Urban transportation systems in rapidly growing cities face escalating challenges in balancing operational efficiency with environmental sustainability. This study aims to develop a conceptual optimization model for sustainable urban transport using spatial data integration and analytical simulation, with a specific case study in Makassar, Indonesia. The research adopts an exploratory-quantitative method using secondary data, including road networks, traffic volume, and carbon emissions. The data were processed using QGIS and Python (NetworkX and GeoPandas) to build a transport network graph. A Genetic Algorithm (GA) was employed to simulate route optimization by minimizing a composite objective function of travel time and CO₂ emissions.
Simulation results demonstrate that the optimized routes yield a 19% reduction in average travel time, a 17.8% reduction in fuel consumption, and a 20.4% decrease in carbon emissions compared to the existing routes. Spatial analysis using heatmaps and K-Means clustering further revealed high-risk traffic zones, particularly near business centers and university areas. Sensitivity analysis of the objective function weights indicated significant trade-offs between time efficiency and emission reduction, offering flexibility for policy-makers to prioritize based on local goals.
The model contributes both practically and academically by presenting a replicable framework for Indonesian cities aiming to align with SDG 11 (Sustainable Cities and Communities) and SDG 13 (Climate Action). Although this study remains at the conceptual and simulation phase (Technology Readiness Level 1–3), it lays the groundwork for future implementation through real-time data integration and pilot testing. The findings provide valuable insights for urban planners, government agencies, and smart city initiatives in emerging economies.