deployment is carried out. Three signal control strategies are evaluated: an uncontrolled (random release) baseline, a fixed‐time plan, and a priority‐based plan with simple queue‐responsive green extensions. 1 h simulation experiments are conducted for the junction demand setting, and performance is assessed using waiting time, queue length, and HBEFA‐based emission estimates for five pollutants. Results show that lightweight state‐aware timing rules can substantially reduce delays and stabilize queues relative to the uncontrolled baseline in this configuration, while higher electric vehicle (EV) penetration yields marked reductions in NOx and PMx in the emission model. At the edge layer, K‐means clustering is applied to speed and count features to label links into low, medium, and high congestion regimes and to highlight congestion hotspots for operator interpretation. Since the demand is synthetic and sensing is emulated, the findings are indicative and not calibrated for any specific city. The prototype provides a practical base for future extensions using optimization‐ or learning‐based control and field calibration.
Dkhar et al. (2026) studied this question.