A Scalable SUMO-Based Framework for Urban Traffic Simulation and Congestion Assessment for Smart City Planning in Noida, India

Accessibility and mobility in a city require a well-planned and developed road network. An optimal routing and traffic flow can also effectively reduce overall energy demand. This study evaluates urban congestion and vehicular speed dynamics in Noida using the Simulation of Urban Mobility (SUMO) platform integrated with GIS. A network of 197 detectors collected congestion and speed data, which were spatially interpolated using inverse distance weighted (IDW) to generate congestion hotspots and speed distribution maps. These are virtual detectors within SUMO and detect the data exhibited by the simulated vehicles. This study investigates the traffic congestion, and speed patterns are investigated using SUMO by integrating road network geometry, building proximity, and vehicle dynamics. The simulation was built using OpenStreetMap (OSM) data, processed with parameters optimizing intersection handling, traffic light logic, and network simplification. Vehicle routes were generated and refined via duarouter, ensuring dynamic path adjustments akin to real-world navigation system. The origin and destinations (OD) are selected by considering the major intersections in the city experiencing high traffic. Traffic volume is generated by the model based on the input parameters and simulation is done using different simulation periods. Results indicate that central Noida, particularly around urban villages, experiences high congestion (>30%) due to narrow, unplanned road networks, despite lower traffic volumes. High congestion is defined as a state where traffic buildup causes reduced vehicular speeds for more than 30% of the observed time within a given region. In contrast, planned southern regions exhibit lower congestion and higher speeds, attributed to structured road layouts. Spatial autocorrelation analysis (Moran’s I = 0.31 for congestion, 0.43 for speed) confirmed clustering, with congestion correlated up to 1500m and speed up to 2000m. Variograms further revealed that speed variability is higher than congestion, reflecting the impact of road design on traffic flow. The adopted framework can effectively handle road networks of different sizes and complexities. It can also be applicable for transportation network in a small town and a large metropolitan region. It can aid policymakers in targeted infrastructure improvements, such as optimizing intersections, expanding road capacity, and integrating public transit solutions.