OPTIMIZING PUBLIC TRANSPORT CORRIDORS USING AI-BASED SCENARIO MODELLING: A CASE STUDY ON TIRANA’S RING ROAD

European cities experiencing rapid growth face mounting pressures to strengthen the performance, reliability, and environmental integrity of their public transport systems. Tirana is no different; escalating travel demand, a limited street network, and fragmented modal integration have revealed the shortcomings of traditional planning techniques, particularly during the initial decision-making phases when service concepts and corridor alignments are determined. To address these challenges, digital modelling platforms and artificial intelligence offer new opportunities for systematically and evidence-based examination of complex mobility issues. In Tirana’s east-west urban axis, this study investigates how a combined workflow using Autodesk InfraWorks, Giraffe.Build and an AI-based optimization layer can support the identification and improvement of public transport corridor alternatives. The research followed a comprehensive three-stage methodology. First, Giraffe.Build generated a set of preliminary corridor scenarios through quick urban network modeling and massing analysis. Next, these scenarios were imported into InfraWorks, where each alignment was checked for geometric feasibility, multimodal compatibility, intersection performance, and sensitivity to the urban context. A machine-learning model was then used to evaluate the scenarios based on various weighted criteria, including accessibility improvements, travel time efficiency, right-of-way impacts, and integration with pedestrian and cycling infrastructure. The AI component refined and ranked the alternatives to find the most balanced and contextually suitable configurations. The integrated workflow clearly highlighted corridor concepts, revealing how variations in alignment and stop distribution affect performance outcomes. The AI model consistently preferred scenarios that improved interchange opportunities and reduced geometric constraints, while InfraWorks simulations verified improvements in operational continuity and intersection behavior. The findings indicate that a blended digital workflow can greatly improve early-stage mobility planning by integrating conceptual urban modeling with detailed engineering validation and systematic optimization. Besides its technical merits, this approach provides a replicable framework for cities aiming to achieve more transparent and data-driven decision-making processes in their transition toward sustainable mobility systems.