Empirical Insights into Centroid Optimization in Transportation Planning: A Novel Approach

Abstract

In complex urban environments, sophisticated analytical techniques are essential to improve the accuracy of transportation modeling and the precision of travel time predictions. This research endeavors to critically examine various multi-zone centroid positioning strategies, with the goal of unveiling the method that most authentically represents the dynamics of urban travel. Anchored by a comprehensive case study of the city of Istanbul in Turkey, the investigation utilizes various analytical tools meticulously tailored to account for the intricate interplay of demographic and geographic variables. This methodical approach enables the identification of optimal centroid locations by evaluating various methods, including a novel approach called kernel density estimation (KDE), as well as five other established other methods. The results are then aligned with real-world travel time data through rigorous statistical analysis to ensure accurate predictions of travel times. The KDE method demonstrated its more acceptable correlation coefficient and lower mean absolute error, root mean square error, and mean absolute percentage error metrics, establishing its superiority in model performance. By highlighting the paramount importance of accurately placed centroids within urban transportation models, this research’s findings contribute significantly to the evolution of transportation planning methodologies and the advancement of urban mobility models through a comparative analysis of optimal methods.