Urban mobility planning in mid-sized cities often faces the challenge of insufficient or outdated data, limiting the development of reliable performance indicators for decision-making. Within the context of Transport System Models (TSM), this work presents a methodological proposal based on traffic microsimulation using open-source tools—specifically, the Simulation of Urban MObility (SUMO) platform—to support the estimation of key performance indicators (KPIs) related to sustainable urban mobility. The approach positions microsimulation as an operational-level component that complements traditional transport system analyses and ICT-based data sources in data-scarce environments.
As an initial case study, the downtown area of Bahía Blanca (Argentina) was selected due to its high traffic density and circulation complexity. A simplified microsimulation model is being configured using publicly available information on the street network, signal timing, and approximate traffic volumes, which are typical inputs obtained from urban ICT infrastructures. SUMO was selected for its open-access nature, transparency, and adaptability to urban contexts with limited data availability. In future stages, real-world traffic flow and vehicle-type data generated by our research group will be incorporated to improve traffic pattern accuracy and model calibration. Statistical validation metrics commonly used in transportation engineering, such as the GEH statistic, will be applied to support KPI estimation.
Preliminary experiments indicate that the proposed approach can reproduce general traffic dynamics and generate representative KPIs, including average speed, travel time, queue length, level of service, and estimated emissions. Although the results are exploratory due to current data limitations, they demonstrate the feasibility of integrating microsimulation within a broader TSM- and ICT-oriented decision-support framework to identify data needs and assess traffic management improvements.
The proposed framework contributes to the Sustainable Development Goals by supporting evidence-based mobility planning (SDG 11), promoting energy-efficient circulation patterns (SDG 7), and enabling the assessment of emission-reduction scenarios (SDG 13).
