Overcoming Data Scarcity in Calibrating SUMO Scenarios with Evolutionary Algorithms
Eclipse Foundation via YouTube
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This 21-minute conference talk from the Eclipse Foundation explores innovative approaches to calibrating traffic simulations with limited observational data. Learn how evolutionary algorithms, specifically the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), can optimize route probabilities in SUMO (Simulation of Urban Mobility) scenarios. The presentation introduces the Mannheim SUMO Traffic Model (MaST) as a case study, demonstrating how this methodology significantly improves calibration accuracy compared to baseline approaches for both 3-hour and 24-hour scenarios. Discover practical solutions for urban planning and mobility management professionals facing data scarcity challenges when developing transportation system simulations. The talk, presented by Jakob Kappenberger and co-authored with Heiner Stuckenschmidt, provides valuable insights into enhancing the effectiveness of traffic simulations even when working with limited data resources.
Syllabus
Overcoming Data Scarcity in Calibrating SUMO Scenarios with Evolutionary Algorithms
Taught by
Eclipse Foundation